diff --git a/README.md b/README.md
index 058531e..c446b57 100644
--- a/README.md
+++ b/README.md
@@ -5,31 +5,53 @@ This repository contains the code for the University of Edinburgh [School of Inf
This assignment-based course is focused on the implementation and evaluation of machine learning systems. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.
The code in this repository is split into:
-
- * a Python package `mlp`, a [NumPy](http://www.numpy.org/) based neural network package designed specifically for the course that students will implement parts of and extend during the course labs and assignments,
- * a series of [Jupyter](http://jupyter.org/) notebooks in the `notebooks` directory containing explanatory material and coding exercises to be completed during the course labs.
-
+1. notebooks:
+ 1. Introduction_to_tensorflow: Introduces students to the basics of tensorflow and lower level operations.
+ 2. Introduction_to_tf_mlp_repo: Introduces students to the high level functionality of this repo and how one
+ could run an experiment. The code is full of comments and documentation so you should spend more time
+ reading and understanding the code by running simple experiments and changing pieces of code to see the impact
+ on the system.
+2. utils:
+ 1. network_summary: Provides utilities with which one can get network summaries, such as the number of parameters and names of layers.
+ 2. parser_utils which are used to parse arguments passed to the training scripts.
+ 3. storage, which is responsible for storing network statistics.
+3. data_providers.py : Provides the data providers for training, validation and testing.
+4. network_architectures.py: Defines the network architectures. We provide VGGNet as an example.
+5. network_builder.py: Builds the tensorflow computation graph. In more detail, it builds the losses, tensorflow summaries and training operations.
+6. network_trainer.py: Runs an experiment, composed of training, validation and testing. It is setup to use arguments such that one can easily write multiple bash scripts with different hyperparameters and run experiments very quickly with minimal code changes.
+
+
## Getting set up
Detailed instructions for setting up a development environment for the course are given in [this file](notes/environment-set-up.md). Students doing the course will spend part of the first lab getting their own environment set up.
-
-## Frequent Issues/Solutions
-
-Don’t forget that from your /mlpractica/l folder you should first do
+Once you have setup the basic environment then to install the requirements for the tf_mlp repo simply run:
```
-git status #to check whether there are any changes in your local branch. If there are, you need to do:
-git add “path /to/file”
-git commit -m “some message”
+pip install -r requirements.txt
+```
+For CPU tensorflow and
+```
+pip install -r requirements_gpu.txt
+```
+for GPU tensorflow.
+
+If you install the wrong version of tensorflow simply run
+
+```
+pip uninstall $tensorflow_to_uninstall
+```
+replacing $tensorflow_to_uninstall with the tensorflow you want to install and then install the correct one
+using pip install as normally done.
+
+## Additional Packages
+
+For the tf_mlp you are required to install either the tensorflow-1.4.1 package for CPU users or the tensorflow_gpu-1.4.1 for GPU users. Both of these can easily be installed via pip using:
+
+```
+pip install tensorflow
```
-Only if this is OK, you can run
-```
-git checkout mlp2017-8/lab[n]
-```
-Related to MLP module not found error:
-Another thing is to make sure you have you MLP_DATA_DIR path correctly set. You can check this by typing
-```echo $MLP_DATA_DIR```
-in the command line. If this is not set up, you need to follow the instructions on the set-up-environment to get going.
+or
-Finally, please make sure you have run
-```python setup.py develop```
+```
+pip install tensorflow_gpu
+```
diff --git a/cifar100_network_trainer.py b/cifar100_network_trainer.py
new file mode 100644
index 0000000..ab6a227
--- /dev/null
+++ b/cifar100_network_trainer.py
@@ -0,0 +1,181 @@
+import argparse
+import numpy as np
+import tensorflow as tf
+import tqdm
+from data_providers import CIFAR100DataProvider
+from network_builder import ClassifierNetworkGraph
+from utils.parser_utils import ParserClass
+from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
+
+tf.reset_default_graph() # resets any previous graphs to clear memory
+parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
+parser_extractor = ParserClass(parser=parser) # creates a parser class to process the parsed input
+
+batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, batch_norm, \
+strided_dim_reduction, experiment_prefix, dropout_rate_value = parser_extractor.get_argument_variables()
+# returns a list of objects that contain
+# our parsed input
+
+experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_prefix,
+ batch_size, batch_norm,
+ strided_dim_reduction)
+# generate experiment name
+
+rng = np.random.RandomState(seed=seed) # set seed
+
+train_data = CIFAR100DataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
+val_data = CIFAR100DataProvider(which_set="valid", batch_size=batch_size, rng=rng)
+test_data = CIFAR100DataProvider(which_set="test", batch_size=batch_size, rng=rng)
+# setup our data providers
+
+print("Running {}".format(experiment_name))
+print("Starting from epoch {}".format(continue_from_epoch))
+
+saved_models_filepath, logs_filepath = build_experiment_folder(experiment_name, logs_path) # generate experiment dir
+
+# Placeholder setup
+data_inputs = tf.placeholder(tf.float32, [batch_size, train_data.inputs.shape[1], train_data.inputs.shape[2],
+ train_data.inputs.shape[3]], 'data-inputs')
+data_targets = tf.placeholder(tf.int32, [batch_size], 'data-targets')
+
+training_phase = tf.placeholder(tf.bool, name='training-flag')
+rotate_data = tf.placeholder(tf.bool, name='rotate-flag')
+dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
+
+classifier_network = ClassifierNetworkGraph(input_x=data_inputs, target_placeholder=data_targets,
+ dropout_rate=dropout_rate, batch_size=batch_size,
+ n_classes=train_data.num_classes,
+ is_training=training_phase, augment_rotate_flag=rotate_data,
+ strided_dim_reduction=strided_dim_reduction,
+ use_batch_normalization=batch_norm) # initialize our computational graph
+
+if continue_from_epoch == -1: # if this is a new experiment and not continuation of a previous one then generate a new
+ # statistics file
+ save_statistics(logs_filepath, "result_summary_statistics", ["epoch", "train_c_loss", "train_c_accuracy",
+ "val_c_loss", "val_c_accuracy",
+ "test_c_loss", "test_c_accuracy"], create=True)
+
+start_epoch = continue_from_epoch if continue_from_epoch != -1 else 0 # if new experiment start from 0 otherwise
+# continue where left off
+
+summary_op, losses_ops, c_error_opt_op = classifier_network.init_train() # get graph operations (ops)
+
+total_train_batches = train_data.num_batches
+total_val_batches = val_data.num_batches
+total_test_batches = test_data.num_batches
+
+if tensorboard_enable:
+ print("saved tensorboard file at", logs_filepath)
+ writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
+
+init = tf.global_variables_initializer() # initialization op for the graph
+
+with tf.Session() as sess:
+ sess.run(init) # actually running the initialization op
+ train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
+ val_saver = tf.train.Saver()
+ best_val_accuracy = 0.
+ best_epoch = 0
+ # training or inference
+
+ if continue_from_epoch != -1:
+ train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
+ continue_from_epoch)) # restore previous graph to continue operations
+ best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
+ print(best_val_accuracy, best_epoch)
+
+ with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
+ for e in range(start_epoch, epochs):
+ total_c_loss = 0.
+ total_accuracy = 0.
+ with tqdm.tqdm(total=total_train_batches) as pbar_train:
+ for batch_idx, (x_batch, y_batch) in enumerate(train_data):
+ iter_id = e * total_train_batches + batch_idx
+ _, c_loss_value, acc = sess.run(
+ [c_error_opt_op, losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ # Here we execute the c_error_opt_op which trains the network and also the ops that compute the
+ # loss and accuracy, we save those in _, c_loss_value and acc respectively.
+ total_c_loss += c_loss_value # add loss of current iter to sum
+ total_accuracy += acc # add acc of current iter to sum
+
+ iter_out = "iter_num: {}, train_loss: {}, train_accuracy: {}".format(iter_id,
+ total_c_loss / (batch_idx + 1),
+ total_accuracy / (
+ batch_idx + 1)) # show
+ # iter statistics using running averages of previous iter within this epoch
+ pbar_train.set_description(iter_out)
+ pbar_train.update(1)
+ if tensorboard_enable and batch_idx % 25 == 0: # save tensorboard summary every 25 iterations
+ _summary = sess.run(
+ summary_op,
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ writer.add_summary(_summary, global_step=iter_id)
+
+ total_c_loss /= total_train_batches # compute mean of los
+ total_accuracy /= total_train_batches # compute mean of accuracy
+
+ save_path = train_saver.save(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ # save graph and weights
+ print("Saved current model at", save_path)
+
+ total_val_c_loss = 0.
+ total_val_accuracy = 0. # run validation stage, note how training_phase placeholder is set to False
+ # and that we do not run the c_error_opt_op which runs gradient descent, but instead only call the loss ops
+ # to collect losses on the validation set
+ with tqdm.tqdm(total=total_val_batches) as pbar_val:
+ for batch_idx, (x_batch, y_batch) in enumerate(val_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_val_c_loss += c_loss_value
+ total_val_accuracy += acc
+ iter_out = "val_loss: {}, val_accuracy: {}".format(total_val_c_loss / (batch_idx + 1),
+ total_val_accuracy / (batch_idx + 1))
+ pbar_val.set_description(iter_out)
+ pbar_val.update(1)
+
+ total_val_c_loss /= total_val_batches
+ total_val_accuracy /= total_val_batches
+
+ if best_val_accuracy < total_val_accuracy: # check if val acc better than the previous best and if
+ # so save current as best and save the model as the best validation model to be used on the test set
+ # after the final epoch
+ best_val_accuracy = total_val_accuracy
+ best_epoch = e
+ save_path = val_saver.save(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ print("Saved best validation score model at", save_path)
+
+ epoch_pbar.update(1)
+ # save statistics of this epoch, train and val without test set performance
+ save_statistics(logs_filepath, "result_summary_statistics",
+ [e, total_c_loss, total_accuracy, total_val_c_loss, total_val_accuracy,
+ -1, -1])
+
+ val_saver.restore(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, best_epoch))
+ # restore model with best performance on validation set
+ total_test_c_loss = 0.
+ total_test_accuracy = 0.
+ # computer test loss and accuracy and save
+ with tqdm.tqdm(total=total_test_batches) as pbar_test:
+ for batch_idx, (x_batch, y_batch) in enumerate(test_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_test_c_loss += c_loss_value
+ total_test_accuracy += acc
+ iter_out = "test_loss: {}, test_accuracy: {}".format(total_test_c_loss / (batch_idx + 1),
+ total_test_accuracy / (batch_idx + 1))
+ pbar_test.set_description(iter_out)
+ pbar_test.update(1)
+
+ total_test_c_loss /= total_test_batches
+ total_test_accuracy /= total_test_batches
+
+ save_statistics(logs_filepath, "result_summary_statistics",
+ ["test set performance", -1, -1, -1, -1,
+ total_test_c_loss, total_test_accuracy])
diff --git a/cifar10_network_trainer.py b/cifar10_network_trainer.py
new file mode 100644
index 0000000..b6470b1
--- /dev/null
+++ b/cifar10_network_trainer.py
@@ -0,0 +1,183 @@
+import argparse
+import numpy as np
+import tensorflow as tf
+import tqdm
+from data_providers import CIFAR10DataProvider
+from network_builder import ClassifierNetworkGraph
+from utils.parser_utils import ParserClass
+from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
+
+tf.reset_default_graph() # resets any previous graphs to clear memory
+parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
+parser_extractor = ParserClass(parser=parser) # creates a parser class to process the parsed input
+
+batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, batch_norm, \
+strided_dim_reduction, experiment_prefix, dropout_rate_value = parser_extractor.get_argument_variables()
+# returns a list of objects that contain
+# our parsed input
+
+experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_prefix,
+ batch_size, batch_norm,
+ strided_dim_reduction)
+# generate experiment name
+
+rng = np.random.RandomState(seed=seed) # set seed
+
+train_data = CIFAR10DataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
+val_data = CIFAR10DataProvider(which_set="valid", batch_size=batch_size, rng=rng)
+test_data = CIFAR10DataProvider(which_set="test", batch_size=batch_size, rng=rng)
+# setup our data providers
+
+print("Running {}".format(experiment_name))
+print("Starting from epoch {}".format(continue_from_epoch))
+
+saved_models_filepath, logs_filepath = build_experiment_folder(experiment_name, logs_path) # generate experiment dir
+
+# Placeholder setup
+data_inputs = tf.placeholder(tf.float32, [batch_size, train_data.inputs.shape[1], train_data.inputs.shape[2],
+ train_data.inputs.shape[3]], 'data-inputs')
+data_targets = tf.placeholder(tf.int32, [batch_size], 'data-targets')
+
+training_phase = tf.placeholder(tf.bool, name='training-flag')
+rotate_data = tf.placeholder(tf.bool, name='rotate-flag')
+dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
+
+classifier_network = ClassifierNetworkGraph(input_x=data_inputs, target_placeholder=data_targets,
+ dropout_rate=dropout_rate, batch_size=batch_size,
+ n_classes=train_data.num_classes, is_training=training_phase,
+ augment_rotate_flag=rotate_data,
+ strided_dim_reduction=strided_dim_reduction,
+ use_batch_normalization=batch_norm) # initialize our computational graph
+
+if continue_from_epoch == -1: # if this is a new experiment and not continuation of a previous one then generate a new
+ # statistics file
+ save_statistics(logs_filepath, "result_summary_statistics", ["epoch", "train_c_loss", "train_c_accuracy",
+ "val_c_loss", "val_c_accuracy",
+ "test_c_loss", "test_c_accuracy"], create=True)
+
+start_epoch = continue_from_epoch if continue_from_epoch != -1 else 0 # if new experiment start from 0 otherwise
+# continue where left off
+
+summary_op, losses_ops, c_error_opt_op = classifier_network.init_train() # get graph operations (ops)
+
+total_train_batches = train_data.num_batches
+total_val_batches = val_data.num_batches
+total_test_batches = test_data.num_batches
+
+
+
+if tensorboard_enable:
+ print("saved tensorboard file at", logs_filepath)
+ writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
+
+init = tf.global_variables_initializer() # initialization op for the graph
+
+with tf.Session() as sess:
+ sess.run(init) # actually running the initialization op
+ train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
+ val_saver = tf.train.Saver()
+ best_val_accuracy = 0.
+ best_epoch = 0
+ # training or inference
+
+ if continue_from_epoch != -1:
+ train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
+ continue_from_epoch)) # restore previous graph to continue operations
+ best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
+ print(best_val_accuracy, best_epoch)
+
+ with tqdm.tqdm(total=epochs-start_epoch) as epoch_pbar:
+ for e in range(start_epoch, epochs):
+ total_c_loss = 0.
+ total_accuracy = 0.
+ with tqdm.tqdm(total=total_train_batches) as pbar_train:
+ for batch_idx, (x_batch, y_batch) in enumerate(train_data):
+ iter_id = e * total_train_batches + batch_idx
+ _, c_loss_value, acc = sess.run(
+ [c_error_opt_op, losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ # Here we execute the c_error_opt_op which trains the network and also the ops that compute the
+ # loss and accuracy, we save those in _, c_loss_value and acc respectively.
+ total_c_loss += c_loss_value # add loss of current iter to sum
+ total_accuracy += acc # add acc of current iter to sum
+
+ iter_out = "iter_num: {}, train_loss: {}, train_accuracy: {}".format(iter_id,
+ total_c_loss / (batch_idx + 1),
+ total_accuracy / (
+ batch_idx + 1)) # show
+ # iter statistics using running averages of previous iter within this epoch
+ pbar_train.set_description(iter_out)
+ pbar_train.update(1)
+ if tensorboard_enable and batch_idx % 25 == 0: # save tensorboard summary every 25 iterations
+ _summary = sess.run(
+ summary_op,
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ writer.add_summary(_summary, global_step=iter_id)
+
+ total_c_loss /= total_train_batches # compute mean of los
+ total_accuracy /= total_train_batches # compute mean of accuracy
+
+ save_path = train_saver.save(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ # save graph and weights
+ print("Saved current model at", save_path)
+
+ total_val_c_loss = 0.
+ total_val_accuracy = 0. # run validation stage, note how training_phase placeholder is set to False
+ # and that we do not run the c_error_opt_op which runs gradient descent, but instead only call the loss ops
+ # to collect losses on the validation set
+ with tqdm.tqdm(total=total_val_batches) as pbar_val:
+ for batch_idx, (x_batch, y_batch) in enumerate(val_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_val_c_loss += c_loss_value
+ total_val_accuracy += acc
+ iter_out = "val_loss: {}, val_accuracy: {}".format(total_val_c_loss / (batch_idx + 1),
+ total_val_accuracy / (batch_idx + 1))
+ pbar_val.set_description(iter_out)
+ pbar_val.update(1)
+
+ total_val_c_loss /= total_val_batches
+ total_val_accuracy /= total_val_batches
+
+ if best_val_accuracy < total_val_accuracy: # check if val acc better than the previous best and if
+ # so save current as best and save the model as the best validation model to be used on the test set
+ # after the final epoch
+ best_val_accuracy = total_val_accuracy
+ best_epoch = e
+ save_path = val_saver.save(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ print("Saved best validation score model at", save_path)
+
+ epoch_pbar.update(1)
+ # save statistics of this epoch, train and val without test set performance
+ save_statistics(logs_filepath, "result_summary_statistics",
+ [e, total_c_loss, total_accuracy, total_val_c_loss, total_val_accuracy,
+ -1, -1])
+
+ val_saver.restore(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, best_epoch))
+ # restore model with best performance on validation set
+ total_test_c_loss = 0.
+ total_test_accuracy = 0.
+ # computer test loss and accuracy and save
+ with tqdm.tqdm(total=total_test_batches) as pbar_test:
+ for batch_idx, (x_batch, y_batch) in enumerate(test_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_test_c_loss += c_loss_value
+ total_test_accuracy += acc
+ iter_out = "test_loss: {}, test_accuracy: {}".format(total_test_c_loss / (batch_idx + 1),
+ total_test_accuracy / (batch_idx + 1))
+ pbar_test.set_description(iter_out)
+ pbar_test.update(1)
+
+ total_test_c_loss /= total_test_batches
+ total_test_accuracy /= total_test_batches
+
+ save_statistics(logs_filepath, "result_summary_statistics",
+ ["test set performance", -1, -1, -1, -1,
+ total_test_c_loss, total_test_accuracy])
diff --git a/data/HadSSP_daily_qc.txt b/data/HadSSP_daily_qc.txt
deleted file mode 100644
index d7badf5..0000000
--- a/data/HadSSP_daily_qc.txt
+++ /dev/null
@@ -1,1023 +0,0 @@
-Daily Southern Scotland precipitation (mm). Values may change after QC.
-Alexander & Jones (2001, Atmospheric Science Letters).
-Format=Year, Month, 1-31 daily precipitation values.
- 1931 1 1.40 2.10 2.50 0.10 0.00 0.00 0.90 6.20 1.90 4.90 7.30 0.80 0.30 2.90 7.50 18.79 1.30 10.29 2.90 0.60 6.70 15.39 11.29 5.00 3.60 1.00 4.20 7.89 1.10 6.50 17.19
- 1931 2 0.90 0.60 0.40 1.10 6.69 3.00 7.59 7.79 7.99 9.59 24.17 1.90 0.20 4.69 10.58 0.80 0.80 0.90 7.59 12.88 4.19 5.89 1.20 8.59 5.69 0.90 1.80 2.20 -99.99 -99.99 -99.99
- 1931 3 0.00 1.30 0.00 0.00 0.00 0.50 0.40 0.60 1.00 0.00 0.10 7.30 6.20 0.20 0.90 0.00 0.00 0.20 5.80 4.60 1.40 0.40 0.40 0.00 0.00 0.00 0.00 0.30 1.80 0.20 0.00
- 1931 4 3.99 3.49 0.00 2.70 0.00 0.00 1.80 1.80 0.00 0.20 3.39 2.40 1.40 1.60 3.59 7.99 2.20 0.20 0.00 0.20 0.30 3.49 5.09 6.79 4.79 3.20 1.90 0.70 0.00 2.10 -99.99
- 1931 5 1.70 0.00 0.70 0.00 5.62 0.70 13.14 0.80 11.13 11.23 0.60 1.70 10.83 8.12 2.21 0.60 0.20 0.70 0.00 0.00 0.00 1.91 2.31 4.31 3.91 0.20 0.00 12.03 1.60 9.23 3.11
- 1931 6 1.40 16.40 3.70 0.10 5.80 12.90 4.30 4.50 10.40 13.20 0.30 0.10 9.30 29.60 23.40 2.30 9.80 8.90 0.40 2.90 6.70 2.40 2.80 0.00 0.40 1.90 2.30 0.30 0.00 0.90 -99.99
- 1931 7 9.49 1.70 8.69 4.10 2.50 13.29 2.70 5.60 3.10 1.30 7.59 3.90 2.30 7.69 1.60 3.60 7.09 1.50 1.10 0.30 2.20 10.69 1.30 3.50 3.70 0.80 13.19 1.60 9.29 1.20 1.80
- 1931 8 0.20 0.00 0.00 0.00 0.00 0.60 2.00 0.60 6.60 0.60 0.90 1.20 0.50 4.80 2.80 6.60 4.10 0.00 17.20 3.50 1.10 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1931 9 9.86 4.33 1.01 0.10 0.30 1.01 0.80 1.31 0.00 0.30 4.23 0.00 1.01 1.01 0.91 14.69 0.40 0.40 0.10 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 2.62 4.33 -99.99
- 1931 10 23.18 5.30 4.20 6.89 4.10 11.29 10.09 5.80 11.99 1.80 2.00 5.10 0.30 0.00 0.00 0.10 0.10 0.00 0.50 0.00 0.00 0.00 3.20 0.00 0.40 2.40 19.59 1.00 11.09 0.20 4.30
- 1931 11 6.60 20.40 24.80 3.30 3.30 2.60 5.20 4.20 8.00 13.60 3.50 0.90 8.50 15.30 0.10 0.10 13.50 10.20 5.10 6.40 0.10 6.70 28.20 7.30 10.20 7.40 5.70 6.40 1.20 0.60 -99.99
- 1931 12 3.20 21.60 16.00 5.80 8.40 0.70 6.90 4.80 2.80 1.10 1.10 0.90 2.50 3.20 0.00 0.60 0.10 3.50 1.50 0.90 0.50 10.60 16.40 4.60 2.20 1.70 5.70 3.00 0.10 0.00 17.40
- 1932 1 12.71 41.12 22.51 7.20 12.41 5.70 1.70 1.80 24.41 3.80 0.80 13.71 4.30 17.21 20.71 8.50 1.50 1.00 11.20 5.20 6.50 0.40 0.40 4.00 0.10 0.00 0.00 1.00 0.30 0.10 1.50
- 1932 2 0.00 0.22 0.00 0.54 0.33 0.11 0.00 0.00 0.22 0.11 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.22 0.11 0.11 0.11 0.00 0.11 0.00 0.00 -99.99 -99.99
- 1932 3 0.10 0.00 0.00 1.60 8.30 4.10 10.00 1.10 0.00 0.00 0.00 0.60 0.50 0.00 0.00 0.00 0.00 0.00 1.90 9.60 12.50 3.40 0.70 2.70 2.40 0.70 5.50 0.50 7.20 4.70 0.90
- 1932 4 7.41 4.61 1.10 0.10 9.41 8.61 2.10 13.62 17.63 4.71 0.70 0.30 10.02 3.61 1.10 0.00 0.00 1.00 6.21 1.90 1.10 11.02 1.70 0.20 0.00 0.00 4.71 10.12 2.90 1.10 -99.99
- 1932 5 0.10 0.20 0.00 0.10 0.70 0.10 0.80 1.00 0.30 0.00 10.51 17.42 4.11 1.00 13.62 0.30 0.10 8.21 4.41 3.70 1.90 0.00 0.90 0.20 3.60 0.70 1.00 1.80 1.00 0.60 0.00
- 1932 6 0.00 0.00 0.00 0.20 0.00 0.00 0.60 0.20 0.50 0.00 0.00 0.10 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.20 1.81 4.02 13.25 1.61 6.63 19.38 -99.99
- 1932 7 2.41 7.62 13.94 7.42 1.30 1.30 1.80 3.81 2.61 4.01 1.00 4.81 9.93 0.00 1.20 0.50 0.40 0.10 2.11 0.80 0.40 1.60 5.01 6.32 3.51 3.01 14.34 0.90 9.52 2.71 1.00
- 1932 8 0.00 1.70 0.30 1.00 2.70 4.61 3.40 2.60 0.50 1.30 9.61 1.80 3.81 0.40 0.70 2.90 0.70 0.00 0.00 2.70 0.90 0.00 0.00 0.00 0.00 3.10 0.40 2.60 3.91 3.91 14.52
- 1932 9 19.37 7.39 9.69 2.70 3.50 3.79 16.68 5.29 4.69 16.88 3.50 1.00 14.08 2.00 0.40 0.10 0.80 0.80 0.20 0.00 0.00 0.90 1.20 8.99 8.69 1.70 0.10 1.20 0.00 8.59 -99.99
- 1932 10 4.40 0.50 0.10 1.80 6.40 8.20 14.69 18.39 4.30 2.80 0.10 16.19 2.20 0.80 2.40 4.80 20.69 0.60 10.29 6.20 9.30 7.50 4.70 1.30 8.80 9.50 1.10 2.70 19.39 5.20 2.40
- 1932 11 11.37 8.08 5.79 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.10 0.30 0.00 0.10 1.30 0.40 0.10 0.20 2.99 8.48 12.27 18.76 8.58 2.29 13.57 6.68 0.80 1.80 22.85 5.39 -99.99
- 1932 12 20.23 19.93 3.81 2.40 0.00 0.00 0.00 0.10 0.40 0.40 0.10 0.70 2.30 13.22 20.43 44.17 27.24 28.95 22.04 4.91 5.51 8.91 5.61 1.30 0.00 3.10 0.20 3.71 4.91 0.10 5.91
- 1933 1 3.40 28.50 2.80 18.80 5.30 4.50 14.60 8.80 0.60 3.50 0.00 3.10 0.50 19.20 1.10 0.90 0.40 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.30 5.80 36.00
- 1933 2 6.10 2.60 14.80 33.10 8.00 9.00 3.10 4.70 7.00 0.10 0.10 0.90 0.10 0.00 0.20 1.70 0.50 0.00 1.40 1.40 0.20 0.00 0.30 2.30 11.30 10.30 4.90 2.70 -99.99 -99.99 -99.99
- 1933 3 2.59 5.29 3.99 5.99 7.19 7.09 0.30 29.54 5.19 0.00 0.00 0.00 1.10 3.89 5.49 2.49 2.89 3.59 0.10 0.00 1.90 0.00 0.00 0.00 0.00 0.10 0.10 0.00 2.20 3.49 1.80
- 1933 4 0.40 14.98 3.20 0.50 0.00 0.00 0.00 11.98 1.70 0.10 4.69 0.20 0.00 0.40 6.09 1.60 0.80 0.10 0.10 0.20 0.00 0.00 0.10 12.68 0.90 5.09 3.79 0.20 3.70 0.90 -99.99
- 1933 5 0.00 0.00 4.71 9.92 2.21 13.73 3.81 5.71 1.80 0.10 0.80 0.20 0.00 0.40 1.10 3.61 1.10 4.91 1.50 3.91 0.00 10.23 1.30 3.81 0.90 3.51 0.20 0.70 0.00 0.00 0.00
- 1933 6 6.82 7.93 0.00 0.00 0.00 0.00 0.00 1.00 0.10 1.20 0.10 0.10 0.00 0.00 2.11 13.14 14.25 6.12 2.41 0.20 1.61 0.60 1.30 0.90 0.30 0.00 0.00 0.00 0.00 0.40 -99.99
- 1933 7 0.00 0.00 0.00 0.00 0.10 0.00 6.00 1.70 8.40 9.90 8.30 4.00 10.00 0.80 1.90 0.20 1.20 1.10 1.60 1.50 0.00 0.90 0.90 16.60 2.70 0.10 14.10 4.70 3.40 21.30 0.40
- 1933 8 2.09 2.29 0.20 0.00 0.00 0.00 1.89 6.87 0.30 0.20 1.39 0.00 1.59 2.89 7.07 4.18 9.36 3.98 3.98 2.19 3.68 2.79 0.20 3.19 0.60 2.39 17.23 2.19 0.80 0.30 13.94
- 1933 9 0.90 0.70 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.18 2.19 12.36 3.99 1.00 0.10 0.40 1.20 0.20 0.10 0.00 0.10 0.00 0.00 -99.99
- 1933 10 0.00 0.00 0.30 0.20 0.00 0.00 13.80 1.90 13.20 1.00 1.70 2.10 6.80 1.40 18.80 2.50 0.60 0.70 3.60 1.00 1.30 4.00 3.00 0.30 0.20 4.00 1.40 4.30 0.60 3.10 3.50
- 1933 11 5.90 0.10 0.10 0.80 0.60 0.20 0.20 1.50 7.80 0.10 1.50 2.60 8.40 19.10 1.90 0.70 0.70 4.50 12.90 0.80 0.40 0.10 0.50 0.00 0.00 0.20 0.10 0.00 0.00 0.00 -99.99
- 1933 12 3.91 0.10 0.00 0.20 0.00 0.50 0.20 0.00 0.70 0.10 5.31 0.60 0.00 0.00 0.90 0.30 0.20 1.70 0.50 0.20 0.30 0.60 0.00 6.71 6.41 0.30 0.00 0.30 6.71 4.21 7.01
- 1934 1 12.11 2.20 17.41 13.91 2.80 15.91 14.91 3.30 19.91 8.80 9.10 10.31 6.80 3.50 3.70 24.21 7.10 1.10 0.00 2.10 3.10 5.00 1.70 0.00 5.30 6.30 0.00 0.10 0.70 4.10 0.40
- 1934 2 0.20 0.30 0.10 0.00 0.49 1.18 6.31 0.99 1.38 0.59 0.49 0.00 0.00 0.00 0.10 0.00 0.00 0.39 0.59 1.09 1.18 0.30 0.00 5.72 0.39 0.10 0.00 0.20 -99.99 -99.99 -99.99
- 1934 3 11.57 4.99 3.89 5.29 9.78 4.39 3.59 4.09 0.60 2.79 2.99 2.99 0.20 6.39 1.80 7.38 3.59 2.69 0.00 0.10 1.70 0.30 2.79 0.30 3.49 0.70 0.00 0.00 0.20 0.00 0.00
- 1934 4 0.10 0.10 0.00 0.40 0.00 1.40 6.59 0.90 2.20 6.39 12.79 26.47 9.49 3.70 1.10 0.40 4.70 1.60 1.10 8.39 3.10 2.70 7.59 1.30 1.30 1.00 0.30 0.10 0.20 0.10 -99.99
- 1934 5 3.10 0.00 0.00 0.00 6.99 15.08 2.70 4.50 0.20 0.00 4.10 1.60 3.40 1.20 15.48 2.50 2.00 6.49 18.08 6.99 2.20 0.70 0.40 1.60 0.00 0.00 0.50 0.10 0.00 0.00 0.00
- 1934 6 0.00 0.00 0.00 0.00 0.00 0.40 1.00 5.00 0.40 0.00 0.00 0.00 1.10 3.40 0.70 0.90 0.30 10.10 1.20 1.90 21.70 14.90 0.00 0.90 0.10 5.20 3.50 0.60 0.30 0.10 -99.99
- 1934 7 0.10 0.00 0.00 0.00 0.00 0.30 0.00 0.00 0.00 0.00 0.20 9.60 6.50 2.10 4.30 4.00 8.40 3.10 2.20 3.70 8.20 1.60 1.80 1.40 5.20 3.00 3.90 0.90 6.50 2.50 1.80
- 1934 8 10.59 11.79 2.20 4.20 0.20 8.89 0.10 3.60 6.60 3.30 4.00 0.50 0.00 1.20 1.90 0.10 0.00 3.60 3.60 15.69 12.89 2.60 0.70 0.10 0.10 0.70 6.30 17.69 5.80 1.90 2.30
- 1934 9 2.60 8.00 7.30 6.00 0.10 9.30 7.70 4.70 1.70 2.70 0.00 0.00 0.00 0.10 8.20 1.60 3.50 4.80 5.10 1.80 8.50 11.90 2.80 4.50 24.50 10.20 5.20 7.50 1.70 8.50 -99.99
- 1934 10 0.50 0.60 14.09 9.30 4.30 16.09 1.50 10.50 7.30 0.90 3.80 2.20 8.20 6.40 0.30 1.20 0.90 1.10 12.69 5.40 7.90 9.00 5.10 17.49 28.79 20.19 12.99 4.30 18.69 3.80 2.30
- 1934 11 1.60 6.31 13.32 0.40 0.00 0.00 0.60 0.00 3.21 1.70 0.30 0.30 0.30 0.00 0.10 0.30 0.10 1.30 2.91 0.50 3.11 3.11 0.70 0.00 8.62 0.80 0.40 1.70 0.10 2.91 -99.99
- 1934 12 11.69 7.89 12.59 5.39 0.10 1.90 7.59 13.49 13.49 4.10 3.70 5.49 2.90 8.29 0.90 2.20 14.09 5.69 3.60 0.30 0.60 0.20 2.40 0.00 12.99 16.98 12.39 2.60 5.29 13.69 8.69
- 1935 1 10.83 0.40 1.60 0.40 0.00 0.60 0.30 1.80 3.01 3.41 11.03 0.60 5.72 0.10 0.10 0.10 0.00 0.00 0.00 0.00 0.00 0.10 4.51 10.23 3.61 0.10 0.30 1.20 0.60 1.20 12.53
- 1935 2 17.00 4.30 3.10 3.80 7.40 0.20 0.00 0.00 0.30 6.80 9.20 6.70 5.40 2.50 23.60 13.00 4.40 14.10 20.30 6.30 3.20 2.20 1.10 3.20 0.00 3.60 5.60 5.60 -99.99 -99.99 -99.99
- 1935 3 0.10 3.50 4.90 4.80 3.20 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.20 0.10 0.00 0.90 1.60 0.10 7.80 8.60 2.60 7.80 2.00 1.50 0.20 0.70 6.40 1.60 0.80
- 1935 4 0.10 0.00 1.00 0.10 0.00 0.00 6.40 7.70 17.10 18.40 7.10 0.00 1.70 2.90 6.40 15.60 5.20 0.80 5.50 6.20 1.30 1.70 1.50 0.10 0.00 0.00 0.00 0.00 0.00 0.60 -99.99
- 1935 5 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.00 3.82 0.90 4.02 7.43 0.20 3.21 1.81 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1935 6 0.00 4.01 7.21 4.41 1.10 7.41 7.91 3.81 0.90 10.52 9.02 4.81 3.71 2.00 0.60 2.00 1.70 0.10 6.61 2.70 2.70 0.10 18.13 0.80 0.00 5.51 1.90 0.00 0.80 1.00 -99.99
- 1935 7 1.10 1.20 6.11 8.31 0.40 0.00 0.00 0.00 1.60 1.90 0.00 0.00 3.01 0.60 0.20 1.90 2.50 3.91 9.52 0.20 0.60 0.00 1.70 0.00 0.20 9.12 4.81 0.40 0.00 0.00 0.00
- 1935 8 0.00 0.00 0.10 1.40 0.00 0.00 1.20 0.40 0.00 8.68 3.99 0.00 0.50 0.50 4.99 5.09 4.39 1.20 0.40 0.00 2.29 0.00 0.40 0.60 9.68 8.78 1.00 8.08 5.89 8.98 0.30
- 1935 9 16.41 5.80 2.20 0.60 0.10 0.00 0.00 0.00 0.00 1.80 0.30 4.80 9.20 9.30 16.21 14.21 11.71 27.61 10.51 1.30 1.20 2.00 0.10 0.10 0.00 16.11 7.50 7.70 13.61 10.51 -99.99
- 1935 10 1.60 28.77 5.09 1.70 0.90 0.90 22.08 6.99 9.79 19.28 3.60 4.50 9.99 4.69 11.89 4.89 10.39 20.88 4.50 1.30 6.79 1.50 12.49 1.80 1.30 13.29 16.68 15.08 14.28 17.08 1.50
- 1935 11 2.80 4.49 8.99 1.50 4.09 2.80 1.50 1.20 3.89 0.50 12.08 3.50 4.19 6.69 10.29 2.70 14.98 0.60 3.30 0.40 0.10 0.50 1.00 1.50 8.29 12.08 11.49 5.59 11.78 12.68 -99.99
- 1935 12 8.40 2.50 2.80 1.70 1.30 0.90 8.90 6.60 0.00 0.00 0.30 1.10 0.70 16.10 6.90 0.00 0.00 0.00 0.00 0.00 1.50 0.10 0.00 6.20 7.00 5.70 2.00 1.40 6.20 1.40 5.40
- 1936 1 14.78 0.20 0.10 5.39 13.78 4.69 0.10 6.09 32.35 5.39 1.40 2.40 0.10 0.00 0.00 0.10 3.79 0.00 1.60 9.79 2.10 4.99 2.30 1.70 10.68 4.49 4.49 1.40 1.10 2.50 4.09
- 1936 2 4.79 0.20 0.10 0.40 0.60 1.80 2.40 0.00 0.00 0.00 0.00 0.00 0.70 2.70 0.30 0.00 6.39 8.89 7.59 2.60 9.49 2.40 5.09 0.20 2.00 8.19 4.69 1.80 0.70 -99.99 -99.99
- 1936 3 0.40 1.00 1.70 10.90 10.30 0.80 9.40 3.30 2.30 0.00 0.00 0.00 0.00 0.50 0.10 1.40 0.40 0.00 2.50 2.50 3.10 2.30 1.90 0.00 0.20 3.70 3.30 3.40 14.70 5.10 3.10
- 1936 4 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.50 0.40 1.29 0.10 0.30 0.10 2.69 0.90 0.00 0.30 0.40 0.00 10.44 8.46 5.17 1.69 2.69 2.19 0.20 0.00 -99.99
- 1936 5 0.00 0.00 0.00 0.00 0.10 1.10 0.00 0.00 0.00 0.00 0.70 1.51 3.61 1.91 6.42 18.97 5.72 0.50 0.00 0.00 0.00 0.60 0.00 1.20 0.00 0.10 0.00 0.30 8.13 1.41 2.21
- 1936 6 1.30 2.21 0.10 0.00 1.30 0.00 0.00 0.20 2.41 0.10 1.71 0.90 0.50 5.72 3.71 11.34 2.31 0.00 1.10 0.10 0.00 3.21 0.80 0.00 0.00 0.00 2.51 0.20 15.85 2.81 -99.99
- 1936 7 13.71 4.70 0.40 3.30 2.50 2.90 0.90 0.00 0.90 2.70 4.40 9.01 1.10 1.70 0.60 0.30 10.21 12.91 2.30 2.80 1.50 4.20 18.31 24.52 9.81 1.20 0.10 2.30 0.70 15.31 1.60
- 1936 8 16.70 4.20 1.00 2.10 1.70 1.60 0.10 8.10 0.40 0.10 10.60 0.40 7.20 5.00 4.60 1.50 7.00 1.60 1.60 0.70 0.40 0.40 7.70 2.00 0.00 0.00 0.00 0.10 0.30 0.70 0.10
- 1936 9 13.79 12.59 4.40 9.99 4.20 17.28 6.99 0.00 4.20 0.40 6.49 4.10 3.20 1.50 0.00 0.00 0.70 0.00 0.00 0.00 0.10 0.00 0.00 17.58 0.70 1.70 0.20 0.00 0.00 0.00 -99.99
- 1936 10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.20 0.10 2.10 10.20 1.10 2.70 17.10 9.20 18.50 1.90 1.10 2.10 5.20 3.40 39.70 8.20 17.10 4.90 2.90 8.50 0.80 0.70
- 1936 11 7.89 4.30 1.90 3.30 7.79 5.80 9.19 11.59 5.10 0.80 3.20 0.90 3.40 14.19 10.49 8.59 2.80 0.10 0.00 0.00 0.10 0.00 0.60 0.30 0.40 0.20 1.40 4.30 4.80 4.90 -99.99
- 1936 12 5.30 3.10 6.10 14.09 6.70 0.20 9.79 0.40 0.20 3.20 8.90 1.50 32.68 2.60 9.40 8.30 9.20 5.70 12.39 11.39 14.09 1.50 0.70 1.90 1.20 0.10 0.00 1.30 3.60 11.59 7.90
- 1937 1 8.30 11.60 7.80 18.30 17.80 8.70 0.60 1.20 8.60 0.70 6.60 20.40 0.40 0.00 10.40 2.60 17.80 0.50 0.50 12.80 7.60 3.90 1.80 8.60 1.40 0.40 0.30 0.20 0.50 3.90 2.40
- 1937 2 1.30 16.72 8.51 5.41 1.80 2.80 7.01 7.11 3.10 3.30 0.20 7.71 4.81 2.90 10.92 4.91 5.51 11.82 9.41 2.80 0.60 0.10 0.00 3.61 8.71 4.11 5.11 0.40 -99.99 -99.99 -99.99
- 1937 3 0.50 0.00 1.50 0.60 0.90 0.50 0.10 0.00 0.10 0.10 2.10 2.60 0.30 0.00 0.00 15.50 6.80 7.40 2.80 1.80 0.30 0.70 2.10 1.00 0.10 0.00 0.00 0.00 0.00 0.50 1.60
- 1937 4 0.00 0.30 1.60 1.90 1.50 2.80 7.90 6.10 11.30 0.40 0.00 0.00 0.20 2.10 11.40 4.20 1.30 0.40 10.40 1.90 4.00 0.70 0.50 0.00 0.00 1.50 0.50 0.10 0.00 0.00 -99.99
- 1937 5 0.00 0.00 0.10 5.29 0.30 2.50 0.20 0.30 0.40 0.40 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.50 1.80 10.48 11.18 4.29 0.80 5.49 0.60 3.19 0.50 0.40 0.00 1.30 1.50
- 1937 6 0.70 9.09 12.89 16.18 9.29 10.49 0.90 1.60 0.10 0.00 2.20 0.40 1.20 0.20 0.30 0.00 0.00 1.70 0.80 1.10 0.80 0.10 0.00 0.00 0.10 0.50 7.59 7.49 3.50 5.29 -99.99
- 1937 7 4.60 18.61 8.21 12.41 14.31 3.60 1.70 6.71 1.90 0.10 0.00 2.90 9.21 8.91 0.10 0.00 0.00 5.40 0.20 11.21 8.41 1.70 1.90 2.30 0.10 0.00 0.00 0.00 0.00 0.00 0.00
- 1937 8 0.00 0.00 0.00 1.90 0.30 10.40 0.00 1.80 2.40 0.00 0.00 3.90 10.90 10.90 0.90 13.00 0.50 9.10 0.10 0.40 0.00 2.60 0.00 0.50 0.20 0.00 0.00 3.60 9.20 3.30 16.70
- 1937 9 9.02 3.51 3.21 12.83 2.61 4.31 12.83 2.00 0.10 0.50 1.70 3.31 0.20 6.01 5.91 0.20 0.90 0.00 1.00 0.00 0.50 0.00 4.51 1.40 0.00 0.30 1.90 0.00 2.81 16.13 -99.99
- 1937 10 18.03 5.61 0.70 0.00 0.00 0.00 0.40 0.00 0.00 0.00 0.00 0.20 1.50 1.30 0.90 1.80 0.00 0.00 1.40 5.31 9.52 8.42 4.01 2.91 15.93 6.91 4.21 1.40 5.21 7.21 0.20
- 1937 11 0.90 0.40 1.20 1.00 0.00 0.50 1.00 0.00 0.00 0.10 0.50 0.30 0.30 0.40 0.20 0.10 0.00 3.10 1.80 5.30 6.00 0.00 0.10 0.80 0.50 0.00 0.00 2.50 5.20 3.80 -99.99
- 1937 12 0.20 0.20 2.90 7.81 4.10 0.00 1.40 0.40 1.20 10.41 1.30 3.30 3.20 0.80 0.20 0.00 0.00 0.90 0.10 1.90 10.11 14.62 14.12 3.60 0.60 0.90 0.20 0.00 0.20 0.10 0.00
- 1938 1 0.00 0.00 0.40 0.40 1.00 7.20 0.70 3.50 17.09 3.10 7.40 10.99 4.50 17.59 8.10 10.89 1.00 12.89 5.80 9.90 2.00 0.50 4.20 13.59 6.40 7.30 10.79 13.79 3.50 11.39 18.89
- 1938 2 6.51 5.41 3.50 3.30 1.80 0.80 0.50 11.41 6.71 1.10 0.70 0.00 0.00 0.10 0.00 0.20 0.00 0.00 0.10 0.00 0.00 0.10 0.00 3.00 13.22 7.21 7.51 8.51 -99.99 -99.99 -99.99
- 1938 3 1.00 0.20 0.40 0.00 0.90 0.40 0.00 2.60 3.40 0.70 0.00 0.00 0.00 6.40 18.50 3.50 6.40 10.70 11.50 6.80 0.20 2.00 2.90 8.40 1.80 4.90 0.40 7.50 2.20 2.40 3.20
- 1938 4 4.20 7.50 0.10 2.40 0.30 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.20 0.10 0.10 0.20 0.00 0.10 0.40 1.10 0.00 0.00 0.00 -99.99
- 1938 5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.60 1.20 0.90 11.68 4.59 14.07 9.98 8.38 1.70 3.89 0.20 0.00 0.00 4.69 3.29 4.39 0.30 8.68 13.18 2.79 6.89 0.60 0.70 6.09
- 1938 6 9.60 3.40 2.20 5.10 5.80 20.40 1.30 4.20 1.10 0.20 2.00 0.00 0.00 0.00 0.00 0.00 0.00 5.10 2.60 2.90 1.40 0.00 13.80 8.10 1.20 31.80 19.70 20.20 3.10 3.10 -99.99
- 1938 7 0.30 0.50 2.40 2.00 4.80 2.30 25.18 3.40 2.00 7.00 1.20 0.10 17.69 0.70 0.10 0.20 0.90 1.30 0.80 0.00 0.00 0.00 0.10 1.80 3.30 4.30 15.59 3.60 33.88 9.39 0.90
- 1938 8 0.00 0.00 0.00 0.00 6.49 0.60 2.50 0.20 0.10 0.00 4.69 0.20 0.10 0.00 9.09 7.09 9.39 14.38 5.59 3.90 0.80 0.00 3.10 0.90 0.10 0.50 1.90 1.30 4.69 0.70 2.20
- 1938 9 0.80 5.21 3.11 0.10 5.81 2.60 0.20 0.00 0.00 0.50 0.70 1.00 6.81 0.20 5.81 29.65 11.12 4.51 6.71 5.91 4.71 1.10 10.42 2.30 0.00 0.00 0.10 2.70 4.61 3.71 -99.99
- 1938 10 0.80 15.81 33.81 16.61 8.30 11.90 9.60 20.71 10.20 8.20 10.10 12.40 10.70 1.40 11.10 10.10 2.10 5.40 1.90 1.60 9.80 4.40 0.70 7.20 2.80 4.20 2.80 0.70 10.30 4.00 16.11
- 1938 11 14.80 4.50 22.70 4.20 0.60 1.80 11.70 16.50 1.00 3.40 5.60 20.40 8.60 0.20 0.60 5.10 2.10 22.40 4.60 1.50 3.80 11.40 10.40 11.50 8.10 2.60 14.70 6.20 13.50 15.50 -99.99
- 1938 12 6.00 9.29 0.70 14.09 4.20 15.09 6.69 2.80 8.89 3.90 8.09 3.80 4.00 0.70 5.90 2.20 0.60 0.00 1.00 0.70 0.20 0.00 0.30 1.80 6.19 0.80 0.30 4.90 5.20 1.80 3.10
- 1939 1 1.30 1.30 0.00 0.00 0.30 9.08 15.37 14.48 3.69 0.50 0.10 1.20 4.69 18.27 11.88 10.38 2.30 2.50 7.49 0.70 1.10 3.59 0.80 4.89 0.70 0.90 0.10 0.20 0.00 0.10 0.00
- 1939 2 0.00 0.40 2.20 3.30 6.39 19.58 5.59 23.37 6.69 1.30 9.29 5.19 0.20 0.80 4.89 6.29 3.20 4.99 2.80 5.59 11.59 3.60 3.50 9.59 7.59 9.29 10.89 2.00 -99.99 -99.99 -99.99
- 1939 3 11.42 9.02 3.21 9.12 5.31 4.81 18.84 5.81 1.20 1.50 5.61 0.70 0.20 1.20 1.30 0.10 2.60 0.80 0.70 1.50 10.12 5.11 1.50 1.10 0.40 1.00 1.00 0.90 0.80 0.00 0.00
- 1939 4 1.40 0.30 0.40 0.00 0.00 0.00 2.00 4.00 0.00 0.00 1.00 3.00 12.70 7.30 10.10 6.60 0.90 0.10 0.00 1.10 7.00 1.20 2.10 2.70 0.70 4.30 0.30 0.30 0.20 0.00 -99.99
- 1939 5 0.00 0.00 0.10 3.01 3.21 0.70 2.91 2.11 0.20 0.00 0.00 0.00 3.91 4.71 0.70 0.00 0.00 0.00 0.30 0.90 6.32 0.10 0.60 0.30 0.00 1.30 0.20 0.00 0.00 0.00 0.00
- 1939 6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.49 2.30 2.40 0.30 1.20 15.88 4.79 0.20 5.19 2.60 0.50 0.50 0.00 0.00 0.00 0.10 0.30 0.00 11.98 11.38 1.40 2.30 -99.99
- 1939 7 3.10 3.00 0.80 4.70 13.60 9.50 6.50 3.50 2.40 0.00 2.00 11.40 17.80 10.90 11.70 4.20 0.10 0.80 0.20 0.70 1.40 5.50 2.50 0.00 0.20 0.10 4.90 20.00 1.20 4.10 2.80
- 1939 8 3.80 4.30 0.70 0.00 0.00 1.00 1.30 0.00 11.80 4.90 1.70 0.00 0.40 0.30 0.00 0.00 0.00 0.00 10.20 0.30 1.30 0.00 0.00 0.00 1.50 2.50 4.80 0.70 0.00 0.30 2.40
- 1939 9 3.80 7.51 26.23 0.60 1.50 4.81 10.01 3.10 12.41 11.41 0.50 2.00 0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.90 -99.99
- 1939 10 0.70 0.30 0.00 0.20 4.19 0.50 0.00 4.09 18.84 19.24 4.69 2.49 1.40 0.40 0.00 0.00 0.50 0.00 0.00 0.00 0.80 1.79 1.69 0.10 0.00 0.40 0.80 0.10 1.20 0.70 0.80
- 1939 11 0.10 1.50 3.00 1.40 11.10 6.30 19.89 22.09 5.10 4.40 9.00 4.80 11.00 19.79 5.60 1.60 2.40 2.10 0.70 0.40 2.10 11.69 2.20 6.70 20.29 10.10 7.00 11.59 6.20 18.19 -99.99
- 1939 12 14.08 6.89 15.58 2.20 0.50 0.20 3.90 3.50 13.48 5.79 0.30 0.00 1.70 4.29 0.90 0.40 0.00 0.10 0.30 0.00 0.00 0.30 1.20 0.20 0.70 0.10 0.30 0.70 0.20 0.00 0.10
- 1940 1 0.10 0.00 0.10 0.00 0.00 4.11 6.51 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.50 7.91 6.51 5.21 0.40 0.00 0.00 7.71 11.01 0.60 7.21 6.11 7.41 0.00 0.10 3.00
- 1940 2 2.30 0.00 2.20 4.61 1.30 6.51 2.70 0.50 0.00 0.00 0.20 0.00 0.10 0.90 2.80 0.00 0.00 0.00 2.20 2.90 0.60 4.51 7.21 2.00 0.20 3.91 6.61 4.21 0.00 -99.99 -99.99
- 1940 3 0.00 0.00 0.10 0.10 0.00 0.00 0.90 6.49 8.29 3.00 14.78 12.78 0.40 2.90 1.50 2.70 5.69 4.79 9.58 7.29 5.59 4.59 2.60 2.80 0.10 0.30 0.00 1.30 7.69 6.39 12.88
- 1940 4 3.31 3.01 7.33 1.91 0.70 5.12 4.42 0.50 0.30 0.00 1.20 0.10 0.80 9.23 1.81 0.80 0.80 0.90 2.71 5.02 11.64 2.01 7.63 5.02 0.20 0.00 0.00 0.40 2.71 0.80 -99.99
- 1940 5 0.70 0.00 0.00 0.00 3.88 0.70 2.59 0.50 0.00 0.00 0.00 0.00 0.00 2.19 6.56 3.58 0.80 0.00 0.00 1.19 0.80 0.70 1.29 0.60 3.78 2.79 0.40 0.50 0.40 2.09 1.19
- 1940 6 0.80 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.80 0.70 0.00 0.00 5.20 0.90 0.00 0.00 0.00 0.00 6.40 6.80 0.30 1.40 3.90 1.40 0.40 0.50 2.60 0.10 -99.99
- 1940 7 1.50 7.71 2.50 2.00 5.90 3.10 1.80 3.20 7.21 11.91 18.32 0.00 4.30 9.31 0.40 5.50 11.31 2.10 3.60 3.00 0.10 0.20 1.80 3.80 0.50 3.30 0.10 0.10 3.00 1.60 0.10
- 1940 8 0.00 0.00 0.00 1.60 0.60 0.00 3.31 6.01 10.02 2.50 0.10 0.50 2.10 2.00 0.10 0.10 1.20 0.80 4.81 11.52 0.90 0.20 0.30 0.40 3.31 4.91 0.60 2.20 0.50 0.30 0.80
- 1940 9 0.10 0.00 0.20 2.70 2.00 3.00 1.20 0.50 3.60 1.20 7.41 7.61 5.60 0.90 0.80 32.03 10.91 7.41 12.31 2.50 1.10 12.21 2.90 0.10 0.20 1.70 1.20 0.00 0.00 0.20 -99.99
- 1940 10 0.10 0.30 0.00 12.01 22.21 3.30 7.60 22.41 17.01 6.60 1.20 0.20 0.00 0.40 3.80 4.00 1.10 0.30 3.90 17.81 11.71 0.80 0.30 0.10 0.30 0.00 0.00 0.10 4.90 18.71 3.90
- 1940 11 5.80 21.90 3.80 3.10 13.90 5.30 3.40 14.20 9.30 7.30 19.70 1.00 0.70 0.50 2.90 0.90 4.10 0.30 20.50 14.70 1.40 4.80 4.50 3.10 3.20 6.70 2.00 0.30 0.70 2.20 -99.99
- 1940 12 8.99 3.70 2.40 8.19 23.67 13.48 0.50 7.99 7.79 4.59 0.80 1.10 7.09 14.58 13.78 8.99 11.19 9.19 0.80 0.10 0.00 0.00 0.00 0.20 0.20 0.40 0.00 3.60 9.39 11.19 2.00
- 1941 1 1.11 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.00 0.20 0.30 1.72 0.50 0.00 0.00 0.00 0.71 0.71 0.40 4.85 5.15 1.82 2.02 1.01 0.30 2.83 3.74 0.40 0.40 1.92
- 1941 2 1.30 0.40 0.00 3.00 14.01 10.01 4.50 8.51 3.50 2.30 0.70 16.22 3.60 5.91 5.20 1.10 0.30 0.70 1.20 0.10 0.00 0.30 1.10 0.20 0.00 8.11 12.31 2.50 -99.99 -99.99 -99.99
- 1941 3 3.70 3.50 4.01 7.01 5.61 1.30 0.60 0.60 0.10 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.70 0.00 2.10 0.30 0.40 0.30 2.20 11.32 9.81 4.51 0.40 1.40 9.61 5.61
- 1941 4 2.71 1.40 2.01 0.80 0.10 0.00 0.00 0.10 0.20 2.51 1.91 1.30 2.31 2.01 2.01 17.16 9.83 1.40 3.91 3.21 1.10 0.40 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 -99.99
- 1941 5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.10 0.30 2.00 2.70 0.90 0.90 1.60 1.70 0.20 2.60 4.70 19.60 21.70 1.90 0.60 6.70 3.00 0.30 0.60 0.00 0.00
- 1941 6 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.40 6.20 0.90 3.00 4.50 0.40 0.00 0.10 2.10 4.80 0.60 0.60 4.80 0.90 0.40 0.40 0.00 0.00 -99.99
- 1941 7 0.00 10.20 3.50 0.20 2.90 11.40 8.00 0.10 0.00 0.00 1.10 1.30 1.90 4.10 4.00 0.50 9.10 2.20 0.30 9.50 2.70 0.80 0.00 1.50 10.10 0.50 0.20 1.00 2.90 1.90 0.00
- 1941 8 0.00 0.00 1.50 6.01 1.70 0.40 0.10 0.00 3.50 9.61 7.91 8.11 8.31 4.01 22.43 7.01 9.21 3.40 4.31 4.01 0.30 0.40 1.40 0.40 7.71 5.31 14.42 11.82 4.01 0.30 1.00
- 1941 9 4.50 0.80 0.10 0.00 0.00 0.00 0.20 0.70 0.50 1.10 0.60 0.10 0.20 0.50 0.10 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.40 5.00 8.60 6.70 9.50 7.80 4.70 -99.99
- 1941 10 1.50 0.60 0.00 0.00 5.50 2.10 5.70 3.00 26.72 1.00 0.00 0.40 7.81 2.30 19.91 6.50 22.92 7.71 14.31 3.80 1.00 0.00 0.00 0.00 0.10 0.10 0.90 0.10 1.60 1.50 0.00
- 1941 11 0.20 0.30 1.00 0.10 3.69 3.69 1.20 1.00 4.79 12.48 6.79 0.80 12.98 0.10 0.50 4.99 2.90 0.20 0.30 8.09 5.19 3.79 9.38 8.99 2.80 14.28 9.88 0.10 0.50 0.50 -99.99
- 1941 12 0.40 0.10 0.30 0.60 6.18 10.17 0.20 1.40 0.70 7.87 7.57 4.68 9.77 14.05 7.87 2.39 0.40 1.79 0.80 1.59 1.59 1.00 2.29 1.30 0.20 3.09 0.20 0.10 0.00 0.00 0.00
- 1942 1 5.20 16.80 18.70 6.00 0.00 0.10 0.30 0.20 0.10 0.10 1.10 9.00 1.70 0.40 0.10 1.70 4.70 0.40 12.90 1.00 14.30 15.10 15.00 16.70 3.30 1.20 16.20 13.40 1.10 2.60 10.60
- 1942 2 7.94 24.33 3.32 0.20 1.41 0.40 0.30 0.90 0.90 2.31 3.62 0.40 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.10 0.20 0.00 0.40 0.00 0.00 1.01 7.54 1.31 -99.99 -99.99 -99.99
- 1942 3 0.00 0.10 7.00 9.80 3.90 4.80 7.20 3.30 0.00 0.00 0.10 0.40 1.80 4.20 3.70 14.10 2.30 0.50 1.30 2.20 0.00 0.00 0.00 0.30 0.00 1.50 0.30 0.10 2.70 7.50 11.90
- 1942 4 0.40 4.30 10.90 9.80 8.80 10.50 10.90 5.60 8.00 0.20 0.10 1.70 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.20 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1942 5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.10 0.00 0.00 0.00 7.61 2.00 15.12 2.60 1.50 0.00 1.50 1.20 10.91 6.21 7.01 11.71 12.41 6.01 1.40 0.50 2.60
- 1942 6 11.58 0.20 0.00 0.00 0.20 0.30 1.50 0.20 0.00 0.00 1.50 8.98 3.29 1.10 0.00 0.00 0.20 0.00 0.00 15.77 5.99 0.60 0.00 0.00 0.00 0.10 1.60 0.20 0.00 0.00 -99.99
- 1942 7 0.00 1.20 13.11 0.60 2.50 4.40 3.80 4.80 0.60 3.20 0.20 4.30 1.20 1.60 4.70 2.30 0.60 0.00 0.30 0.20 7.11 11.41 11.91 5.71 4.10 2.50 0.10 8.51 0.20 0.10 0.10
- 1942 8 0.00 1.60 0.00 0.00 0.00 5.11 17.92 10.91 8.31 15.82 4.01 1.50 1.00 2.40 11.42 7.91 4.51 3.10 2.10 5.11 11.02 1.90 0.00 7.71 11.02 0.00 0.00 0.00 2.80 3.91 4.81
- 1942 9 3.60 17.71 7.70 25.21 5.10 2.60 17.41 4.20 0.20 0.10 0.00 0.00 1.20 8.51 4.40 4.40 3.40 1.90 3.50 30.02 2.50 6.70 4.10 2.30 0.40 0.00 5.60 4.20 0.20 1.40 -99.99
- 1942 10 0.40 0.40 13.40 5.20 0.50 0.00 6.00 6.10 22.60 6.80 1.10 6.40 8.60 8.90 13.10 4.40 6.30 2.90 5.30 1.40 5.90 1.40 10.00 19.70 7.80 3.70 2.30 0.40 0.10 0.00 0.00
- 1942 11 0.00 0.60 0.10 0.20 0.90 13.06 3.59 0.40 0.90 1.10 2.69 0.20 0.50 0.50 0.10 0.00 0.00 0.00 1.00 0.90 0.00 0.20 0.30 0.00 0.00 0.10 0.00 0.80 0.40 6.28 -99.99
- 1942 12 0.00 0.10 0.00 22.09 3.20 8.70 7.50 11.59 13.19 26.49 0.60 4.00 1.40 1.70 4.10 11.69 3.90 2.00 8.60 17.89 7.40 1.80 2.20 1.70 1.90 3.70 3.60 3.80 0.30 9.40 11.89
- 1943 1 5.61 1.30 0.00 0.60 1.30 1.10 0.20 0.80 12.72 0.50 7.01 6.21 2.80 7.81 2.90 17.22 2.80 0.10 0.90 13.42 2.00 0.50 0.10 15.52 6.71 4.41 11.51 9.41 7.41 3.10 8.01
- 1943 2 3.80 5.30 3.80 14.40 23.10 3.30 1.60 21.20 2.70 5.00 22.70 5.80 4.10 7.90 2.60 0.90 1.00 0.30 0.80 0.50 0.00 1.10 1.80 5.70 4.30 0.20 0.40 0.20 -99.99 -99.99 -99.99
- 1943 3 0.50 0.10 0.00 0.10 0.00 0.00 0.90 3.40 2.60 3.50 1.60 1.30 0.00 2.10 4.71 0.70 1.50 0.00 0.20 0.00 0.00 0.00 0.00 0.70 3.40 1.90 2.80 1.40 12.82 23.53 3.40
- 1943 4 1.00 0.10 0.00 0.00 7.40 3.00 0.30 0.60 1.50 3.50 3.90 15.40 5.00 3.40 2.60 0.30 1.90 4.50 1.20 0.00 0.70 0.40 5.60 10.80 16.70 4.50 7.40 0.00 4.50 0.20 -99.99
- 1943 5 0.00 1.10 0.00 0.00 0.50 3.10 6.49 9.29 0.70 0.90 4.50 17.28 10.49 0.20 0.00 0.00 0.00 0.00 0.10 0.00 1.20 19.08 2.90 0.00 3.50 8.59 0.00 0.00 9.99 1.00 9.19
- 1943 6 8.71 3.21 0.90 13.42 4.51 1.30 7.11 0.10 1.50 1.70 1.80 5.81 7.71 3.61 2.00 8.11 3.11 13.82 11.22 5.11 2.70 8.31 0.90 3.21 0.30 0.00 0.00 0.00 0.00 0.00 -99.99
- 1943 7 0.00 0.00 1.00 0.80 13.46 6.88 1.70 2.99 0.60 5.98 3.09 14.56 3.29 6.08 0.30 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.20 2.39 1.10 1.10 0.10 1.10 5.68
- 1943 8 16.11 4.00 1.60 12.11 4.30 1.30 21.21 1.70 1.20 6.40 0.80 10.11 1.10 3.20 3.50 0.70 1.80 0.90 13.71 4.00 2.80 4.80 0.80 5.80 7.80 5.70 1.60 12.61 14.61 0.50 15.81
- 1943 9 6.09 1.50 0.10 19.78 3.20 4.10 9.49 0.00 0.00 3.20 1.50 3.70 7.09 0.50 7.99 3.50 0.00 0.70 4.20 1.30 0.30 1.90 4.50 2.20 0.40 0.60 17.58 2.60 0.50 3.10 -99.99
- 1943 10 9.81 8.31 29.23 11.71 29.83 1.50 0.10 5.00 1.20 14.01 0.40 6.21 0.20 0.00 0.00 11.11 19.52 0.50 10.71 15.01 7.71 1.10 0.50 0.10 4.10 25.52 4.80 0.10 0.00 5.50 9.21
- 1943 11 2.00 0.80 0.00 6.41 7.82 0.30 2.30 1.80 2.30 6.21 3.01 3.41 2.71 0.30 0.20 1.00 0.00 0.30 0.90 0.50 1.20 3.31 23.55 2.61 0.90 1.50 6.51 5.51 6.81 0.30 -99.99
- 1943 12 11.51 0.60 0.10 0.10 0.30 1.20 7.41 0.20 0.10 0.00 0.00 0.00 0.10 0.10 0.10 0.30 6.91 9.71 11.81 10.71 8.01 2.20 0.60 1.70 1.60 0.90 1.70 2.60 0.50 0.50 3.40
- 1944 1 1.30 6.80 0.50 0.60 7.00 5.00 9.29 7.70 0.00 0.00 0.70 12.29 9.89 0.20 0.10 3.30 1.40 4.30 2.10 11.79 10.79 9.59 6.40 19.09 1.60 14.89 4.20 2.40 4.30 1.10 6.70
- 1944 2 6.61 12.92 2.50 0.30 0.50 7.51 3.61 0.80 2.40 0.00 0.00 0.40 4.81 0.00 6.01 0.10 0.10 0.00 0.40 0.30 0.70 0.70 0.30 0.10 1.00 2.60 0.30 0.70 1.90 -99.99 -99.99
- 1944 3 6.75 0.60 0.20 0.40 0.10 0.00 0.00 0.10 0.79 0.30 2.08 4.37 0.10 0.00 0.20 0.69 0.40 3.37 1.98 0.60 0.50 0.10 0.00 0.20 0.00 0.00 0.00 0.30 0.10 0.89 0.00
- 1944 4 3.40 12.39 7.69 2.60 2.40 0.00 0.10 0.10 0.90 1.00 0.80 0.50 0.70 0.70 0.40 0.70 1.30 10.09 23.07 6.59 4.40 2.30 6.39 0.80 0.00 0.00 0.00 0.00 0.00 0.60 -99.99
- 1944 5 8.58 7.98 2.00 14.97 0.30 0.00 0.40 0.90 12.87 0.10 0.00 0.00 0.30 0.00 0.80 2.00 1.70 6.79 3.59 0.00 0.00 0.00 0.00 10.88 4.99 7.09 6.09 0.50 0.00 0.00 2.39
- 1944 6 2.70 2.90 15.69 15.49 0.70 0.40 0.90 1.00 1.10 1.80 3.40 4.70 6.59 0.80 5.89 0.20 0.00 0.00 0.00 0.00 0.00 0.20 0.00 1.30 2.50 8.49 19.48 4.20 7.19 5.79 -99.99
- 1944 7 3.90 30.77 2.70 2.00 1.80 3.60 11.39 1.50 4.99 0.70 0.20 5.59 0.70 6.19 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 9.29 3.30 2.70 0.90 0.20 3.10 0.10
- 1944 8 0.00 0.00 0.00 1.10 0.00 0.40 0.80 2.30 4.19 6.99 6.19 0.70 0.10 0.30 0.80 3.69 2.89 4.39 0.10 0.00 0.00 0.00 4.89 7.58 0.00 2.00 24.85 2.79 3.19 11.38 3.39
- 1944 9 1.10 1.10 22.10 20.10 2.10 2.10 4.00 2.20 0.00 0.00 0.00 0.00 4.10 13.90 13.00 0.20 0.00 1.30 2.30 0.10 0.10 11.90 10.20 0.20 5.30 6.70 0.70 15.90 5.00 1.50 -99.99
- 1944 10 0.80 4.99 1.40 3.50 2.30 0.40 0.30 0.10 0.00 7.09 20.67 2.10 15.18 5.09 2.40 0.40 14.68 2.40 8.69 14.98 5.29 10.39 1.90 1.20 1.30 10.99 10.29 1.50 0.10 0.70 0.50
- 1944 11 1.20 1.10 4.20 32.57 15.28 15.09 4.40 3.20 1.30 7.89 3.60 3.70 3.50 9.39 2.00 4.10 8.09 5.49 1.30 0.40 14.19 9.89 0.70 0.00 0.30 0.00 21.48 7.69 10.39 11.09 -99.99
- 1944 12 20.60 12.00 9.30 6.00 7.00 11.80 4.40 0.30 0.20 3.60 1.10 0.00 1.90 16.30 13.80 13.90 4.00 0.40 8.30 9.60 2.10 0.20 0.20 1.40 1.60 6.50 0.90 0.00 1.00 0.00 0.30
- 1945 1 2.00 10.88 0.70 0.10 1.20 1.20 0.00 0.20 0.50 0.40 0.50 0.10 0.10 0.10 1.60 4.79 22.06 7.49 1.20 0.10 1.20 2.50 2.70 2.99 0.00 0.00 0.10 1.90 31.35 2.10 14.28
- 1945 2 9.71 1.10 29.33 4.30 5.81 19.62 3.20 6.11 10.11 1.80 2.60 14.82 8.91 4.71 1.90 5.31 3.80 0.60 6.21 0.40 3.20 2.20 1.30 11.51 18.32 5.11 0.80 4.30 -99.99 -99.99 -99.99
- 1945 3 0.00 0.00 1.50 0.60 0.10 0.00 0.00 0.50 0.50 0.00 0.00 0.30 1.30 1.40 1.70 1.20 1.30 6.89 17.28 1.60 0.30 0.00 0.00 0.40 5.79 0.30 8.39 6.39 9.59 9.59 18.68
- 1945 4 7.56 5.97 2.19 6.27 4.08 3.48 0.90 0.00 0.00 4.48 10.55 1.79 1.00 7.76 0.50 0.10 0.70 0.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.30 1.19 1.00 0.40 0.30 -99.99
- 1945 5 1.00 1.60 2.00 0.00 7.99 0.80 2.30 5.10 0.80 1.00 0.70 1.40 17.79 4.10 8.79 27.58 1.20 0.20 0.00 10.99 2.40 9.29 0.00 0.80 0.30 4.00 2.50 2.70 2.50 1.60 6.00
- 1945 6 9.39 2.90 6.49 2.30 16.29 1.90 11.69 1.90 2.90 0.50 8.09 0.60 1.50 4.40 1.90 0.20 1.90 0.40 1.20 3.70 0.40 3.60 2.70 1.50 6.99 2.10 0.00 0.10 8.49 7.19 -99.99
- 1945 7 3.60 0.00 0.20 2.40 0.60 1.30 0.10 0.20 14.20 0.00 0.20 0.10 15.30 0.00 7.40 8.40 0.20 11.40 9.00 5.60 9.40 2.30 0.80 0.00 0.00 0.20 0.30 0.00 0.10 0.00 0.00
- 1945 8 0.00 0.00 0.00 1.40 3.11 8.41 0.80 0.00 0.00 0.00 0.00 0.00 0.00 4.01 6.01 0.20 0.00 0.30 0.00 0.00 5.91 0.60 19.93 3.21 0.20 0.00 0.20 2.91 0.50 0.10 0.00
- 1945 9 0.00 0.00 0.00 1.30 0.00 0.00 0.00 0.00 0.60 5.01 3.30 19.02 10.11 4.91 12.32 24.63 5.71 0.80 8.41 9.91 21.93 17.12 2.40 0.00 1.80 4.71 2.40 0.00 0.60 0.50 -99.99
- 1945 10 0.00 0.10 0.50 0.40 0.00 0.20 1.00 1.10 17.40 9.70 0.40 0.00 0.00 0.10 0.00 0.10 0.10 0.00 0.50 3.10 5.00 7.00 26.80 26.80 10.10 1.40 11.90 13.80 1.10 6.90 11.10
- 1945 11 1.51 0.90 1.61 0.40 0.00 0.00 3.32 0.00 0.00 0.10 1.11 0.00 0.20 0.20 0.70 0.00 0.00 0.20 0.50 0.20 0.80 0.90 1.11 0.30 2.61 0.10 2.21 0.40 1.31 0.50 -99.99
- 1945 12 2.90 8.60 3.20 8.40 0.60 5.40 6.20 6.60 0.50 0.30 0.20 1.90 1.00 2.10 7.90 11.20 11.70 9.70 0.80 0.70 1.90 2.50 2.10 3.30 3.10 14.70 2.90 0.30 0.10 0.70 0.00
- 1946 1 0.00 0.00 20.21 18.61 1.00 2.20 0.80 4.60 17.61 7.50 5.70 2.80 0.00 0.00 0.00 0.00 0.00 2.30 1.10 0.40 2.50 19.01 5.10 10.51 18.51 2.60 6.60 12.51 8.10 5.70 10.51
- 1946 2 4.00 4.10 12.11 18.22 6.41 7.41 4.40 2.80 3.50 2.20 1.80 5.51 0.20 0.80 1.50 0.50 0.90 1.90 5.41 1.10 0.80 12.71 0.00 0.00 0.00 0.00 0.00 0.00 -99.99 -99.99 -99.99
- 1946 3 0.40 0.50 2.80 3.40 0.30 0.00 0.20 5.20 1.00 0.10 0.80 0.50 0.10 0.00 0.00 7.90 17.90 13.50 9.80 8.40 3.80 4.50 0.90 1.00 0.50 0.10 0.00 0.00 0.00 0.00 0.00
- 1946 4 0.00 0.00 0.00 0.70 0.00 7.03 4.72 0.20 0.00 0.00 2.01 2.31 0.20 0.00 0.40 17.06 0.00 0.00 2.81 0.80 0.60 6.02 3.61 4.12 0.90 0.30 0.00 0.00 0.00 0.00 -99.99
- 1946 5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.10 0.30 1.69 0.70 0.00 0.20 2.39 2.59 0.00 4.58 0.00 0.00 2.09 1.99 5.48 0.00 2.59 4.78
- 1946 6 3.50 2.80 2.90 18.82 9.01 3.10 0.70 0.10 4.00 1.60 4.50 0.90 0.60 2.00 0.60 4.40 2.90 3.30 2.20 0.40 0.10 0.10 3.00 0.40 5.41 1.50 7.51 4.10 15.61 0.10 -99.99
- 1946 7 0.40 1.70 6.49 14.49 2.90 0.20 0.00 0.20 0.10 0.00 0.00 0.00 5.20 3.10 5.79 5.00 4.90 5.50 2.50 0.30 4.20 5.99 10.59 1.30 2.70 0.80 1.00 4.80 15.29 4.30 1.60
- 1946 8 3.40 3.70 4.90 2.90 3.50 3.50 2.40 4.20 3.70 0.80 0.60 5.19 1.10 5.09 0.30 0.20 0.70 2.90 2.60 0.00 1.60 0.00 4.30 2.60 0.00 0.10 4.40 14.69 7.49 7.49 5.49
- 1946 9 3.20 1.70 4.30 5.60 13.81 4.70 4.50 2.40 0.80 25.51 0.10 23.31 13.51 15.61 1.60 9.61 7.80 4.60 4.10 0.80 16.41 7.00 2.00 0.90 4.20 5.70 2.80 0.00 2.80 1.30 -99.99
- 1946 10 5.53 1.21 2.21 2.41 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 2.71 2.31 0.30 0.20 1.01 0.10 0.00 0.00 0.30 0.00 0.00 0.00 0.20
- 1946 11 0.40 2.90 6.10 6.00 2.00 1.30 0.30 0.30 0.00 0.30 2.70 7.00 1.80 0.10 0.30 11.01 19.71 7.00 6.60 16.21 13.31 4.30 10.71 6.40 16.11 10.01 6.10 12.11 5.60 7.90 -99.99
- 1946 12 13.71 7.20 1.20 0.00 8.71 0.20 1.10 4.00 0.20 6.30 13.81 1.90 5.70 8.71 0.50 0.00 0.00 0.80 0.30 1.40 11.01 10.81 1.10 14.91 15.51 3.00 2.90 0.20 5.10 8.41 4.50
- 1947 1 11.81 8.41 16.21 9.21 1.00 3.80 5.70 15.91 2.00 5.10 10.51 7.11 2.00 15.61 3.50 10.41 7.51 1.20 0.00 0.00 0.00 0.00 0.00 0.10 0.60 0.10 0.20 0.40 0.20 0.10 0.00
- 1947 2 0.40 0.30 1.59 1.99 0.60 0.10 0.10 0.00 3.69 0.70 0.20 0.20 0.10 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.70 0.10 0.00 0.00 6.28 7.77 0.30 0.60 -99.99 -99.99 -99.99
- 1947 3 0.00 0.00 1.00 0.40 0.20 1.70 0.10 0.10 0.60 0.90 1.50 5.20 6.19 1.20 5.40 7.29 3.40 7.19 0.60 3.30 12.29 3.60 5.99 2.80 7.89 9.39 7.69 4.70 6.49 0.70 0.10
- 1947 4 0.00 0.00 0.00 3.20 23.01 6.30 6.50 2.30 1.00 2.40 0.90 0.20 7.90 0.50 3.10 0.80 0.00 6.30 18.21 10.71 13.41 6.50 19.91 6.60 8.70 14.61 13.21 2.40 10.31 1.10 -99.99
- 1947 5 1.60 0.20 4.20 1.10 8.10 5.00 1.00 5.60 4.00 5.00 1.20 1.40 3.50 10.10 0.40 0.00 9.30 4.30 0.00 0.00 1.60 6.00 0.60 5.20 3.40 1.00 0.00 6.30 0.50 16.50 7.20
- 1947 6 0.50 0.00 8.40 9.40 7.10 1.50 5.20 9.60 1.80 0.90 0.00 1.70 1.20 4.00 2.20 4.00 10.10 1.70 4.90 6.30 2.50 1.10 3.00 7.90 0.00 0.60 0.80 6.20 1.70 0.70 -99.99
- 1947 7 1.20 0.70 4.91 11.23 2.41 3.71 7.52 5.82 1.90 1.50 0.40 0.70 0.10 0.00 9.02 9.63 0.00 10.73 0.00 9.42 8.62 0.00 4.31 0.20 1.50 4.81 3.81 10.43 0.00 0.50 0.00
- 1947 8 0.00 1.70 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1947 9 0.00 0.00 0.30 5.10 0.80 0.20 3.00 17.70 12.60 3.20 10.70 9.90 1.20 9.70 8.10 16.50 0.10 0.90 8.50 12.60 0.00 12.60 0.50 0.50 0.00 0.40 2.20 1.70 1.10 0.40 -99.99
- 1947 10 0.00 0.00 0.00 0.00 0.10 0.30 4.92 1.71 2.11 1.40 11.54 14.14 2.31 3.31 1.91 1.71 1.40 0.30 0.20 0.00 0.00 2.41 3.71 0.20 0.00 0.40 0.30 0.30 1.00 1.91 5.32
- 1947 11 9.61 16.02 9.31 0.40 0.00 2.70 3.10 24.62 12.41 8.51 24.42 2.50 0.40 3.20 1.20 0.50 0.40 0.00 18.42 24.52 20.92 13.31 6.21 2.10 0.30 0.30 0.10 0.60 0.20 0.10 -99.99
- 1947 12 0.00 0.00 0.50 0.30 1.90 0.40 3.60 0.60 0.00 1.30 0.80 0.20 0.50 0.10 0.00 0.00 0.00 0.00 0.00 0.10 0.40 1.70 8.21 7.21 7.61 10.21 9.01 4.71 1.60 1.60 15.52
- 1948 1 17.80 7.80 6.20 9.10 5.00 4.10 4.30 3.00 5.60 22.20 10.10 6.60 15.80 14.00 0.60 6.20 14.40 6.50 4.10 1.80 4.80 0.20 0.00 0.00 1.30 6.10 6.80 4.80 0.30 9.50 6.90
- 1948 2 18.51 4.20 7.31 1.80 15.41 16.61 8.31 20.31 10.71 2.20 18.31 3.40 2.40 3.90 0.30 1.30 0.10 0.00 0.30 1.50 0.30 0.10 0.70 0.20 0.00 0.00 0.00 0.00 0.00 -99.99 -99.99
- 1948 3 0.00 0.40 0.00 0.00 0.20 3.70 19.08 0.20 5.10 0.30 0.00 0.00 0.00 2.20 4.40 6.20 0.60 15.59 7.39 9.29 4.30 0.70 0.20 0.00 0.00 0.00 0.00 0.40 12.09 6.39 26.78
- 1948 4 16.02 2.60 2.70 1.10 4.00 3.50 15.42 1.70 0.10 0.80 0.20 0.90 1.60 0.20 0.00 0.00 4.91 6.21 0.00 0.50 3.60 7.51 1.00 0.10 0.00 0.90 12.71 3.90 2.60 0.20 -99.99
- 1948 5 1.01 1.21 1.81 10.88 0.50 0.50 0.00 0.00 0.00 0.00 2.32 0.20 4.23 0.81 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.42 0.30 0.10 0.30 0.10 0.00 3.02 0.00 7.35 5.74
- 1948 6 5.40 7.70 9.50 7.50 5.90 28.50 3.10 16.10 0.00 0.50 0.00 0.00 0.00 1.50 2.90 5.50 6.00 5.70 5.60 2.80 2.40 1.40 0.00 0.30 2.70 7.40 6.60 1.60 0.20 0.00 -99.99
- 1948 7 0.00 4.50 33.13 7.11 0.00 0.50 0.20 0.00 0.00 3.30 4.20 3.90 2.40 0.00 1.00 0.30 0.50 9.21 8.41 10.51 8.91 4.00 1.50 4.10 1.40 0.30 0.00 0.00 0.00 0.00 7.81
- 1948 8 0.10 1.20 1.70 1.00 7.30 3.30 16.00 2.30 0.10 0.60 0.90 22.00 0.00 10.60 5.50 4.00 3.40 0.50 0.10 0.50 12.20 8.10 7.80 18.00 8.60 1.70 0.00 15.20 5.90 18.00 23.60
- 1948 9 8.20 17.71 4.00 0.70 3.70 2.60 8.50 0.90 4.30 1.50 10.01 5.80 7.50 36.32 3.00 1.50 1.00 6.20 0.10 0.20 3.50 2.50 2.00 9.30 5.40 28.62 0.40 7.30 1.30 3.80 -99.99
- 1948 10 14.00 1.70 2.20 0.00 0.00 0.00 0.00 23.60 25.90 9.20 7.20 1.60 24.80 4.70 3.80 7.50 7.20 1.40 2.00 3.30 3.30 1.30 17.60 11.30 0.70 1.00 0.70 0.90 4.90 3.40 9.10
- 1948 11 2.10 15.09 20.58 6.59 3.90 0.20 0.00 0.00 0.10 0.30 3.90 15.69 2.70 2.30 5.40 5.40 16.29 0.80 5.30 8.09 0.30 0.00 0.00 0.40 1.00 0.10 1.00 0.20 0.20 0.00 -99.99
- 1948 12 6.10 9.50 1.90 0.70 17.61 12.71 9.71 8.90 13.21 7.70 22.31 1.20 6.70 12.11 3.40 0.80 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.10 0.00 1.10 5.00 26.11 14.11 4.20 6.90
- 1949 1 2.70 0.80 0.70 3.30 21.69 22.69 19.19 0.60 1.00 5.10 5.20 2.10 5.40 4.10 6.70 2.40 6.50 10.99 13.99 8.00 0.80 3.30 4.40 0.20 4.10 3.80 0.50 0.00 0.50 4.00 0.00
- 1949 2 0.00 0.00 0.00 0.00 0.20 1.60 8.49 11.19 6.10 3.10 6.00 3.50 5.50 7.20 2.90 0.80 0.70 7.49 16.29 7.49 9.19 20.59 3.50 1.60 7.99 7.00 1.70 8.09 -99.99 -99.99 -99.99
- 1949 3 0.00 0.00 11.53 7.92 7.92 0.70 2.81 0.30 0.10 0.00 7.92 6.02 2.01 0.40 6.42 2.01 1.10 0.20 1.40 7.42 4.01 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00
- 1949 4 1.00 6.31 16.42 3.70 0.30 1.80 6.61 1.90 5.31 24.33 20.43 8.51 1.20 0.10 1.30 0.40 0.10 5.11 1.40 13.52 6.51 14.12 4.91 3.10 0.80 5.01 1.30 1.10 0.30 0.90 -99.99
- 1949 5 0.00 0.00 0.00 6.12 1.30 7.22 2.01 0.10 0.00 0.00 0.00 0.00 0.20 3.11 0.10 0.20 6.92 1.71 0.00 0.00 0.00 3.61 4.11 1.10 0.10 6.12 3.71 3.51 3.11 7.42 2.61
- 1949 6 2.92 1.41 4.23 4.13 4.93 0.30 2.82 0.00 0.00 5.23 0.00 3.42 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.21 0.30 0.50 0.00 -99.99
- 1949 7 0.00 0.00 0.20 0.20 0.10 0.10 0.00 0.00 0.00 0.10 0.00 0.30 14.57 1.60 0.60 0.00 0.00 0.20 0.20 11.88 0.40 0.20 0.10 0.10 0.80 1.20 1.80 4.79 3.59 12.28 1.50
- 1949 8 13.39 2.70 0.30 7.09 5.50 1.50 47.16 2.80 3.90 5.70 0.10 0.00 3.00 4.10 8.09 0.00 0.00 0.60 0.10 0.00 3.60 0.50 0.10 0.00 0.00 0.30 1.30 1.10 3.50 1.30 8.79
- 1949 9 0.30 7.20 0.70 8.90 2.70 3.80 1.30 12.00 12.50 0.20 0.00 0.00 0.20 0.70 5.80 2.20 0.00 0.00 0.00 0.10 0.20 1.10 0.50 1.20 0.20 0.00 0.00 0.30 0.80 0.90 -99.99
- 1949 10 2.10 4.49 0.00 0.00 0.10 7.49 2.50 0.70 2.70 11.38 2.90 1.00 0.10 1.70 5.59 3.19 23.76 5.79 4.09 5.29 9.38 3.19 19.77 1.70 36.04 0.60 0.20 6.59 1.00 8.29 2.20
- 1949 11 6.11 0.90 7.31 15.93 4.01 10.02 9.32 2.81 15.53 5.21 14.13 16.53 0.70 3.01 4.71 5.31 4.21 0.80 2.60 7.21 4.51 9.92 0.70 0.20 1.40 0.20 0.60 1.30 4.61 5.01 -99.99
- 1949 12 8.30 20.10 8.10 13.60 6.60 18.90 8.90 4.50 2.60 0.00 1.70 5.90 8.10 13.40 4.80 12.90 5.40 10.00 8.20 1.20 10.40 1.90 13.10 13.80 25.90 8.20 4.80 8.20 2.00 0.30 4.30
- 1950 1 7.01 10.51 5.81 1.90 19.62 22.42 2.70 4.30 0.70 1.90 2.50 5.50 0.80 7.01 8.41 0.50 0.00 0.00 1.10 0.00 0.00 0.10 0.00 0.00 0.00 0.20 0.00 0.40 4.80 2.60 0.80
- 1950 2 11.90 6.60 4.50 2.40 0.00 0.10 7.50 6.30 6.70 14.50 7.20 1.50 2.90 19.90 19.20 5.20 5.30 3.70 2.70 1.50 0.70 2.00 4.00 0.80 0.00 0.00 1.60 0.70 -99.99 -99.99 -99.99
- 1950 3 11.69 4.90 1.80 1.60 0.00 0.00 0.00 1.60 0.20 0.00 0.30 0.30 0.00 2.70 5.89 11.09 7.89 18.68 3.50 9.39 1.10 12.19 1.10 0.10 0.00 0.00 0.00 0.00 0.20 0.10 6.59
- 1950 4 14.08 0.60 0.60 5.69 1.00 1.80 19.37 13.68 11.98 7.29 2.60 1.70 2.10 0.00 0.20 3.49 4.09 1.00 0.00 4.09 1.40 2.10 3.79 0.30 1.70 0.10 1.60 0.40 2.00 9.18 -99.99
- 1950 5 13.03 6.81 0.10 0.20 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 3.61 2.91 1.50 1.70 7.31 1.90 0.10 0.00 0.00 2.10 4.11 3.01 1.00 0.50 0.10
- 1950 6 0.40 0.10 0.80 0.10 0.00 0.00 0.20 0.10 1.80 0.00 0.00 0.00 2.60 2.50 2.30 4.51 7.01 0.70 0.00 7.41 0.30 0.10 0.00 3.30 7.01 1.80 4.31 23.03 4.01 2.00 -99.99
- 1950 7 0.30 0.20 0.00 0.00 0.00 0.10 6.70 5.90 2.50 9.80 1.20 2.00 29.30 5.40 9.60 4.00 6.60 9.60 14.70 5.80 1.80 3.00 0.30 1.80 3.40 3.30 2.00 6.10 2.30 10.70 2.20
- 1950 8 1.30 2.10 0.30 4.30 8.51 0.10 0.30 14.41 2.20 8.21 6.20 2.40 1.50 9.71 10.81 4.30 13.61 8.61 2.70 3.40 0.00 6.70 4.70 11.31 9.81 12.51 1.20 3.30 0.40 2.60 0.00
- 1950 9 6.40 1.80 6.00 8.20 1.20 35.19 8.00 0.30 2.40 17.59 19.49 6.80 7.40 3.70 0.90 24.99 26.49 1.70 11.10 7.40 7.20 5.20 13.00 10.40 8.60 4.20 8.70 3.40 2.90 18.29 -99.99
- 1950 10 7.19 2.80 6.69 4.59 9.99 4.59 4.49 12.18 11.38 9.49 3.99 4.59 7.99 4.49 2.30 20.17 1.60 2.50 0.60 0.00 6.99 1.20 0.00 0.00 0.00 6.39 1.00 0.10 1.50 6.69 2.10
- 1950 11 8.90 1.00 0.70 0.10 0.40 0.00 15.60 14.70 2.70 0.40 9.40 8.70 3.50 1.80 3.80 2.10 2.80 8.60 0.90 0.80 4.80 0.50 0.00 0.00 0.00 0.00 2.30 4.20 5.00 12.10 -99.99
- 1950 12 7.67 0.70 1.30 0.20 1.00 5.18 0.70 1.79 14.45 2.29 1.10 0.20 0.30 1.30 0.00 0.20 3.39 2.49 5.28 2.99 1.00 2.39 0.80 0.00 0.00 0.10 0.00 0.10 0.00 0.30 0.10
- 1951 1 2.69 0.20 2.79 2.40 0.30 2.30 5.39 0.50 15.67 13.97 9.28 4.79 8.08 8.38 0.80 26.35 13.67 1.40 2.69 2.99 7.09 0.40 0.00 0.00 5.09 0.50 2.89 0.30 3.19 6.49 3.49
- 1951 2 5.70 15.40 3.30 7.90 10.90 2.20 1.70 5.20 0.10 0.40 1.70 2.50 0.20 0.50 0.00 6.80 5.20 3.00 8.90 2.60 1.50 0.80 1.70 0.10 3.40 0.60 0.20 0.20 -99.99 -99.99 -99.99
- 1951 3 1.50 0.60 0.90 5.50 1.10 3.30 4.30 0.00 1.00 0.20 0.40 0.10 3.70 0.30 0.00 0.20 3.20 4.60 0.00 0.10 25.90 16.10 0.70 0.90 5.30 4.70 0.20 0.10 1.00 9.30 5.60
- 1951 4 2.11 0.50 17.99 4.72 0.40 5.73 3.12 0.10 0.30 0.00 20.00 2.41 3.92 4.62 20.40 2.61 0.20 2.71 0.00 0.00 0.00 0.10 0.20 0.40 0.90 0.40 0.00 0.00 0.90 3.32 -99.99
- 1951 5 18.36 5.19 0.10 0.20 0.20 0.00 1.10 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.90 0.00 0.00 0.00 3.49 7.09 0.30 0.50 0.40 9.48 0.90 0.50 1.30 0.00 0.00 0.00 0.00
- 1951 6 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.10 6.51 6.21 6.41 2.60 2.30 7.61 5.11 0.30 1.60 14.02 0.40 0.00 1.40 0.00 6.31 0.50 0.10 0.00 0.00 0.40 -99.99
- 1951 7 3.10 6.61 0.00 0.40 6.61 7.81 4.10 4.40 11.31 5.41 6.11 9.41 0.00 0.00 0.60 0.10 2.80 1.10 0.20 0.10 0.00 7.71 0.00 0.40 4.20 8.61 1.80 0.40 0.00 0.00 0.90
- 1951 8 0.40 18.61 0.60 0.90 3.40 7.81 6.60 5.80 0.00 0.60 7.51 0.50 0.20 0.00 0.40 10.91 0.90 12.11 1.50 4.80 11.81 1.20 4.20 1.60 12.01 6.60 1.20 12.51 6.70 0.40 1.40
- 1951 9 2.20 0.50 12.00 4.70 0.00 0.00 0.00 0.00 0.00 0.30 8.70 5.20 15.10 10.30 2.80 2.10 1.00 0.50 0.00 0.00 0.10 1.90 10.20 17.80 8.50 5.60 7.10 0.00 0.00 0.00 -99.99
- 1951 10 0.00 0.10 0.10 1.50 0.00 0.00 0.00 0.00 4.20 0.30 0.00 0.00 0.10 0.90 3.80 2.50 0.40 0.20 7.60 7.70 2.00 1.10 0.90 0.00 0.00 0.00 0.00 0.00 0.10 4.30 0.40
- 1951 11 3.60 2.10 10.21 18.51 11.11 7.50 0.00 3.70 4.30 11.61 3.50 0.80 2.90 8.90 8.00 15.51 9.50 6.40 15.51 5.50 2.90 1.20 16.31 4.40 0.80 2.90 11.61 6.00 1.80 5.90 -99.99
- 1951 12 6.01 10.31 13.21 5.10 5.20 4.20 19.82 12.51 2.60 0.10 0.30 0.60 4.30 5.30 4.70 0.40 10.91 2.10 26.42 0.00 15.81 1.00 18.02 5.30 5.30 4.50 16.61 1.80 14.21 5.91 5.20
- 1952 1 16.11 1.50 0.70 6.70 3.80 2.80 3.00 15.81 9.71 5.40 1.00 0.20 21.21 5.10 9.11 12.71 1.10 0.30 0.00 0.00 0.00 4.80 0.00 0.00 1.50 0.30 0.40 11.81 0.50 21.11 5.20
- 1952 2 4.79 5.39 0.50 0.30 7.28 3.09 0.60 0.10 2.20 3.49 0.20 0.30 2.99 0.00 0.40 0.70 0.10 0.00 0.10 4.29 0.60 0.00 0.30 0.00 0.00 0.00 0.00 3.89 4.79 -99.99 -99.99
- 1952 3 10.60 6.40 5.40 3.80 5.20 15.00 11.60 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 1.80 12.40 0.20 13.30 1.70 0.70 0.30 0.00 0.10 0.50 0.00 0.10 0.20 0.20
- 1952 4 1.10 0.20 0.00 7.39 8.59 2.30 1.20 0.70 5.49 0.20 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.40 3.59 14.18 8.69 2.90 1.80 0.00 0.40 0.00 1.50 0.10 0.00 3.29 -99.99
- 1952 5 0.40 0.00 3.12 0.91 0.00 8.36 0.10 12.89 1.21 5.64 11.79 0.30 0.40 0.00 0.00 0.00 0.00 0.20 1.01 0.20 0.00 0.00 0.00 0.00 0.00 0.10 0.91 0.10 0.10 0.00 7.15
- 1952 6 8.50 2.30 0.80 11.90 0.30 0.60 0.30 0.00 0.00 1.30 0.00 0.00 3.40 0.80 1.60 12.00 5.90 3.10 9.50 1.90 10.40 0.00 0.60 7.30 0.30 2.10 0.00 10.00 1.90 0.70 -99.99
- 1952 7 10.50 0.30 0.00 0.00 0.00 4.60 9.60 1.00 0.60 6.50 1.90 4.40 1.90 1.30 4.30 10.60 0.50 4.10 8.60 1.70 2.00 0.10 0.00 0.00 0.00 0.10 0.10 0.20 0.20 0.00 9.80
- 1952 8 2.90 10.58 5.89 16.48 0.10 5.99 25.96 3.99 16.38 1.10 4.89 5.69 1.70 0.70 0.10 4.59 0.30 0.00 0.00 0.20 0.00 0.30 0.10 0.90 0.00 18.17 2.70 1.10 0.10 2.80 3.69
- 1952 9 2.90 15.12 0.30 0.10 0.00 0.40 0.40 4.10 0.10 0.00 0.00 0.00 0.10 0.00 0.00 1.70 0.50 0.10 1.10 5.61 0.10 3.30 16.22 16.12 13.32 1.20 0.80 0.10 0.30 0.30 -99.99
- 1952 10 0.80 0.20 0.70 0.50 3.50 1.00 1.60 3.40 0.60 0.00 0.00 10.59 6.59 0.00 0.00 0.10 0.00 1.30 9.79 0.10 0.30 13.29 14.29 6.40 6.30 8.89 16.39 7.79 6.30 3.10 4.80
- 1952 11 3.20 4.61 2.50 15.92 7.51 7.91 0.10 4.51 0.60 0.50 0.00 0.50 4.51 4.71 0.20 0.30 0.00 0.10 0.10 5.31 2.10 2.80 0.10 0.10 0.00 0.40 0.00 0.00 0.00 0.00 -99.99
- 1952 12 0.30 0.10 0.00 0.00 0.00 1.40 1.40 21.48 3.40 9.89 4.10 1.20 0.20 0.90 4.70 16.69 1.20 8.59 8.59 9.29 7.29 10.19 6.00 5.70 5.10 0.80 1.40 1.20 2.20 2.50 2.80
- 1953 1 0.00 0.00 0.00 4.09 1.80 0.10 0.30 2.80 0.80 1.90 1.00 3.70 0.20 4.69 0.60 0.70 0.80 0.00 0.00 0.00 0.00 1.80 3.00 1.70 0.70 15.88 6.69 1.80 1.90 17.68 0.60
- 1953 2 0.00 0.00 0.00 0.30 0.20 0.20 0.40 8.33 1.71 0.70 0.30 0.40 6.52 0.10 0.30 4.52 0.90 1.71 5.12 4.72 1.91 3.71 2.71 10.14 1.51 1.51 0.00 0.00 -99.99 -99.99 -99.99
- 1953 3 0.00 0.00 0.00 0.00 0.00 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.14 0.20 3.72 6.94 5.33 6.84 7.74
- 1953 4 7.90 2.30 1.50 4.00 2.40 0.90 1.40 1.80 0.00 2.00 10.90 2.60 1.70 1.00 6.10 2.90 0.20 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.10 0.60 7.30 6.30 1.70 1.80 -99.99
- 1953 5 0.20 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.01 4.31 8.81 1.80 2.80 14.22 4.21 1.60 5.21 1.30 4.01 12.02 4.81 0.40 1.80 1.30 0.10 0.00
- 1953 6 0.80 0.10 2.10 0.10 0.00 0.00 0.00 0.00 1.70 0.10 0.00 0.10 0.50 10.20 4.30 2.60 1.70 3.30 0.80 4.40 3.00 0.00 0.00 0.20 3.90 19.80 0.00 0.00 0.10 0.00 -99.99
- 1953 7 0.10 2.80 1.30 0.80 14.62 6.41 5.31 4.81 2.30 1.00 15.42 2.40 11.91 3.10 0.10 11.11 7.91 2.80 3.90 12.01 3.90 7.91 3.10 15.62 4.71 11.11 3.00 2.50 1.40 0.10 0.10
- 1953 8 0.30 0.20 2.90 1.80 0.20 0.00 0.00 0.00 0.40 0.00 0.20 3.40 0.00 9.31 1.30 8.01 11.81 1.70 1.10 8.21 5.51 0.40 3.50 5.61 2.20 0.50 7.81 1.40 5.81 12.61 13.41
- 1953 9 13.00 11.00 0.40 0.30 0.40 0.00 0.00 0.50 0.40 0.40 0.30 0.00 0.00 1.40 7.00 0.80 15.10 1.20 14.00 10.90 13.70 7.50 0.10 0.30 2.70 5.00 4.70 4.80 13.40 20.80 -99.99
- 1953 10 7.89 2.60 0.00 0.00 0.00 0.00 0.00 0.30 0.00 0.50 0.40 2.30 1.20 0.10 0.80 5.49 0.20 0.00 0.00 5.59 0.30 2.20 6.89 10.49 0.30 14.49 4.50 0.10 12.09 3.60 19.18
- 1953 11 11.40 6.20 10.90 1.70 3.60 19.90 8.80 7.60 2.50 10.10 17.80 17.80 4.10 32.60 4.10 0.20 0.00 2.60 4.10 0.00 0.00 0.00 6.50 5.60 7.80 11.30 8.10 3.80 3.40 2.00 -99.99
- 1953 12 6.00 19.70 29.20 0.00 0.20 0.00 0.00 1.30 8.50 1.10 1.20 4.90 5.20 2.50 0.10 0.00 0.30 6.70 0.80 9.50 5.70 3.00 11.30 9.70 1.50 8.10 2.10 0.20 1.40 0.50 0.40
- 1954 1 0.40 0.30 0.00 0.00 2.30 0.30 0.80 2.20 0.20 0.00 0.90 11.09 6.39 8.49 17.08 7.99 2.40 25.37 6.69 20.67 2.10 9.59 1.90 5.29 16.58 0.40 0.00 0.50 0.70 0.50 0.20
- 1954 2 0.80 0.10 0.10 1.10 0.50 13.88 0.70 0.10 2.80 7.29 1.30 13.28 8.98 0.90 0.30 7.29 0.40 1.70 3.69 6.09 2.90 17.67 5.49 14.08 5.39 0.60 0.00 0.00 -99.99 -99.99 -99.99
- 1954 3 0.00 8.92 5.51 0.20 3.81 18.54 9.32 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.90 1.90 4.11 9.42 7.92 2.00 1.50 1.60 0.10 4.71 0.70 17.43 3.61 0.70
- 1954 4 4.39 16.67 9.18 3.39 0.50 0.00 2.79 0.10 0.00 0.40 1.40 1.40 3.29 0.50 0.00 0.10 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.50 6.59 -99.99
- 1954 5 1.00 9.13 3.41 2.51 20.86 1.50 2.01 0.10 0.00 0.10 1.20 0.40 10.03 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.01 3.71 15.14 2.11 3.11 9.33 16.35 0.60 0.00 0.00
- 1954 6 0.00 0.00 0.00 0.00 1.20 4.10 0.70 5.60 1.40 0.90 0.10 0.00 0.10 13.10 12.10 4.00 3.90 4.30 8.30 10.10 3.50 9.30 4.50 6.10 9.20 1.40 0.50 0.10 3.40 1.50 -99.99
- 1954 7 0.70 1.90 5.99 3.10 0.30 1.00 1.00 0.50 4.40 0.50 0.00 0.00 4.00 2.10 0.10 6.79 3.10 0.40 10.49 1.00 1.70 4.30 21.48 0.50 0.00 17.58 14.79 3.10 0.60 0.60 0.60
- 1954 8 7.41 0.60 0.20 7.81 12.91 5.20 0.50 2.40 5.30 1.40 0.00 8.11 3.50 3.40 3.80 0.00 17.61 0.90 0.00 0.10 6.20 2.00 1.20 0.00 0.00 1.20 1.10 13.81 3.20 14.21 5.50
- 1954 9 3.40 2.10 5.30 0.00 4.20 3.00 5.70 8.40 14.51 6.60 3.80 1.90 4.80 13.71 19.71 11.61 4.80 0.50 17.01 7.50 1.90 0.00 17.61 10.41 4.60 1.30 0.70 3.70 12.81 7.70 -99.99
- 1954 10 3.50 0.40 10.81 14.31 1.60 0.00 11.11 0.30 4.70 5.00 1.60 3.80 9.11 2.40 21.62 17.31 33.23 26.62 2.70 1.40 4.00 9.21 10.01 1.70 1.40 15.51 8.91 19.12 7.41 0.40 0.10
- 1954 11 0.00 4.00 4.40 2.00 0.30 0.00 10.40 12.20 12.30 21.90 9.10 11.10 4.70 0.10 2.20 0.90 2.40 3.00 1.40 5.30 7.20 18.70 11.40 19.30 4.40 13.10 24.00 3.70 9.60 8.60 -99.99
- 1954 12 18.41 8.60 8.50 6.40 1.00 0.50 1.70 6.80 9.00 2.00 7.60 2.70 2.50 15.41 1.80 7.20 6.00 8.40 8.80 3.30 6.30 7.70 0.20 5.40 11.81 8.20 4.90 7.70 1.50 0.00 0.00
- 1955 1 0.00 0.00 0.30 0.50 0.10 0.00 0.00 0.10 27.21 3.29 2.29 1.00 0.20 1.00 3.79 1.30 2.19 2.59 0.00 2.99 7.08 0.00 0.70 3.39 6.18 0.60 10.37 8.67 5.48 2.99 1.00
- 1955 2 9.20 5.00 0.10 0.00 0.20 7.30 10.60 1.30 0.00 0.40 1.80 0.90 1.80 1.80 0.30 0.90 3.40 0.90 0.50 0.10 0.30 0.30 0.90 0.50 0.00 0.00 2.30 21.20 -99.99 -99.99 -99.99
- 1955 3 9.70 0.00 1.10 0.10 0.20 0.20 0.60 0.30 0.00 0.00 0.00 0.00 0.00 0.20 0.10 0.10 0.30 0.60 0.00 17.60 3.90 2.50 9.40 7.60 0.20 0.00 0.00 0.00 0.00 0.00 0.00
- 1955 4 0.00 6.69 2.50 0.20 7.29 2.00 10.69 4.99 9.89 0.40 1.10 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.30 3.20 0.20 0.00 6.49 8.79 15.58 0.10 1.70 3.00 -99.99
- 1955 5 6.70 0.10 17.60 19.00 3.40 1.10 7.30 8.00 4.30 1.10 3.30 7.40 1.30 1.70 2.40 0.20 1.30 0.70 0.00 0.90 0.60 7.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1955 6 0.00 3.60 1.30 0.50 0.80 0.00 2.20 0.00 0.00 0.40 3.10 0.10 16.80 1.50 0.40 0.10 0.00 0.00 0.80 0.30 1.60 0.70 7.30 0.70 1.50 2.40 8.40 16.90 0.30 6.20 -99.99
- 1955 7 10.63 10.43 14.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.40 0.10 0.00 0.00 0.10 0.10 0.00 0.00 0.00 0.20 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10
- 1955 8 0.00 0.10 0.00 0.00 0.30 0.10 0.00 6.92 0.80 0.00 0.00 0.10 0.00 0.00 0.90 7.32 3.11 8.32 2.01 0.40 2.61 0.10 0.00 0.00 0.00 0.00 0.50 0.40 2.01 0.60 1.40
- 1955 9 15.21 2.50 4.20 12.61 0.70 0.00 0.00 8.41 5.30 13.01 1.50 9.71 2.00 1.90 0.20 2.70 9.21 0.30 0.00 6.30 3.60 2.90 0.30 10.11 8.11 2.20 0.70 1.00 3.20 0.60 -99.99
- 1955 10 2.90 6.39 1.40 2.90 12.99 0.20 7.49 2.60 0.20 0.00 1.20 0.00 6.49 5.10 0.90 2.80 0.40 20.58 7.99 0.90 0.10 0.20 0.30 0.40 15.19 0.20 0.90 0.30 1.00 2.50 3.20
- 1955 11 0.70 5.00 7.00 0.10 2.20 6.30 17.20 3.30 1.20 2.40 7.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.90 0.10 0.40 0.00 1.50 0.70 4.50 1.80 0.70 1.20 -99.99
- 1955 12 1.40 11.61 2.40 0.80 3.80 14.51 0.30 6.40 25.61 1.60 0.00 0.20 7.10 18.71 4.30 1.10 0.10 0.00 0.00 0.20 3.60 13.71 15.01 1.30 16.11 7.80 27.91 9.20 1.80 2.10 8.00
- 1956 1 1.00 0.40 0.20 0.30 0.70 1.20 2.00 0.00 0.60 6.39 1.10 1.60 0.00 3.89 5.19 2.40 4.39 1.40 9.68 6.69 7.29 1.10 1.10 0.30 1.30 12.28 7.58 13.87 2.20 2.89 0.20
- 1956 2 0.40 0.00 6.21 7.72 0.40 0.40 0.10 0.60 0.10 0.40 0.40 0.50 0.50 0.00 1.60 0.10 0.10 0.20 1.80 0.80 1.20 0.10 0.00 0.00 0.00 0.00 6.82 4.91 6.52 -99.99 -99.99
- 1956 3 25.16 4.49 7.79 2.20 7.69 3.89 0.20 2.70 0.00 0.00 0.00 1.40 0.00 0.00 0.20 0.10 0.50 0.00 0.00 3.00 0.00 5.19 0.00 5.09 0.20 0.40 0.30 0.00 0.00 0.00 0.00
- 1956 4 0.00 1.00 0.50 3.09 0.00 0.40 2.79 7.38 5.49 1.60 0.00 0.00 0.00 2.29 1.00 0.50 0.00 0.00 0.00 0.00 1.00 0.40 0.60 1.80 2.59 1.10 0.00 0.30 0.20 4.29 -99.99
- 1956 5 1.50 2.40 1.40 0.50 0.50 7.29 8.39 13.68 15.38 2.80 2.50 0.00 2.90 0.50 2.40 1.10 0.80 0.10 0.00 4.99 0.10 0.00 7.89 0.10 0.30 0.20 0.00 0.00 1.10 0.10 0.90
- 1956 6 1.00 1.20 12.41 13.12 8.91 9.91 1.20 0.00 0.00 0.00 0.80 5.51 1.20 3.50 0.10 2.90 5.81 3.80 0.50 0.20 0.20 0.10 0.30 0.00 0.60 0.00 3.90 1.10 2.80 4.00 -99.99
- 1956 7 7.80 3.80 4.40 19.90 0.80 5.20 5.70 1.90 0.00 0.60 0.00 0.00 8.30 3.60 0.00 0.80 5.00 3.40 0.00 0.00 0.10 0.30 18.60 1.10 2.50 0.20 0.80 12.90 23.10 2.00 1.50
- 1956 8 11.79 5.30 3.20 1.00 0.90 2.80 3.50 0.00 0.00 17.69 3.70 27.78 7.40 0.50 5.00 18.09 9.79 4.60 1.70 1.60 0.10 0.70 7.80 9.69 2.00 0.30 11.39 7.30 0.70 0.20 0.30
- 1956 9 0.00 5.80 14.30 0.60 11.90 3.50 1.00 0.00 0.00 7.60 1.00 9.90 0.70 0.00 2.40 0.00 0.90 0.40 0.30 1.20 1.70 15.20 7.00 0.00 0.60 2.80 22.40 6.20 5.60 3.00 -99.99
- 1956 10 1.90 5.69 9.79 3.50 0.10 0.80 1.30 0.40 0.00 0.00 0.90 0.40 0.00 0.00 0.80 17.78 1.30 0.60 17.58 0.00 0.70 13.49 8.49 10.69 2.70 0.20 6.49 1.10 0.10 0.20 0.00
- 1956 11 0.00 0.00 0.10 0.20 0.10 0.00 1.60 5.71 4.71 2.20 0.40 0.60 3.51 0.80 0.10 1.10 0.00 0.30 0.10 0.00 1.80 3.01 0.00 8.82 3.91 8.31 7.61 0.60 0.20 2.70 -99.99
- 1956 12 3.50 2.60 3.20 24.30 4.10 1.60 0.80 3.60 5.50 24.80 13.20 10.30 16.80 9.80 10.30 1.70 1.40 0.10 3.00 0.70 2.10 4.60 2.60 1.50 2.00 1.70 8.40 5.60 2.80 9.00 1.10
- 1957 1 1.20 8.70 15.00 13.50 11.20 1.20 0.70 1.30 3.30 0.10 4.60 0.10 0.10 0.30 0.00 0.10 0.00 0.00 4.90 13.80 15.00 17.60 10.10 0.70 28.60 6.40 3.80 11.10 2.80 6.90 6.70
- 1957 2 3.60 0.30 6.81 8.11 7.51 3.50 11.01 4.60 0.20 5.61 2.50 2.40 2.80 0.00 4.60 0.50 0.00 0.00 0.00 1.30 0.00 0.00 28.83 3.10 0.50 0.00 0.00 0.00 -99.99 -99.99 -99.99
- 1957 3 6.99 1.10 1.30 2.60 1.70 3.10 2.30 6.69 3.40 2.90 0.70 0.00 3.80 3.50 18.59 6.20 3.10 8.49 17.29 8.19 1.00 1.10 4.70 1.60 6.39 1.10 0.10 0.10 9.29 0.20 0.10
- 1957 4 0.80 2.50 10.40 2.40 0.00 0.00 0.00 0.00 0.00 0.10 0.20 0.70 0.70 0.90 2.70 10.40 6.20 2.50 1.80 11.50 7.90 0.00 0.20 0.00 0.00 0.20 0.20 2.10 0.00 0.30 -99.99
- 1957 5 0.10 0.00 0.00 0.30 0.00 0.50 4.50 9.60 0.20 1.90 3.30 5.40 2.20 4.00 8.50 5.40 2.50 5.00 5.10 1.00 0.10 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1957 6 1.71 1.21 3.32 3.52 1.51 0.60 0.60 0.00 1.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.30 0.10 0.20 0.10 14.48 16.18 11.06 0.80 0.00 -99.99
- 1957 7 0.00 0.30 0.10 0.00 0.00 19.49 6.60 0.30 0.00 1.80 13.79 6.99 3.10 0.70 0.20 3.80 10.79 5.90 7.99 6.40 0.00 0.60 1.60 10.39 19.69 11.39 0.30 0.10 0.00 0.80 0.50
- 1957 8 0.00 0.00 0.00 2.30 1.20 0.00 0.50 5.70 3.50 10.71 3.10 0.90 11.11 10.01 1.60 1.10 0.80 6.50 2.80 0.90 1.70 1.10 16.71 37.03 4.40 2.80 1.00 0.10 0.20 2.10 3.20
- 1957 9 0.00 0.20 4.70 2.90 3.10 12.20 6.00 5.90 6.80 2.70 9.50 5.10 0.30 0.70 1.20 7.60 6.80 3.20 0.00 1.50 5.90 8.90 0.20 0.00 0.00 0.00 1.20 0.30 0.20 0.00 -99.99
- 1957 10 0.30 1.20 0.00 6.51 0.00 0.10 0.30 0.40 0.10 0.50 0.20 3.60 1.70 0.40 14.32 0.30 9.81 15.62 6.61 12.52 6.41 8.31 1.20 11.32 18.93 0.20 9.91 2.60 0.50 7.91 8.21
- 1957 11 8.00 7.20 5.10 6.30 2.70 0.50 0.40 1.00 0.00 1.10 1.90 0.30 0.00 0.10 0.00 0.00 3.20 0.80 2.00 2.00 7.60 0.10 0.20 0.90 0.10 2.00 3.00 1.60 0.00 0.00 -99.99
- 1957 12 0.00 0.00 1.50 0.40 6.60 16.11 21.11 2.50 1.30 23.02 3.90 0.80 0.00 0.20 1.70 6.10 3.50 2.90 12.01 9.51 17.81 7.40 0.10 1.00 1.10 1.50 1.50 2.10 1.50 1.70 0.00
- 1958 1 0.00 0.90 1.70 12.31 7.10 11.81 3.00 29.02 10.91 11.31 1.40 0.40 2.00 1.70 1.20 1.60 5.00 3.50 3.10 3.70 0.90 0.00 0.10 8.11 21.71 2.80 3.70 5.30 0.20 0.10 1.20
- 1958 2 3.20 0.10 3.90 14.59 0.10 0.00 3.40 6.79 3.20 8.49 6.79 0.30 7.19 4.30 2.30 0.40 0.10 0.40 1.20 5.79 2.30 14.09 5.29 2.50 0.10 1.80 1.00 3.90 -99.99 -99.99 -99.99
- 1958 3 0.10 0.00 4.41 4.11 1.60 0.40 0.30 0.40 0.10 0.10 0.20 3.31 2.21 0.00 0.00 0.10 0.00 0.00 0.20 0.10 0.00 0.00 0.00 0.80 0.30 3.11 2.11 2.41 8.52 3.21 0.10
- 1958 4 0.00 0.40 2.80 2.70 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.10 5.80 4.10 4.40 2.30 0.50 0.00 4.00 5.70 2.70 8.20 3.00 1.90 0.20 0.00 0.00 -99.99
- 1958 5 0.00 0.00 0.00 0.00 7.42 0.20 9.83 5.12 0.00 0.00 0.90 0.50 0.30 1.20 1.30 0.20 3.31 14.25 10.23 7.12 1.10 3.31 8.33 6.42 0.60 1.81 0.90 0.20 2.21 4.92 1.20
- 1958 6 2.00 7.71 0.40 0.00 0.00 0.00 2.60 0.10 5.61 5.21 12.11 0.00 0.00 0.00 3.10 1.00 0.10 6.01 5.61 0.20 0.50 0.40 1.80 4.10 11.61 2.40 8.21 0.00 1.20 9.01 -99.99
- 1958 7 1.70 0.00 0.10 0.80 0.00 0.10 0.50 1.00 1.20 0.20 7.69 13.98 8.49 1.70 6.99 4.19 1.30 1.60 10.39 0.10 5.69 0.20 0.10 0.00 6.69 9.29 12.68 30.86 8.69 6.39 3.20
- 1958 8 9.51 2.60 8.31 8.01 0.70 1.30 0.30 0.10 7.61 18.12 2.30 1.00 10.81 3.81 12.02 0.00 1.50 10.91 2.30 7.21 7.81 11.11 0.30 0.00 0.90 9.01 6.81 0.00 0.20 3.10 0.70
- 1958 9 0.40 0.00 0.00 0.60 0.90 21.18 5.79 1.10 0.00 0.00 0.00 0.00 0.50 0.60 0.10 1.70 0.00 10.29 1.70 5.10 2.80 0.60 19.58 13.99 0.00 0.00 0.80 15.09 4.90 3.20 -99.99
- 1958 10 1.90 3.40 15.59 14.69 2.10 15.19 3.70 3.20 6.89 12.59 1.90 13.79 0.50 4.10 5.70 1.30 0.40 1.50 2.30 0.90 0.50 0.50 0.30 0.00 0.00 0.00 0.00 0.00 3.70 3.70 0.90
- 1958 11 19.40 0.90 0.40 8.85 1.71 0.60 4.62 1.21 1.31 0.30 6.03 6.23 0.80 1.21 1.71 0.40 0.00 0.00 1.01 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 -99.99
- 1958 12 0.80 0.10 0.30 0.10 0.00 0.20 7.51 5.01 0.40 8.91 8.31 19.92 0.30 5.31 1.20 0.40 4.11 9.81 9.91 4.91 6.61 3.30 0.10 1.90 9.61 7.51 7.51 9.51 11.21 5.61 10.11
- 1959 1 16.73 0.10 0.40 0.30 0.00 0.00 0.60 1.00 0.20 0.60 0.50 0.00 0.00 0.00 0.00 2.20 3.21 16.13 9.02 0.80 2.10 1.10 0.10 0.10 0.00 0.00 0.00 0.30 1.20 0.30 0.00
- 1959 2 0.00 0.00 0.00 0.10 0.10 0.00 0.10 0.00 1.60 0.10 0.10 0.00 4.39 6.99 1.90 0.10 0.00 0.10 1.40 2.50 3.39 1.40 5.89 5.79 2.50 4.79 8.58 1.60 -99.99 -99.99 -99.99
- 1959 3 0.30 5.80 1.50 2.50 7.10 1.30 0.00 0.00 0.10 3.20 8.20 0.60 6.70 14.70 0.10 0.00 0.00 0.00 0.00 0.00 0.00 5.10 0.30 4.50 2.50 4.40 3.00 0.90 2.90 1.40 7.40
- 1959 4 4.70 2.60 0.40 1.20 4.90 2.10 8.01 1.30 1.10 3.10 7.91 3.60 6.31 3.00 1.80 0.40 2.40 2.10 1.40 0.20 0.00 0.00 0.00 5.00 20.02 2.70 7.31 1.60 0.10 6.01 -99.99
- 1959 5 3.28 3.28 0.20 0.10 0.40 0.00 6.55 0.00 1.39 4.17 8.04 5.46 0.10 0.00 0.10 0.20 1.09 2.18 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 5.16
- 1959 6 1.90 0.00 2.79 4.09 2.89 6.39 15.96 10.87 1.80 0.30 0.50 0.10 0.00 0.00 0.00 0.50 1.40 0.20 0.00 0.00 1.80 1.50 0.20 3.59 8.18 6.78 3.39 2.29 3.69 4.39 -99.99
- 1959 7 8.11 18.83 4.81 0.00 1.30 3.00 0.50 0.00 0.00 0.00 26.34 6.71 0.10 0.00 1.70 13.82 6.81 4.01 3.30 0.20 0.00 0.00 0.00 0.00 0.60 22.33 18.73 8.01 0.20 0.00 0.20
- 1959 8 0.70 0.70 0.10 0.20 0.40 1.11 0.30 0.00 1.41 0.00 0.00 0.00 7.04 2.01 2.92 0.10 2.01 0.00 0.00 0.50 0.91 2.41 6.94 3.22 0.50 0.00 0.70 0.00 0.00 0.00 0.00
- 1959 9 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.71 5.87 1.09 6.37 2.79 4.48 0.10 0.40 0.00 0.00 0.10 -99.99
- 1959 10 0.00 0.10 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.00 1.90 5.80 0.10 0.00 0.00 7.50 33.52 14.91 5.20 18.41 6.30 1.50 6.00 9.21 11.51 33.92 1.80 0.70 2.60 0.10 0.90
- 1959 11 2.00 4.20 0.70 0.70 2.20 4.40 0.10 18.81 9.81 6.30 0.60 2.80 10.01 6.90 1.20 4.50 2.90 4.60 14.21 6.90 2.60 21.71 9.91 4.90 6.10 2.80 3.50 12.81 4.30 2.70 -99.99
- 1959 12 6.11 6.11 0.90 0.10 7.91 7.01 4.41 9.12 3.31 0.30 2.60 2.81 14.33 0.50 1.10 13.93 8.72 4.21 7.21 10.12 6.91 13.83 8.22 6.71 12.42 11.32 4.01 0.80 18.63 4.21 16.73
- 1960 1 0.10 0.50 3.59 4.29 0.40 0.00 0.00 0.20 0.00 0.20 0.10 1.70 0.60 0.40 0.20 0.10 5.19 16.18 0.80 10.18 13.48 19.57 1.90 0.60 0.30 0.10 0.60 3.79 2.80 32.75 8.79
- 1960 2 9.80 15.70 16.90 4.50 0.40 0.00 0.10 0.00 0.20 0.80 0.20 0.90 2.50 0.90 1.10 0.60 6.40 4.80 4.40 3.90 4.80 2.90 0.80 9.50 3.20 14.30 15.30 0.90 17.10 -99.99 -99.99
- 1960 3 3.10 10.08 8.49 0.70 0.00 0.00 0.00 0.00 1.20 2.30 0.70 0.90 5.29 3.59 2.40 0.10 0.00 4.39 10.38 6.79 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.00 1.60 0.10
- 1960 4 6.41 10.71 8.41 10.21 10.01 1.60 3.90 15.01 8.01 2.80 7.31 21.82 9.21 4.40 0.30 0.00 0.00 0.20 0.90 0.10 0.10 0.00 0.00 0.20 0.00 0.20 0.00 0.10 0.00 0.90 -99.99
- 1960 5 0.00 0.10 1.50 4.70 0.20 0.30 0.30 3.00 0.20 0.00 0.00 11.20 15.40 0.70 0.80 0.00 0.00 0.00 0.00 0.00 0.10 1.00 15.60 0.00 2.90 3.70 0.50 0.00 0.00 0.00 1.30
- 1960 6 2.89 0.50 0.00 0.10 7.88 6.88 9.87 3.49 0.10 3.59 9.77 8.17 0.30 1.79 6.88 0.80 0.10 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 -99.99
- 1960 7 0.10 0.30 2.10 5.40 14.01 4.90 1.50 0.60 0.20 9.31 6.31 3.20 4.30 4.70 1.90 12.71 3.90 5.30 0.70 2.60 3.90 1.20 3.50 1.10 6.81 0.70 4.10 2.00 3.90 0.20 0.90
- 1960 8 0.80 7.19 1.80 0.40 0.00 0.00 0.40 6.69 7.39 0.40 0.20 1.20 3.10 0.40 2.10 1.00 9.29 2.10 0.90 5.39 6.59 10.99 0.80 13.79 13.69 5.00 3.70 1.10 0.50 0.10 0.10
- 1960 9 0.10 8.31 4.61 0.50 3.50 8.31 0.00 7.71 0.00 2.80 3.00 1.00 18.72 10.41 0.50 6.61 1.00 2.90 1.40 0.30 1.60 0.50 0.10 0.70 0.00 0.00 0.00 0.40 0.20 0.20 -99.99
- 1960 10 3.09 23.55 7.09 0.60 7.48 0.30 1.30 0.50 0.00 0.00 0.30 0.40 0.10 0.10 0.00 0.10 6.69 6.09 12.87 1.50 1.40 2.59 1.40 0.30 1.00 1.30 1.00 1.30 0.30 7.19 7.78
- 1960 11 11.40 12.40 14.50 3.50 0.70 0.00 0.10 3.10 15.30 15.30 8.20 6.60 3.20 10.50 11.10 3.80 0.00 1.00 2.40 8.60 2.30 5.60 3.30 3.50 0.10 0.00 6.00 1.20 12.80 23.70 -99.99
- 1960 12 4.30 19.31 19.41 17.61 2.80 0.90 3.80 5.70 0.10 0.80 3.30 0.10 0.50 0.10 4.40 3.30 3.90 2.00 0.90 0.10 1.00 7.50 1.10 4.70 35.92 10.71 8.10 2.40 6.20 4.20 4.10
- 1961 1 9.08 1.70 2.00 1.20 5.49 0.10 8.09 7.39 1.20 0.00 21.86 6.59 0.70 0.20 0.00 0.20 0.70 6.69 0.20 0.00 0.10 0.10 0.00 0.00 0.00 11.98 7.39 12.18 9.18 5.59 1.20
- 1961 2 1.80 0.10 8.40 12.30 18.90 8.60 9.80 11.30 8.00 2.40 9.10 8.80 5.20 3.50 0.50 0.10 4.40 0.70 0.00 0.20 0.00 0.00 0.00 3.10 4.90 10.10 3.80 1.60 -99.99 -99.99 -99.99
- 1961 3 10.57 0.20 0.10 0.60 0.00 1.00 0.10 0.10 0.40 0.60 12.67 4.69 2.49 0.30 0.00 0.00 3.59 0.60 1.10 0.00 0.30 0.50 3.99 0.40 4.99 0.50 0.20 13.07 15.46 0.90 1.60
- 1961 4 2.60 0.20 0.10 4.39 17.87 0.30 0.10 4.59 5.09 1.30 9.68 11.98 2.20 0.90 0.00 0.50 0.10 0.80 12.98 14.27 2.40 4.99 2.10 0.40 6.29 2.89 1.50 0.20 2.10 0.80 -99.99
- 1961 5 7.39 3.69 8.58 1.70 3.99 6.49 13.28 3.09 0.10 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.20 1.30 0.00 0.20 2.20 2.89 0.10
- 1961 6 0.00 1.59 0.40 2.29 10.25 0.90 2.29 1.99 0.60 2.19 0.10 0.10 0.00 1.39 2.49 3.98 7.17 2.59 1.00 0.60 6.97 0.20 0.00 4.48 8.96 0.10 1.39 0.20 0.00 1.29 -99.99
- 1961 7 0.10 0.50 11.57 0.10 0.00 2.69 4.19 0.70 0.40 0.60 13.06 33.41 2.19 0.00 3.59 1.80 0.20 0.30 0.00 0.00 0.00 0.10 0.00 3.09 19.45 3.49 2.59 0.50 0.10 3.99 0.10
- 1961 8 0.70 0.70 34.38 1.10 6.60 1.50 7.10 44.67 5.30 2.10 0.20 6.00 0.80 0.80 2.90 0.60 5.20 6.00 1.80 9.49 7.70 0.40 0.90 8.99 10.79 6.00 3.70 0.20 0.10 0.00 0.00
- 1961 9 9.59 8.29 1.30 1.10 4.20 2.40 0.10 0.10 4.30 3.00 2.00 23.79 2.40 9.69 9.09 1.90 0.20 0.10 3.60 2.10 2.20 1.20 4.30 4.30 16.59 11.39 10.79 13.29 10.19 1.60 -99.99
- 1961 10 0.00 4.00 6.00 15.90 11.00 0.20 5.40 5.10 10.70 4.20 5.60 0.10 0.00 0.30 3.00 24.50 1.80 0.20 0.00 4.70 9.00 20.00 22.40 7.30 5.50 8.60 1.20 0.50 1.60 4.50 3.70
- 1961 11 10.39 4.80 0.30 2.00 11.19 3.70 18.39 1.20 1.50 0.40 0.20 0.60 0.20 0.00 0.40 0.20 0.10 0.10 1.80 0.00 1.50 10.89 11.19 9.39 9.69 1.00 0.40 11.79 12.99 2.60 -99.99
- 1961 12 3.10 1.10 0.90 16.40 3.50 1.80 0.70 10.10 8.30 22.80 7.10 6.80 9.20 0.10 1.10 0.10 0.00 0.00 0.00 0.10 0.00 0.20 0.00 0.00 0.10 0.10 2.60 5.70 0.60 0.50 1.50
- 1962 1 0.10 1.30 0.10 4.00 9.60 13.41 0.80 16.11 2.70 18.21 16.91 9.10 5.60 4.10 32.01 7.40 11.70 8.50 5.10 3.70 2.10 11.10 10.70 13.81 7.40 0.30 0.00 0.30 3.40 25.51 12.70
- 1962 2 1.00 2.81 4.92 9.33 11.74 8.43 1.81 3.41 2.51 3.51 39.24 3.11 0.10 3.81 8.23 1.51 1.20 0.90 0.20 0.00 0.00 0.00 0.00 0.40 0.80 2.31 0.70 0.10 -99.99 -99.99 -99.99
- 1962 3 0.60 0.50 0.00 0.10 0.00 0.00 0.10 2.20 1.90 1.00 0.10 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.10 0.10 0.00 0.00 0.10 5.60 15.00 0.20 0.10 9.60 8.40 1.90 5.70
- 1962 4 5.21 31.27 5.21 4.81 2.20 22.75 4.31 0.80 3.91 4.51 0.30 0.00 0.00 0.00 0.00 0.20 0.30 1.70 0.30 3.61 1.60 0.00 0.00 0.10 0.30 0.00 0.00 0.00 0.00 0.00 -99.99
- 1962 5 0.00 0.00 0.00 0.10 1.50 1.20 7.41 1.20 0.20 4.91 1.90 0.00 0.10 0.10 17.73 7.61 3.30 1.70 0.70 6.81 1.30 6.21 0.60 0.00 0.00 0.00 0.00 2.20 0.30 0.10 0.00
- 1962 6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.70 0.20 1.90 1.10 3.79 1.40 2.00 0.00 7.19 7.19 10.39 2.80 10.59 2.50 10.88 3.00 4.59 0.40 0.10 0.00 0.70 0.20 -99.99
- 1962 7 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12.41 1.90 0.00 0.00 0.00 0.00 0.00 0.00 16.42 3.80 3.00 23.32 1.40 0.00 17.62 0.10 0.00 0.00 0.10 10.51 2.20 2.50
- 1962 8 0.30 4.10 15.19 5.90 2.70 3.60 2.70 2.50 11.69 32.18 3.10 0.50 0.00 2.50 13.49 4.30 0.10 2.60 8.80 6.20 10.59 7.50 13.29 4.30 9.89 19.89 4.40 0.30 0.70 1.40 0.00
- 1962 9 6.10 9.80 9.10 8.00 2.30 0.70 3.50 17.61 34.62 2.30 24.81 0.30 0.80 9.80 1.40 2.00 0.00 0.00 0.00 0.00 0.00 4.20 0.50 0.90 2.40 9.50 9.10 3.90 21.11 12.41 -99.99
- 1962 10 6.61 0.10 1.00 6.61 0.00 0.00 0.00 0.00 0.00 0.20 0.30 0.00 0.60 0.10 0.00 0.40 0.30 1.30 0.00 0.00 0.00 0.00 1.50 4.91 1.60 1.80 14.12 0.50 14.92 11.82 7.31
- 1962 11 9.61 4.61 2.70 2.40 2.30 1.60 3.30 0.40 0.30 0.10 0.20 0.00 3.20 3.40 3.80 16.92 2.60 0.10 0.40 0.20 0.10 4.41 19.42 0.70 0.00 0.00 0.60 0.80 1.80 0.30 -99.99
- 1962 12 0.80 0.00 0.30 0.10 0.00 0.50 35.67 15.09 1.90 7.39 3.10 0.70 1.70 12.89 7.59 0.50 5.60 4.10 17.39 4.50 0.70 0.40 3.10 2.60 7.99 0.20 0.00 0.00 1.10 3.60 0.50
- 1963 1 1.41 0.10 1.82 3.03 0.40 0.10 0.10 0.30 0.00 0.30 0.10 0.00 0.50 0.10 2.62 0.10 0.10 0.50 0.10 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 8.37 1.11 1.11
- 1963 2 0.50 0.10 0.00 0.20 2.10 5.40 12.60 0.30 0.10 0.10 0.00 0.00 3.20 10.00 2.80 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -99.99 -99.99 -99.99
- 1963 3 0.00 0.00 0.00 6.71 6.51 12.71 4.30 16.02 7.01 3.30 0.00 1.10 18.72 11.91 4.30 7.01 14.32 4.20 0.00 0.20 0.30 0.00 0.80 29.73 2.30 5.91 3.30 9.31 4.10 0.20 1.40
- 1963 4 4.42 0.50 0.50 1.00 2.11 0.00 0.00 0.00 0.20 2.31 2.01 4.32 4.22 7.83 0.70 5.82 9.94 2.01 0.00 6.32 12.34 1.91 0.90 0.60 0.00 1.20 2.71 0.40 0.80 8.03 -99.99
- 1963 5 0.20 3.70 0.40 8.70 6.70 7.90 11.00 3.00 5.20 7.90 5.40 9.50 5.00 1.40 0.50 10.80 3.00 4.40 4.90 10.10 4.50 0.00 0.80 4.70 0.60 1.10 0.00 0.00 0.00 0.60 0.00
- 1963 6 0.00 0.00 0.00 0.10 0.00 2.31 6.51 0.60 0.00 0.00 0.60 5.41 0.50 0.00 4.11 0.10 9.92 7.52 2.91 11.32 1.80 1.00 9.62 6.21 2.81 8.52 6.61 0.80 0.40 1.00 -99.99
- 1963 7 0.40 3.90 8.79 3.00 5.79 1.80 0.00 1.60 0.40 3.00 2.60 0.80 2.90 8.09 6.09 1.50 1.10 3.30 3.80 0.00 3.20 0.70 19.28 0.80 1.10 0.00 0.00 0.00 0.00 0.00 0.00
- 1963 8 0.30 4.70 0.10 5.00 10.10 9.00 7.40 1.10 8.50 4.10 1.00 0.40 0.20 1.00 1.50 9.90 1.90 0.90 1.00 5.50 0.30 1.30 14.20 1.40 4.70 9.10 0.30 0.00 4.60 6.30 4.30
- 1963 9 4.80 3.40 1.20 0.70 0.90 3.00 11.69 8.79 2.60 0.10 0.20 1.00 0.20 0.10 0.70 0.10 1.50 0.00 0.10 0.10 0.00 0.00 7.79 4.80 26.98 9.29 1.70 9.49 1.30 6.19 -99.99
- 1963 10 3.89 14.98 3.00 7.79 2.10 3.89 6.59 11.58 19.37 0.20 0.00 7.79 1.30 1.50 7.19 1.00 2.10 1.70 14.38 1.40 16.18 3.60 1.90 0.10 0.00 0.00 0.00 4.79 2.10 0.20 8.09
- 1963 11 3.10 1.50 0.90 1.20 2.90 0.90 2.40 5.39 2.10 29.57 15.99 14.39 9.19 0.70 0.10 0.10 20.68 6.69 2.40 12.39 17.98 14.29 28.77 6.39 5.00 0.00 6.59 2.10 0.10 0.40 -99.99
- 1963 12 0.40 0.00 0.00 0.00 0.00 0.20 0.50 1.10 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.10 0.10 0.00 1.00 0.00 0.20 0.00 8.48 3.99 2.00 2.00 6.19 13.08 11.48 1.90
- 1964 1 0.29 0.10 8.87 4.09 0.49 1.36 0.19 0.00 0.00 0.19 0.10 0.49 1.36 1.17 0.00 0.00 0.10 3.90 1.07 0.19 0.00 0.10 2.44 0.49 0.39 0.10 17.05 4.00 10.72 9.26 7.89
- 1964 2 1.08 4.30 6.01 2.06 0.09 1.08 0.00 0.45 2.15 2.42 0.99 0.00 0.00 0.18 0.09 0.00 1.08 0.36 0.00 0.00 0.72 3.23 6.28 1.97 0.27 2.24 2.69 0.18 0.00 -99.99 -99.99
- 1964 3 0.00 0.00 1.18 0.00 0.63 0.09 0.00 0.09 0.54 0.81 0.00 3.62 2.44 11.57 1.27 0.00 0.63 0.00 13.29 7.32 1.18 0.09 8.50 4.79 0.00 0.00 0.45 0.00 0.09 0.45 0.36
- 1964 4 0.00 0.09 0.09 0.00 2.40 0.09 2.49 1.11 1.85 0.74 8.03 1.66 10.25 2.49 2.95 2.12 2.49 1.02 14.96 1.57 3.69 0.46 1.85 1.48 5.54 3.05 7.29 1.66 3.97 3.79 -99.99
- 1964 5 2.05 8.49 8.49 4.29 3.64 4.67 18.30 3.17 6.63 9.06 6.16 2.61 2.61 0.19 0.00 0.19 0.09 7.00 2.15 2.33 7.28 0.19 0.00 0.00 0.00 0.00 0.00 0.00 3.92 0.28 0.09
- 1964 6 0.09 1.04 5.56 4.43 0.85 16.11 1.79 0.57 15.93 7.82 1.23 11.78 0.19 6.22 3.49 5.18 0.19 0.09 0.38 0.28 0.00 0.00 0.09 0.57 0.00 4.99 0.00 0.19 0.75 0.09 -99.99
- 1964 7 0.47 0.56 0.19 0.28 0.00 5.77 24.38 4.19 1.02 12.47 0.65 0.09 8.93 4.19 0.00 0.09 2.33 2.61 0.28 0.56 2.23 0.09 0.28 10.05 0.09 0.09 0.74 3.07 2.98 1.40 2.23
- 1964 8 0.29 0.68 0.39 0.29 4.76 1.56 1.46 1.75 0.19 0.78 0.00 0.19 0.00 7.19 3.01 10.79 25.86 8.17 0.39 0.68 7.78 11.28 10.89 11.57 6.42 5.25 8.65 1.94 0.10 0.00 0.00
- 1964 9 0.00 0.00 0.10 0.10 13.94 0.77 4.55 15.39 20.42 5.52 0.29 0.10 7.74 14.23 6.97 4.45 3.29 6.39 3.19 1.06 23.33 12.20 0.10 1.36 1.84 3.58 0.10 2.61 0.10 0.10 -99.99
- 1964 10 0.00 0.00 0.00 0.10 18.12 31.61 8.77 4.14 0.29 2.51 1.06 2.60 14.36 3.37 0.67 0.48 0.48 5.11 0.77 1.54 0.10 7.13 0.96 3.37 0.96 2.60 0.00 0.00 0.00 0.00 0.10
- 1964 11 0.00 0.09 0.09 0.00 0.56 0.19 0.09 0.09 0.00 0.19 7.59 9.56 10.12 15.65 13.78 4.87 2.44 15.09 4.87 1.78 0.28 3.28 5.15 5.53 1.41 7.69 10.78 4.50 1.41 5.81 -99.99
- 1964 12 0.47 1.87 0.65 4.48 7.47 5.60 17.56 17.65 3.08 2.43 26.43 13.45 3.46 2.24 4.20 1.87 0.09 0.00 1.31 0.47 0.28 0.09 0.09 0.09 1.68 7.66 0.37 13.64 18.31 7.29 11.30
- 1965 1 0.30 0.00 0.00 0.90 0.40 9.89 10.19 17.68 16.28 17.38 7.39 5.59 22.37 7.79 7.49 14.98 6.19 0.00 0.10 0.00 3.10 4.19 11.19 0.20 0.30 1.00 0.30 0.20 0.00 0.00 0.00
- 1965 2 0.00 0.10 0.00 0.00 0.00 0.10 0.10 0.10 0.20 0.51 8.55 6.01 0.10 0.10 0.00 0.00 0.31 0.10 0.71 0.20 0.20 0.00 0.51 0.00 0.31 0.00 0.41 3.77 -99.99 -99.99 -99.99
- 1965 3 0.20 1.10 5.00 0.50 0.10 1.00 3.60 0.20 0.00 0.00 0.00 0.30 4.80 2.60 9.60 0.40 0.50 4.30 3.90 0.40 4.70 2.50 1.90 11.00 8.00 21.40 2.70 0.00 0.00 0.00 0.00
- 1965 4 0.00 0.00 0.60 0.50 0.10 2.40 3.81 0.80 16.73 6.91 11.92 2.91 0.60 12.83 0.60 15.53 2.91 3.01 0.20 0.00 0.00 1.60 0.10 1.00 4.91 3.61 1.20 1.10 1.80 0.00 -99.99
- 1965 5 0.00 2.40 3.30 1.40 0.20 3.30 9.01 6.51 1.30 0.00 0.20 0.00 0.00 5.81 0.20 3.00 9.71 0.50 0.00 0.20 4.21 8.51 4.81 5.81 5.21 1.20 4.21 0.00 0.00 0.00 0.00
- 1965 6 0.00 0.00 0.00 5.11 1.90 3.61 0.20 0.00 0.00 0.10 1.20 4.81 0.00 12.42 12.22 1.00 13.22 4.21 1.50 7.91 0.80 4.01 7.61 22.14 9.92 0.40 0.30 0.80 0.10 0.00 -99.99
- 1965 7 0.10 0.00 0.10 0.10 0.30 0.10 1.00 0.80 0.00 11.20 3.60 2.90 5.00 0.10 0.00 0.00 0.00 0.00 0.10 5.40 2.70 1.40 7.60 5.50 0.40 0.70 21.20 32.00 12.20 5.10 4.00
- 1965 8 0.70 3.50 0.00 28.90 7.50 3.40 1.10 0.00 0.00 0.00 0.10 0.00 0.00 5.70 0.00 0.50 3.90 0.60 2.20 21.20 7.10 2.40 1.40 11.50 3.80 0.00 4.10 8.20 2.90 8.00 0.40
- 1965 9 0.00 0.10 6.40 1.30 5.40 18.30 3.40 3.30 6.60 0.70 0.00 0.20 0.20 22.80 3.10 1.00 20.10 0.00 0.30 2.30 10.90 0.90 7.90 17.50 27.30 2.50 0.10 1.10 1.70 0.40 -99.99
- 1965 10 12.70 2.90 3.80 9.80 1.40 1.20 0.10 0.00 0.00 0.00 0.00 0.00 0.30 7.70 0.30 1.20 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 13.70 20.90 11.20 10.90 7.80 38.90
- 1965 11 10.70 2.00 0.10 0.10 0.00 0.30 7.80 1.70 0.20 0.10 1.10 2.20 0.20 0.30 0.00 2.20 2.60 2.20 11.40 2.00 0.00 7.60 8.80 2.40 1.40 6.00 0.90 1.80 1.70 0.10 -99.99
- 1965 12 13.40 7.20 0.30 14.40 5.30 0.50 10.90 15.00 9.60 1.50 1.90 2.60 4.60 7.80 2.40 3.80 16.20 2.60 2.40 2.00 3.20 10.30 1.70 0.30 0.00 0.10 0.00 4.90 9.60 3.60 10.10
- 1966 1 11.30 2.20 0.10 4.90 12.60 3.10 0.20 0.80 0.00 0.00 0.10 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.30 0.20 0.00 11.30 6.00 6.80 13.40 12.70 0.20 0.00
- 1966 2 2.70 5.90 5.80 26.32 7.11 5.80 3.90 1.30 1.30 0.50 0.10 0.80 0.30 0.00 0.00 0.00 0.00 13.31 6.60 9.31 2.90 10.71 2.00 11.91 7.01 5.60 7.81 0.00 -99.99 -99.99 -99.99
- 1966 3 16.19 2.70 4.20 2.20 0.40 0.80 9.59 1.50 10.29 5.40 2.30 1.60 0.70 0.50 0.10 0.20 4.80 0.00 0.00 1.90 1.90 5.90 1.90 0.60 15.99 19.68 0.60 0.10 2.70 2.60 8.19
- 1966 4 0.00 0.00 0.00 0.70 8.38 0.30 0.10 3.09 9.97 1.90 0.70 0.10 0.00 0.40 0.40 0.10 0.00 2.89 2.79 2.39 8.38 16.96 3.29 1.70 3.19 6.68 1.60 0.40 0.60 0.00 -99.99
- 1966 5 0.50 0.70 0.90 7.01 2.90 1.60 7.81 1.40 0.10 5.11 4.70 0.40 2.60 1.10 0.00 13.11 1.70 1.30 9.41 2.80 16.32 5.71 1.30 10.01 2.70 0.00 0.00 0.00 0.00 0.00 0.00
- 1966 6 0.10 0.00 6.71 15.81 0.40 4.20 0.40 0.00 8.71 0.20 0.00 1.00 8.71 1.60 4.10 2.70 5.10 3.60 3.60 5.60 6.40 16.51 18.71 0.80 1.00 10.21 1.60 0.00 0.50 0.00 -99.99
- 1966 7 0.10 0.50 0.00 0.00 1.61 0.70 0.30 0.40 2.51 4.92 1.00 4.32 1.41 0.00 1.71 0.00 0.00 0.00 0.00 0.00 0.00 1.20 0.90 1.31 11.85 3.71 5.92 2.91 4.02 0.60 0.00
- 1966 8 0.80 0.20 7.32 5.22 0.10 0.20 0.10 1.30 18.15 6.12 5.22 1.10 32.59 0.10 0.00 4.91 0.90 1.60 0.90 11.43 2.31 0.70 0.10 0.00 0.00 0.00 0.10 0.00 2.21 0.30 0.00
- 1966 9 14.23 1.80 32.47 3.41 2.81 0.40 0.40 1.70 21.65 5.81 10.82 5.91 6.92 13.83 0.20 4.31 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 1.40 0.30 7.42 -99.99
- 1966 10 8.90 0.70 9.40 0.00 23.80 6.20 8.00 0.40 1.60 4.20 1.10 0.20 7.40 0.50 0.40 3.40 4.00 1.60 4.40 4.50 2.70 2.60 0.20 0.00 1.30 0.40 0.50 0.50 0.50 2.40 1.60
- 1966 11 2.40 0.00 2.60 2.40 0.80 1.40 0.80 2.10 1.40 0.30 16.69 6.89 7.89 13.19 11.79 0.30 0.00 0.20 0.00 0.00 0.10 0.00 0.90 7.09 4.60 5.10 4.60 2.40 18.39 13.09 -99.99
- 1966 12 12.91 1.40 0.60 1.90 8.50 1.30 17.91 7.20 3.40 0.50 8.00 2.40 0.40 6.10 10.71 2.00 23.51 3.00 26.81 3.60 2.30 5.20 1.80 2.40 2.40 9.70 3.70 11.81 5.80 3.10 7.60
- 1967 1 0.50 0.10 0.20 0.00 0.90 1.10 0.60 0.30 0.50 0.10 0.10 0.10 0.80 0.00 0.00 2.70 12.69 13.19 7.99 7.69 8.79 7.89 4.60 4.00 13.79 7.39 4.20 2.20 3.40 3.40 9.19
- 1967 2 3.60 10.79 4.80 0.90 0.20 2.80 0.00 0.40 0.50 0.00 0.00 0.00 0.00 0.00 2.60 7.09 4.40 9.79 16.19 7.09 3.40 9.69 1.80 6.00 7.49 9.79 19.49 9.49 -99.99 -99.99 -99.99
- 1967 3 13.01 2.70 0.30 10.31 10.11 4.40 2.30 2.50 8.41 5.70 10.51 1.10 3.40 5.00 6.30 7.91 3.90 1.00 0.50 2.40 1.10 4.80 2.40 8.61 8.81 6.60 2.00 2.10 0.60 0.60 0.00
- 1967 4 18.57 5.09 2.20 2.20 0.10 0.80 1.00 0.50 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.70 0.10 1.90 5.79 1.90 1.10 3.49 5.99 7.39 0.00 2.30 0.30 0.20 0.90 1.90 -99.99
- 1967 5 1.00 1.20 10.22 3.51 3.71 6.61 5.21 3.71 1.30 0.80 5.51 0.70 1.70 0.90 7.91 6.21 3.31 11.72 3.31 3.51 13.22 12.82 3.61 4.71 1.90 1.40 5.01 5.11 0.20 0.30 0.00
- 1967 6 0.30 5.11 2.50 0.10 2.90 9.51 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 11.42 1.70 6.81 7.51 0.30 2.00 5.51 0.00 4.51 5.21 0.30 0.00 -99.99
- 1967 7 5.11 5.11 4.81 0.10 0.10 2.90 14.32 0.70 1.00 0.80 0.80 0.90 8.51 13.72 0.40 2.30 10.82 5.61 1.90 1.00 0.10 0.00 7.71 1.60 3.91 5.41 0.60 0.80 7.51 1.40 8.01
- 1967 8 0.90 1.10 5.51 1.80 0.40 0.70 0.00 4.91 2.40 3.30 16.72 2.30 0.60 13.92 6.31 5.01 0.50 0.70 0.00 0.00 0.00 0.30 0.00 0.20 0.80 0.40 6.71 4.41 2.70 3.90 0.00
- 1967 9 6.40 18.60 7.20 19.20 6.70 0.70 0.00 0.10 0.40 12.40 13.60 0.00 0.00 0.00 0.00 0.10 3.50 4.00 0.80 0.40 0.30 0.10 1.10 11.80 5.00 4.40 0.50 3.70 7.90 10.20 -99.99
- 1967 10 23.68 12.99 11.59 0.10 3.00 22.38 9.79 28.98 14.69 2.20 3.00 5.30 15.09 7.29 5.90 6.99 4.10 17.99 8.29 1.60 0.40 5.40 7.99 15.49 15.49 8.39 2.20 0.40 0.90 3.90 1.20
- 1967 11 11.49 3.10 2.60 0.40 0.30 0.70 1.90 10.69 3.00 12.99 4.30 10.59 10.69 6.79 0.30 0.00 0.10 0.20 0.50 0.30 0.00 0.00 0.20 4.70 1.20 5.10 10.19 5.89 3.20 1.00 -99.99
- 1967 12 0.60 1.30 0.00 3.10 1.60 0.30 0.10 0.50 0.40 4.10 3.50 0.20 0.90 1.70 4.10 0.10 0.80 0.00 0.00 15.22 12.11 16.12 4.80 8.81 0.10 3.30 4.80 0.90 3.10 4.30 3.00
- 1968 1 5.99 6.98 1.00 1.30 3.19 0.10 0.20 2.89 0.20 1.90 0.00 3.29 10.48 16.46 7.08 13.97 5.59 8.98 0.70 0.20 0.20 0.40 0.50 1.50 0.50 0.90 2.29 1.40 9.18 9.68 13.57
- 1968 2 5.98 3.95 9.19 11.59 8.28 2.94 6.16 4.23 0.18 0.18 0.00 5.88 0.28 0.00 0.28 2.21 0.46 0.09 6.34 2.39 0.00 0.09 3.13 0.18 0.00 0.00 0.00 0.09 0.09 -99.99 -99.99
- 1968 3 0.10 0.10 0.10 0.40 0.20 0.10 0.00 0.00 0.20 0.00 0.00 3.40 3.40 9.30 0.90 18.20 13.50 8.80 9.70 2.20 6.80 11.70 8.80 0.80 2.90 19.60 0.70 0.20 0.30 3.40 16.40
- 1968 4 11.10 2.90 0.90 0.30 0.10 1.20 0.00 0.40 0.10 0.00 0.00 0.00 0.00 0.00 0.00 8.40 4.30 6.10 10.10 0.20 5.60 0.50 0.30 0.00 0.00 2.20 5.90 0.50 0.50 0.90 -99.99
- 1968 5 6.01 9.61 9.71 15.01 16.82 0.80 0.60 0.00 10.51 3.90 2.80 3.20 0.70 12.21 2.60 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.80 3.00 0.10 0.00 0.00 2.40 1.20
- 1968 6 2.61 0.60 1.10 0.00 1.30 1.90 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.80 6.21 3.91 1.00 3.11 5.51 7.01 9.12 2.81 0.00 0.00 2.10 0.80 -99.99
- 1968 7 18.88 29.52 7.23 0.30 0.40 0.70 1.00 5.02 0.10 1.81 0.60 0.20 0.00 0.50 2.41 0.80 0.30 0.40 1.10 0.90 0.10 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.80 0.80
- 1968 8 0.00 0.00 0.30 0.40 1.90 0.00 0.10 0.00 0.00 0.00 0.00 17.00 20.70 0.90 4.50 0.40 1.40 0.30 17.10 1.20 0.50 8.40 0.60 0.00 0.00 0.00 0.80 0.20 0.00 6.30 1.30
- 1968 9 11.11 11.21 5.40 0.10 8.30 0.00 0.40 4.40 0.40 8.30 7.20 18.41 0.70 0.10 0.00 0.00 0.00 0.00 10.51 1.30 0.10 10.81 0.50 0.00 11.61 7.00 17.01 10.61 8.71 15.81 -99.99
- 1968 10 20.78 4.30 5.29 1.30 3.00 0.50 0.80 0.20 21.38 7.09 18.08 9.69 4.20 4.20 10.39 7.79 0.10 0.10 20.88 0.20 0.00 0.00 0.00 0.00 1.20 3.70 9.79 6.89 8.49 7.49 25.38
- 1968 11 3.00 0.00 0.00 0.00 0.40 0.00 0.30 0.10 0.40 4.50 2.30 0.00 0.00 0.00 0.00 0.00 0.00 0.10 1.10 10.79 30.27 11.19 8.39 8.09 2.70 0.30 14.89 0.10 0.00 0.50 -99.99
- 1968 12 0.56 0.56 4.74 3.53 3.53 0.37 0.00 0.19 0.00 0.09 0.00 2.79 0.84 0.93 2.97 5.39 0.84 1.02 11.71 4.18 12.55 10.97 0.46 0.37 0.09 0.09 0.09 0.00 0.09 0.09 0.74
- 1969 1 0.30 0.60 4.10 6.50 2.00 1.90 6.00 5.90 0.20 7.90 1.00 13.50 0.80 3.50 1.40 1.90 4.20 3.50 0.40 15.50 5.30 0.10 5.60 4.20 2.00 3.90 8.30 0.60 3.40 6.80 4.60
- 1969 2 5.81 0.00 0.60 0.80 10.82 2.40 0.50 0.90 0.40 11.22 1.90 0.20 0.30 0.40 0.10 0.10 1.90 0.90 0.10 0.10 0.10 9.22 0.30 0.60 1.50 0.20 0.20 0.20 -99.99 -99.99 -99.99
- 1969 3 0.20 0.40 0.00 0.00 0.00 0.00 0.10 1.00 0.00 0.20 0.00 1.10 3.20 0.90 0.60 0.10 2.40 3.70 3.20 0.30 0.10 0.00 0.00 0.20 0.00 0.00 0.00 0.40 4.60 6.00 3.50
- 1969 4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.31 5.13 1.81 10.06 1.11 1.61 10.16 1.11 0.70 0.00 0.00 0.00 0.20 3.72 0.60 3.52 6.23 2.82 1.01 0.70 0.40 0.10 0.40 -99.99
- 1969 5 0.20 4.50 0.30 1.30 0.20 9.91 11.91 6.31 0.80 4.20 3.10 4.30 8.81 0.90 1.80 0.20 1.30 0.30 0.00 0.00 0.00 0.00 0.90 15.82 0.90 7.71 2.80 2.20 0.70 3.20 1.40
- 1969 6 0.40 10.71 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.71 6.51 11.61 8.81 3.50 7.11 4.20 2.70 2.90 1.20 0.90 10.51 1.20 2.00 1.30 2.10 -99.99
- 1969 7 4.49 0.00 6.79 1.80 1.60 1.00 3.70 0.40 0.60 4.39 1.40 0.20 0.00 0.00 0.00 1.00 2.30 11.48 0.50 2.50 2.60 9.59 0.30 0.00 6.89 7.09 2.50 1.00 0.00 0.00 0.00
- 1969 8 0.20 3.61 6.91 3.00 1.20 0.00 0.00 13.02 6.11 0.70 1.30 0.10 0.90 1.90 0.00 0.90 0.80 0.70 7.01 3.31 3.91 1.60 0.00 2.50 2.80 0.90 0.10 0.00 0.00 0.00 0.10
- 1969 9 0.00 0.20 0.00 0.10 0.10 0.30 0.60 0.50 11.10 8.30 5.30 0.00 0.00 0.00 0.00 0.10 3.10 1.70 0.00 6.80 11.20 0.30 2.40 4.20 8.20 11.60 3.00 6.20 2.60 1.90 -99.99
- 1969 10 13.34 5.92 2.11 0.10 3.31 2.21 2.61 14.54 0.80 0.00 0.00 0.00 9.43 1.70 1.40 3.91 0.20 0.20 0.40 0.50 0.80 6.42 18.65 5.11 0.40 0.40 0.40 2.21 1.81 2.91 1.00
- 1969 11 18.41 18.21 3.60 6.70 13.31 4.70 14.01 9.91 8.50 7.60 1.20 7.10 2.90 2.70 2.30 0.20 0.90 3.10 6.20 7.20 7.10 11.41 0.20 0.00 0.10 2.40 10.11 0.60 0.40 2.70 -99.99
- 1969 12 2.80 8.49 0.10 0.00 2.60 4.00 0.50 0.70 0.50 6.10 0.30 0.10 27.98 12.49 2.50 2.60 3.30 5.00 5.80 14.69 18.49 1.50 6.89 0.40 0.40 0.40 0.00 0.00 0.20 0.10 0.20
- 1970 1 0.70 0.40 0.40 0.40 4.20 0.00 0.00 1.80 10.19 0.80 10.99 3.90 1.40 6.29 4.80 0.40 12.69 4.40 8.39 3.50 7.19 0.60 0.50 8.79 3.50 5.20 0.10 0.00 1.50 1.20 7.89
- 1970 2 21.42 5.40 1.00 0.30 0.00 8.81 6.10 5.30 1.90 0.30 0.10 0.10 0.00 0.00 0.10 7.81 2.50 8.01 19.72 5.40 13.41 11.71 5.30 0.70 0.20 0.40 0.20 0.50 -99.99 -99.99 -99.99
- 1970 3 0.90 0.20 1.90 0.30 0.50 0.40 0.70 0.40 0.20 7.99 3.59 0.80 0.10 0.10 0.00 8.09 5.09 1.70 10.58 1.60 2.00 0.60 0.00 0.00 2.20 0.50 0.60 2.80 13.58 1.30 0.10
- 1970 4 0.20 3.00 0.40 0.70 10.21 0.10 0.20 0.60 0.50 0.90 1.70 0.00 0.00 3.70 3.80 13.21 8.01 0.70 2.80 5.91 17.52 21.82 1.70 0.70 0.30 0.10 0.70 1.90 0.30 0.30 -99.99
- 1970 5 6.81 1.50 0.00 0.60 7.71 0.30 5.61 0.50 1.90 0.00 0.00 0.00 1.70 1.60 1.20 0.00 0.10 2.90 1.20 8.21 0.20 0.80 0.40 8.21 5.61 0.30 0.50 0.30 1.30 1.50 4.31
- 1970 6 1.50 0.20 0.00 0.00 0.00 0.20 0.20 0.40 0.00 5.69 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 2.00 3.79 10.18 12.08 0.20 5.89 7.29 3.30 3.30 9.79 -99.99
- 1970 7 6.90 0.80 0.90 0.60 13.90 4.50 0.20 9.30 0.40 5.30 7.00 6.00 2.50 0.40 0.10 0.10 0.50 4.80 0.80 3.00 2.40 2.90 14.80 8.90 0.50 3.60 3.20 3.30 3.30 15.00 0.80
- 1970 8 0.30 0.00 0.10 0.00 0.00 0.00 0.00 0.50 6.41 0.10 1.80 1.80 13.42 2.40 30.33 5.21 0.00 0.00 2.80 7.51 0.20 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.20 2.90 11.91
- 1970 9 9.20 10.50 1.40 1.10 0.80 2.00 21.40 9.20 16.80 6.20 2.10 1.70 8.30 0.70 0.30 9.90 20.00 0.70 7.10 2.00 0.10 0.50 0.00 0.40 10.70 3.10 0.80 10.00 11.00 4.30 -99.99
- 1970 10 21.74 2.50 6.61 16.23 9.92 6.21 1.80 0.10 1.70 0.10 2.20 0.00 0.20 0.00 0.00 0.00 1.20 9.52 0.40 0.00 0.00 0.40 9.32 11.82 6.51 3.41 7.11 11.32 22.34 3.71 26.64
- 1970 11 7.71 24.12 9.81 15.22 0.50 0.00 4.60 5.41 0.70 10.31 11.11 11.61 7.21 0.30 8.91 3.20 5.71 1.40 2.00 5.31 1.30 2.60 31.43 1.70 2.20 10.91 9.71 3.70 1.90 0.50 -99.99
- 1970 12 3.60 17.82 3.00 6.11 7.61 2.10 0.00 0.10 0.00 0.20 0.80 1.90 0.70 0.80 1.00 12.82 4.41 5.91 2.90 0.30 0.20 0.00 1.00 0.20 0.70 0.30 0.30 0.70 0.30 0.10 0.00
- 1971 1 0.50 0.10 0.00 0.60 6.41 15.13 2.81 4.41 7.21 0.00 0.00 0.00 0.70 0.10 0.30 2.20 3.81 10.12 4.91 5.51 0.60 6.41 6.51 11.12 5.11 1.00 4.01 5.61 2.91 0.30 0.00
- 1971 2 0.50 3.10 0.20 0.10 0.00 0.40 0.00 2.10 0.70 3.50 17.82 21.72 12.91 12.41 2.80 6.01 6.61 0.10 7.31 4.50 0.80 0.40 1.00 0.50 0.10 0.20 9.31 2.30 -99.99 -99.99 -99.99
- 1971 3 12.60 0.10 0.00 0.70 0.30 0.00 0.00 0.00 0.50 1.40 3.00 3.50 3.50 0.20 0.00 0.70 0.40 4.40 5.50 0.10 0.00 0.80 4.30 5.70 3.40 0.60 2.60 6.10 4.10 2.20 0.90
- 1971 4 0.00 0.10 0.30 1.80 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.11 0.20 4.51 0.70 0.50 0.40 0.80 9.42 28.65 3.21 0.00 0.10 0.00 0.00 0.10 2.70 -99.99
- 1971 5 0.00 0.00 0.00 0.00 0.00 4.01 2.81 0.50 1.70 0.10 0.30 0.00 0.00 4.11 5.62 5.11 4.61 0.60 0.30 0.00 0.10 5.82 12.24 0.40 0.20 7.02 3.91 0.60 3.51 4.21 0.50
- 1971 6 0.00 0.00 0.00 0.00 0.00 0.10 0.20 0.40 0.30 0.20 4.70 0.00 1.10 0.40 1.30 0.70 0.10 3.20 5.50 8.10 9.50 0.00 1.50 4.70 7.60 6.00 4.50 0.40 0.20 2.70 -99.99
- 1971 7 0.40 0.40 8.70 2.80 0.00 0.00 0.00 0.10 0.00 0.00 0.10 0.00 0.00 0.20 0.20 0.00 0.00 0.00 0.00 0.20 4.50 8.60 10.50 24.00 7.80 4.00 4.10 0.00 0.00 10.80 7.80
- 1971 8 5.81 0.70 6.21 5.41 10.72 7.21 0.40 4.31 0.70 0.00 0.10 6.31 12.42 0.90 0.40 0.10 0.00 0.00 0.00 0.00 0.10 2.71 0.80 0.40 0.20 6.71 7.62 2.10 6.51 5.11 7.51
- 1971 9 4.01 9.72 1.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.81 7.01 0.20 0.70 1.60 0.30 1.50 0.60 0.20 0.20 0.60 0.00 3.11 2.81 0.10 4.21 5.41 0.40 -99.99
- 1971 10 0.20 0.00 0.00 0.00 5.00 4.00 7.50 1.40 9.11 21.51 3.20 0.20 0.00 0.90 14.91 5.20 20.51 8.21 16.11 13.31 15.81 2.30 6.10 0.80 0.00 0.00 0.00 1.50 2.00 3.20 0.30
- 1971 11 2.40 3.60 3.30 25.52 2.30 4.80 8.51 0.20 0.20 0.10 0.50 2.70 0.30 1.80 5.90 1.10 12.41 0.70 0.30 18.82 0.20 2.70 0.30 1.60 1.00 4.40 2.90 1.80 6.31 2.00 -99.99
- 1971 12 0.50 0.00 5.31 0.60 0.00 0.10 1.40 0.90 1.00 0.10 1.20 7.01 5.01 6.31 0.90 0.40 0.00 11.51 10.31 17.02 3.00 3.50 4.81 0.90 2.00 8.41 0.00 0.60 0.10 0.00 0.00
- 1972 1 2.10 2.30 0.50 0.10 0.00 0.00 2.10 10.49 2.20 12.99 17.49 6.30 7.00 0.40 2.50 4.80 6.50 24.18 3.10 4.10 3.10 3.80 11.09 5.70 5.10 5.60 0.40 0.20 0.20 0.10 0.10
- 1972 2 8.39 6.19 2.10 0.10 2.50 0.60 0.30 2.70 4.00 10.09 4.89 9.19 2.00 1.90 15.48 3.10 0.70 0.00 0.00 0.10 0.10 0.00 0.00 0.00 5.29 0.80 3.40 2.90 2.80 -99.99 -99.99
- 1972 3 1.40 9.12 7.82 5.91 0.20 1.20 0.80 0.10 0.10 0.10 0.00 0.00 0.00 1.20 0.00 5.61 0.50 1.30 3.91 0.00 0.20 0.50 0.00 0.00 4.01 12.03 4.31 1.20 11.43 1.30 7.22
- 1972 4 19.69 14.39 5.10 6.70 9.39 2.40 11.89 9.99 13.09 7.99 0.70 1.30 0.10 5.00 0.60 0.80 0.30 0.00 0.00 0.00 0.60 0.10 0.00 0.00 0.00 0.00 0.40 23.09 12.59 8.29 -99.99
- 1972 5 2.00 0.00 1.00 0.00 6.60 6.30 6.10 5.10 2.10 0.40 9.90 4.10 0.10 0.10 0.00 0.00 0.00 0.10 0.50 3.30 4.50 0.60 2.50 7.70 26.30 10.20 1.20 2.50 12.80 4.80 3.70
- 1972 6 5.90 10.11 4.90 10.01 4.30 1.20 11.61 0.60 1.60 1.50 0.20 1.30 0.00 0.80 0.00 0.00 24.52 3.80 1.90 12.81 4.70 3.40 4.70 7.31 0.40 3.60 9.71 1.60 0.60 1.70 -99.99
- 1972 7 1.00 0.20 3.51 9.82 2.10 4.31 0.10 0.30 2.40 3.00 0.10 6.31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 8.61 4.41 8.11 0.00 0.00 0.00 0.60 0.40 2.30 3.81
- 1972 8 1.11 0.10 9.25 3.72 3.52 6.73 11.96 8.04 0.80 0.50 0.30 0.20 0.70 0.00 0.00 9.65 1.31 0.00 1.41 0.00 0.00 0.10 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1972 9 0.00 0.00 0.00 0.00 0.90 3.02 0.50 1.01 0.40 1.31 0.80 3.22 0.20 0.00 0.00 0.00 0.00 0.00 0.00 6.53 1.91 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1972 10 0.00 0.00 0.00 0.00 0.00 0.00 0.30 10.90 8.60 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 3.10 3.60 0.30 2.40 0.10 1.50 9.70 6.50 7.30 0.50 0.70
- 1972 11 2.80 3.00 0.10 0.40 1.60 5.69 0.90 11.29 21.27 10.59 6.59 4.09 0.10 6.39 5.49 0.50 0.10 9.59 11.59 1.40 0.50 0.40 0.00 1.00 2.50 0.30 10.49 9.99 18.78 15.48 -99.99
- 1972 12 4.50 0.20 9.01 13.41 6.40 6.00 3.20 0.20 9.11 8.41 24.02 8.41 3.90 0.50 0.00 2.50 0.00 0.00 0.00 0.60 0.30 0.40 2.80 0.20 2.00 5.10 3.30 3.90 2.00 7.31 12.71
- 1973 1 8.60 6.80 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.00 0.30 2.10 5.30 14.20 1.90 0.10 3.10 18.00 21.60 9.30 3.70 6.40 1.20 13.50 3.10 1.30 1.80 5.60 5.80 1.80
- 1973 2 0.40 0.40 0.60 3.50 5.81 4.50 8.51 8.41 3.20 1.10 13.01 9.71 6.41 1.60 0.50 0.00 0.00 2.30 2.00 1.20 3.50 8.01 2.20 0.00 0.00 0.10 4.50 7.01 -99.99 -99.99 -99.99
- 1973 3 6.45 6.95 2.88 2.38 3.97 0.30 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.49 0.40 4.07 0.50 0.00 6.55 0.30 14.29 3.57 9.33
- 1973 4 4.48 0.40 18.41 8.36 8.76 2.39 0.90 0.90 0.30 0.10 0.30 0.30 0.00 0.10 0.00 0.00 0.00 0.10 0.10 1.19 2.79 0.30 0.90 0.30 0.00 0.60 0.90 0.00 4.78 7.17 -99.99
- 1973 5 0.10 0.20 7.38 13.27 4.79 3.39 2.39 2.39 9.58 3.09 1.40 15.17 0.30 0.40 0.00 0.00 0.00 0.00 0.20 3.39 7.48 2.20 0.20 1.40 1.80 1.00 3.59 0.40 4.09 1.00 0.40
- 1973 6 4.71 3.30 0.50 0.10 0.00 0.00 0.00 0.50 3.00 0.20 2.80 14.02 1.10 2.10 0.00 3.60 0.70 28.64 0.70 0.00 0.00 0.00 0.30 0.60 0.30 0.30 0.00 3.40 1.20 5.51 -99.99
- 1973 7 12.06 1.21 6.03 0.50 2.71 2.41 0.20 0.00 0.20 7.24 1.01 7.84 7.34 6.53 0.00 0.00 0.70 5.83 3.92 4.22 2.01 1.41 0.20 0.00 0.00 0.00 0.00 1.81 0.20 0.30 0.60
- 1973 8 3.41 3.61 5.02 9.83 6.52 11.04 2.81 7.02 5.82 1.30 0.00 0.00 0.00 0.00 0.00 0.30 0.10 1.50 4.11 0.00 0.00 1.30 0.20 0.00 0.00 3.51 1.50 2.91 3.91 7.52 8.53
- 1973 9 1.70 1.20 6.19 7.89 0.70 3.30 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 1.40 8.09 4.69 0.90 0.00 0.10 2.10 0.40 0.00 5.59 3.50 0.20 20.07 9.79 0.70 0.60 -99.99
- 1973 10 0.50 0.00 0.00 0.10 0.00 0.70 2.31 11.43 4.51 0.40 0.00 0.00 0.10 0.00 0.00 0.30 0.80 14.84 4.01 11.23 7.22 1.10 1.91 1.00 2.61 0.30 0.40 0.00 0.00 0.00 0.20
- 1973 11 0.60 1.50 1.80 14.23 1.40 0.20 3.51 15.44 1.30 5.41 19.04 8.52 4.81 3.11 1.90 0.80 14.33 8.72 0.10 0.20 3.01 0.60 10.02 0.70 0.60 0.60 0.30 5.01 0.00 0.00 -99.99
- 1973 12 0.00 1.20 2.80 1.00 4.30 0.30 11.79 0.10 1.20 11.59 6.60 15.29 2.10 0.60 22.49 3.90 1.00 4.00 22.29 5.40 3.40 8.69 1.00 0.50 8.59 1.10 3.50 2.10 11.39 0.20 0.40
- 1974 1 3.60 0.00 1.40 16.10 8.70 9.50 5.00 13.00 2.50 11.50 10.80 6.10 7.10 6.70 4.50 9.30 29.10 5.80 0.10 0.10 0.20 9.10 6.30 1.90 8.60 10.70 4.40 9.70 26.30 12.70 2.80
- 1974 2 5.00 5.60 1.10 4.50 3.20 0.50 4.50 15.60 9.80 1.80 7.50 2.70 1.30 12.50 11.70 0.80 0.00 0.40 0.90 3.10 2.60 2.70 1.20 0.70 0.50 0.50 0.50 10.00 -99.99 -99.99 -99.99
- 1974 3 1.70 0.20 1.10 0.50 0.20 13.80 0.20 0.00 0.30 0.10 0.20 0.60 1.60 7.90 7.30 5.10 7.50 2.10 6.80 0.00 12.10 0.10 0.00 0.60 2.20 0.10 0.00 0.00 0.00 0.00 0.00
- 1974 4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 6.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.59 2.97 0.10 0.20 -99.99
- 1974 5 3.19 1.69 0.40 0.20 0.30 0.00 0.10 2.19 7.48 4.98 2.49 5.38 1.00 0.00 0.00 1.10 6.38 3.49 2.59 0.40 3.39 4.59 4.09 0.10 0.10 0.10 6.78 0.40 0.10 0.00 0.50
- 1974 6 5.90 1.30 0.90 3.00 6.90 7.80 6.90 0.80 2.50 6.40 1.90 0.00 0.00 0.00 0.00 5.10 1.90 1.70 0.90 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.70 11.10 -99.99
- 1974 7 0.00 4.58 1.40 8.67 5.58 0.30 0.50 5.38 0.70 4.58 3.29 2.39 1.20 7.87 8.57 1.10 0.40 2.19 0.70 0.30 2.89 12.76 1.89 0.10 2.99 2.89 3.59 1.59 0.50 0.10 0.00
- 1974 8 3.10 0.70 0.10 0.00 3.70 1.40 2.40 4.80 9.71 4.40 13.01 1.80 0.00 14.11 1.10 2.30 1.00 0.40 0.40 3.80 1.10 6.01 4.40 10.71 2.70 1.90 0.90 1.70 0.00 0.00 1.70
- 1974 9 13.41 7.40 4.40 10.91 9.51 9.01 6.50 3.30 3.30 0.40 0.90 13.71 2.50 9.81 1.30 15.41 0.80 1.30 1.30 11.51 7.81 6.50 2.60 8.21 1.40 1.40 0.00 0.00 0.70 0.10 -99.99
- 1974 10 4.19 2.00 0.00 0.00 3.69 2.60 0.40 0.00 0.80 0.10 0.00 1.10 2.90 0.50 0.30 1.90 11.68 7.19 4.39 1.80 0.10 0.00 0.10 0.50 0.70 4.29 2.60 0.10 1.30 5.59 2.00
- 1974 11 2.50 6.20 2.60 0.10 5.70 0.10 10.90 14.60 10.80 22.30 8.10 6.10 20.10 10.60 1.10 4.30 0.90 1.80 0.10 0.00 4.60 2.90 6.70 14.70 2.80 9.20 6.50 1.60 5.30 4.20 -99.99
- 1974 12 3.09 1.50 7.69 7.09 3.69 5.59 10.78 4.79 16.17 7.79 5.69 1.10 6.49 5.19 13.48 16.87 6.59 1.80 15.47 6.79 8.49 3.89 3.99 3.69 20.57 12.48 7.09 13.38 1.30 4.19 8.79
- 1975 1 0.70 2.50 1.50 14.09 7.49 3.20 1.00 1.90 12.29 12.79 10.19 7.29 20.98 12.19 8.29 3.30 1.90 0.20 20.19 2.10 27.78 15.99 7.59 13.89 5.40 10.09 7.29 3.30 18.39 10.59 5.60
- 1975 2 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.10 0.20 3.81 8.32 0.50 4.61 10.82 3.81 0.10 10.12 5.31 0.30 2.20 1.50 0.70 0.00 0.00 0.00 0.00 -99.99 -99.99 -99.99
- 1975 3 5.02 1.10 2.11 0.30 8.54 7.94 2.01 0.50 0.40 1.91 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.20 0.00 11.45 0.30 0.60 0.70 1.10 0.40 0.10 0.10 0.30 0.00 0.00
- 1975 4 2.29 0.20 1.10 0.50 0.00 3.19 0.30 0.20 1.10 3.89 4.19 1.60 3.39 2.99 1.50 9.77 1.40 0.60 3.99 5.78 6.08 0.00 0.10 0.10 0.00 0.40 0.70 6.98 4.59 7.98 -99.99
- 1975 5 2.50 0.00 0.00 0.00 0.00 0.00 0.00 3.20 4.10 4.00 2.00 5.50 4.20 0.40 0.00 0.00 0.00 0.00 0.30 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1975 6 7.00 2.00 1.30 20.40 3.30 0.00 0.00 0.00 0.00 0.00 0.20 0.10 0.60 0.50 4.20 5.00 3.80 8.00 3.10 0.10 0.00 0.00 0.30 0.00 0.10 0.00 0.00 0.00 0.00 0.00 -99.99
- 1975 7 0.00 0.00 0.70 0.00 0.00 0.00 0.00 0.20 1.99 6.28 1.99 1.99 8.97 11.96 10.07 0.00 1.00 0.00 4.09 0.20 14.75 10.57 3.49 1.30 2.69 1.79 0.00 1.20 6.98 0.40 0.00
- 1975 8 0.00 0.00 0.00 4.01 3.51 0.30 0.40 8.31 1.90 0.00 0.00 1.10 0.00 2.20 1.20 0.40 0.00 0.20 5.61 3.81 2.00 0.00 1.20 0.60 1.50 2.30 0.40 0.50 23.64 0.20 0.00
- 1975 9 0.00 2.40 0.20 1.40 4.70 4.40 1.40 10.69 8.50 1.40 6.00 0.70 0.30 0.10 1.20 13.79 24.89 0.50 4.80 9.99 1.40 10.39 4.60 27.39 10.99 0.70 5.90 9.29 3.00 13.49 -99.99
- 1975 10 6.30 15.00 10.50 16.80 0.70 0.00 0.00 0.50 15.40 0.20 0.00 0.00 0.00 2.50 1.30 0.00 0.00 0.00 0.00 0.00 0.00 9.90 10.50 1.70 0.30 0.90 0.10 0.00 3.30 1.50 5.50
- 1975 11 1.00 15.32 2.00 4.91 2.00 0.10 0.10 0.00 0.20 0.50 5.31 0.00 0.10 3.40 13.22 0.30 0.00 7.01 1.50 0.00 0.00 9.91 5.61 5.11 5.21 19.43 9.61 6.21 8.21 12.12 -99.99
- 1975 12 13.75 0.20 0.70 1.61 1.00 0.90 0.20 0.40 0.70 0.90 1.81 0.00 0.50 0.40 1.30 0.30 0.10 0.20 0.80 0.80 0.90 0.90 4.21 5.72 0.70 1.20 0.50 2.41 1.20 11.14 4.31
- 1976 1 8.69 21.69 3.50 5.50 10.39 8.69 3.60 0.50 7.40 9.39 1.90 4.40 6.00 1.60 1.40 1.10 3.60 10.89 19.99 10.79 9.99 4.30 0.80 0.10 0.40 2.10 1.60 6.30 0.10 0.00 0.00
- 1976 2 0.00 0.30 0.20 0.10 2.30 3.60 4.10 2.20 12.21 8.21 12.21 2.00 0.00 4.91 0.00 0.00 0.30 0.20 0.10 1.20 3.70 12.41 4.51 2.20 2.00 1.10 0.00 1.20 5.01 -99.99 -99.99
- 1976 3 0.10 0.00 0.00 1.10 0.00 0.00 0.00 0.00 9.41 15.01 8.21 1.90 0.00 0.80 1.40 7.21 0.10 0.00 0.20 19.42 8.31 0.40 1.30 9.71 3.90 9.71 0.10 12.11 4.10 7.91 5.90
- 1976 4 7.50 2.90 7.20 3.20 2.30 1.90 0.10 0.30 0.10 13.80 2.10 3.50 12.10 0.00 0.10 0.20 1.10 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 2.60 -99.99
- 1976 5 7.18 4.79 0.80 1.20 0.00 1.80 0.00 0.00 1.60 2.00 8.08 3.29 2.99 2.89 7.08 18.36 4.09 1.10 6.19 2.10 12.97 0.80 0.00 12.57 1.30 0.00 0.60 10.58 11.97 6.49 6.99
- 1976 6 3.79 1.00 0.20 2.00 0.30 0.00 0.00 0.00 4.69 3.39 9.89 0.30 0.90 1.30 1.40 11.18 5.09 3.30 4.49 0.40 3.89 2.80 4.99 0.80 0.60 0.00 0.00 0.00 0.40 0.00 -99.99
- 1976 7 0.00 0.00 0.99 0.30 0.00 0.00 0.00 0.00 10.52 1.09 0.50 4.47 4.66 4.86 12.11 1.09 0.10 15.88 0.50 1.09 0.10 0.79 0.69 0.20 0.00 0.20 0.00 0.10 0.69 2.98 1.39
- 1976 8 4.42 3.88 0.00 0.18 0.18 0.72 0.09 0.00 0.00 0.00 1.44 4.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.34 1.80 0.09 0.45
- 1976 9 0.00 0.00 0.00 0.00 0.30 0.90 0.60 15.64 2.91 7.32 0.60 1.90 3.41 0.20 0.00 0.10 0.00 0.00 3.01 4.21 22.36 6.82 0.10 0.00 16.04 0.10 13.93 14.84 1.40 3.41 -99.99
- 1976 10 1.80 6.88 8.79 0.00 14.93 1.59 2.86 0.53 0.85 10.91 14.72 2.33 4.98 20.76 2.22 0.64 8.90 0.53 1.48 7.63 1.38 14.72 11.54 0.53 4.87 0.00 0.32 0.64 1.17 0.00 7.31
- 1976 11 4.16 5.89 5.89 7.21 3.45 6.19 4.06 1.52 2.74 4.16 0.10 0.00 0.00 5.58 18.28 0.00 12.69 0.00 0.00 0.00 0.30 0.30 1.02 1.52 5.89 19.39 15.74 5.08 8.43 5.38 -99.99
- 1976 12 4.94 0.20 0.40 0.71 7.66 12.70 5.34 3.12 4.94 2.12 0.81 1.51 0.50 2.82 4.74 2.92 1.01 0.00 6.35 4.03 1.11 3.93 0.10 0.10 0.00 1.81 0.71 4.43 14.92 8.47 0.00
- 1977 1 0.00 0.60 4.50 11.90 0.80 1.60 0.70 5.30 1.70 0.10 0.00 0.00 2.90 4.10 0.10 0.00 0.30 7.80 14.00 6.50 5.60 2.20 0.50 3.30 16.70 0.20 0.00 0.00 3.60 4.70 1.50
- 1977 2 7.21 25.62 4.90 5.90 1.10 10.21 1.30 1.20 18.41 7.21 0.60 0.90 4.80 6.81 2.10 0.20 10.71 4.40 0.20 2.30 9.41 1.00 0.00 0.00 0.00 0.00 0.00 1.90 -99.99 -99.99 -99.99
- 1977 3 4.41 11.52 4.01 3.91 0.30 0.70 2.30 0.50 7.11 6.91 3.71 3.20 8.91 2.40 12.92 3.51 7.81 7.81 1.90 1.80 0.40 0.50 0.50 0.00 0.60 3.20 0.20 0.00 0.20 26.54 9.01
- 1977 4 6.23 4.09 0.19 0.39 0.49 0.19 0.10 0.58 1.27 0.58 3.89 4.28 3.41 0.00 0.97 1.75 0.00 0.00 3.70 7.59 19.67 10.03 4.87 4.38 3.60 4.19 15.19 4.38 4.58 0.49 -99.99
- 1977 5 1.10 1.00 9.60 0.20 0.70 10.80 4.80 0.30 1.30 3.20 8.80 11.30 0.70 0.10 0.70 0.40 0.10 0.00 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
- 1977 6 0.00 0.00 0.10 5.40 3.80 16.70 1.40 1.30 2.40 4.40 2.20 4.70 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.20 0.10 0.70 0.20 0.50 3.10 3.90 -99.99
- 1977 7 1.70 3.20 0.30 0.00 0.00 0.00 0.20 1.70 0.00 0.00 0.00 0.00 0.00 0.00 8.80 1.00 14.10 2.00 2.90 0.10 0.80 15.00 7.40 2.40 0.20 0.10 0.10 0.00 0.50 0.10 0.40
- 1977 8 5.60 6.20 9.30 19.80 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 4.30 7.20 3.60 0.00 0.20 0.10 1.60 5.60 0.10 0.60 0.90 21.80 3.60 4.40 0.10 1.60 4.50 8.10 3.40
- 1977 9 7.21 2.20 10.82 9.42 12.92 2.60 6.91 2.60 22.24 11.32 4.41 0.00 0.10 0.60 0.40 0.00 0.00 0.00 0.00 0.80 0.00 0.70 0.00 1.40 7.11 9.42 21.54 8.41 24.44 15.93 -99.99
- 1977 10 2.60 0.10 17.40 2.90 23.90 3.20 30.20 2.80 4.70 3.00 6.70 0.30 0.30 0.40 0.00 0.00 0.00 0.90 1.30 5.40 2.70 10.90 19.00 2.20 1.40 2.70 0.70 1.00 8.40 46.40 4.60
- 1977 11 6.00 8.80 11.00 9.80 12.10 24.50 10.50 9.40 17.50 5.10 9.30 5.60 4.40 6.70 4.00 2.80 0.50 4.00 7.80 0.40 0.10 5.20 9.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1977 12 0.20 0.00 0.00 0.00 0.20 0.20 1.50 0.80 9.40 7.60 12.00 0.90 3.70 4.30 0.20 0.20 2.80 1.10 0.00 0.00 2.00 20.00 11.90 4.40 3.10 8.10 1.80 1.90 1.20 0.30 3.70
- 1978 1 3.80 20.10 1.10 6.90 0.90 1.70 0.60 10.50 9.70 6.90 0.50 1.00 0.10 0.40 2.20 0.60 0.20 8.20 3.40 0.70 9.70 4.00 11.70 0.10 2.60 3.90 17.20 4.60 0.90 8.40 8.20
- 1978 2 9.60 0.32 8.64 6.30 4.48 0.96 0.00 0.43 0.00 0.21 1.28 0.00 0.53 0.00 0.00 0.00 0.00 0.00 0.11 1.39 0.85 18.03 6.62 6.62 11.10 3.41 4.16 0.85 -99.99 -99.99 -99.99
- 1978 3 6.53 3.37 0.59 0.00 0.00 10.00 17.32 0.00 3.46 0.40 4.85 5.44 13.66 9.60 4.06 0.79 0.10 1.19 17.42 3.96 5.74 12.47 7.42 11.09 11.78 12.47 1.88 8.41 8.12 0.40 2.97
- 1978 4 5.08 0.60 0.00 0.00 0.00 0.00 0.00 0.20 1.59 0.80 3.09 1.79 1.99 0.20 0.70 0.10 0.30 0.20 1.20 0.80 0.00 0.00 0.00 0.20 0.50 1.40 6.08 4.29 0.50 0.10 -99.99
- 1978 5 0.00 0.39 1.38 0.69 1.08 0.00 0.00 0.00 0.00 1.28 2.86 0.79 1.48 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.69 0.89 0.89 0.00 0.10 0.20 0.00 0.00 0.00 0.00 0.30
- 1978 6 0.30 0.00 0.00 8.40 1.10 3.40 0.70 1.00 0.40 0.00 0.00 0.00 0.00 0.70 1.40 0.00 0.00 0.30 0.10 4.60 6.90 13.90 1.50 1.30 0.10 0.20 5.50 0.70 3.00 2.90 -99.99
- 1978 7 5.79 2.20 8.99 3.00 0.00 0.60 1.40 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.50 0.70 1.40 6.79 15.08 2.70 1.80 17.48 1.50 7.99 0.00 0.00 2.00 0.90
- 1978 8 0.90 6.79 2.00 0.70 1.10 8.29 1.60 2.40 0.50 0.00 6.39 3.20 5.29 12.79 3.30 1.90 0.20 2.90 4.30 9.59 16.68 0.10 0.30 0.20 0.00 0.00 0.10 0.10 0.10 6.29 0.60
- 1978 9 0.00 2.85 0.57 0.19 7.98 1.80 1.52 8.36 35.62 11.78 0.19 16.43 9.69 8.64 1.42 1.52 0.76 0.57 0.38 0.09 7.22 2.28 6.55 6.08 15.86 3.32 14.82 25.36 5.41 1.90 -99.99
- 1978 10 5.81 3.31 2.71 4.41 2.81 0.10 1.00 2.91 2.01 2.01 1.10 0.20 0.00 5.51 6.62 3.01 0.40 0.20 2.71 0.90 3.11 0.10 12.13 1.00 8.72 0.00 0.00 0.20 1.20 2.71 3.01
- 1978 11 6.60 2.80 8.60 4.40 0.00 0.00 3.50 0.00 0.70 3.20 0.40 17.99 27.19 29.29 18.19 9.60 11.79 2.20 5.20 11.69 11.09 9.60 6.00 3.80 1.50 0.10 0.00 0.00 7.00 1.00 -99.99
- 1978 12 5.80 7.01 8.81 8.91 0.00 0.00 15.81 14.71 5.20 4.40 9.11 4.60 0.40 0.30 0.70 0.00 0.10 0.40 0.10 2.60 4.60 1.10 0.10 10.01 9.11 3.10 10.91 4.30 3.00 1.40 2.40
- 1979 1 9.30 3.49 2.71 1.55 5.14 8.14 6.10 6.98 10.85 1.07 2.03 1.07 1.26 8.04 4.55 0.58 0.19 0.19 11.82 5.33 0.00 0.00 1.55 2.23 1.65 1.16 1.55 4.36 0.48 5.04 4.07
- 1979 2 0.20 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.40 0.90 0.80 0.10 0.20 0.90 0.20 0.40 3.30 1.90 0.00 0.00 2.10 3.40 8.70 2.90 -99.99 -99.99 -99.99
- 1979 3 18.00 16.30 5.40 3.00 4.50 14.40 1.70 21.40 5.90 12.90 7.60 1.20 0.70 0.30 1.40 0.30 0.80 0.20 0.00 3.70 3.50 0.40 0.00 20.70 12.20 2.60 0.30 2.30 0.50 1.50 1.30
- 1979 4 3.51 0.30 1.40 0.90 0.00 1.60 2.01 0.50 5.21 12.83 6.62 8.52 0.20 3.11 0.00 0.00 0.00 1.00 3.61 2.81 1.50 12.83 4.51 0.80 0.10 0.00 0.80 0.60 2.61 0.30 -99.99
- 1979 5 0.39 0.39 0.30 0.69 1.38 1.67 0.39 0.30 0.00 6.88 1.08 1.38 0.10 4.43 1.57 8.26 4.92 0.59 0.49 1.28 2.56 2.46 2.75 1.38 1.28 1.87 4.13 7.08 1.57 3.74 0.10
- 1979 6 0.00 0.00 0.88 1.66 0.10 7.21 4.48 3.12 0.00 0.10 0.10 4.09 2.92 2.43 0.19 0.00 0.00 0.00 0.00 3.21 4.48 7.01 2.24 0.97 1.27 0.00 5.16 2.05 2.24 0.00 -99.99
- 1979 7 0.09 0.19 0.00 0.00 0.00 4.78 0.66 8.24 0.19 0.00 0.00 3.18 0.47 1.12 8.61 3.46 5.06 0.56 1.59 2.25 0.37 0.19 0.75 6.65 5.34 1.69 1.78 12.08 1.59 7.68 2.34
- 1979 8 0.68 0.19 0.19 4.07 24.12 20.83 0.39 6.20 0.00 1.45 3.49 17.05 14.53 3.88 5.04 8.82 1.45 0.29 1.84 8.14 5.43 9.30 1.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 13.76
- 1979 9 12.83 5.23 0.86 0.38 0.86 0.76 3.23 3.33 0.76 4.28 3.04 2.85 2.38 0.29 0.00 7.98 14.35 0.48 9.60 2.38 0.57 10.36 1.14 15.21 7.51 3.14 1.14 0.00 0.00 0.19 -99.99
- 1979 10 0.90 1.50 17.79 8.49 0.00 3.20 3.60 5.60 0.40 4.50 7.80 0.00 12.79 6.30 1.00 0.80 11.29 17.19 2.70 1.20 0.00 0.80 0.00 1.70 6.50 0.60 3.70 2.40 11.29 25.48 7.20
- 1979 11 2.74 22.21 13.31 12.82 9.20 1.76 7.24 9.98 1.47 9.98 11.94 0.00 5.09 3.13 1.08 2.45 14.97 2.54 0.20 3.33 4.11 5.58 5.58 22.79 29.55 1.17 5.19 7.63 5.28 7.83 -99.99
- 1979 12 9.20 5.90 8.30 8.40 2.10 12.09 34.18 9.60 15.89 2.80 0.50 10.19 0.60 0.50 2.60 10.09 12.29 0.70 0.00 0.30 0.10 0.10 1.10 0.20 11.29 28.19 1.50 3.70 0.60 0.30 0.30
- 1980 1 0.00 7.20 22.80 6.00 0.90 0.10 0.00 0.40 4.10 2.00 0.40 0.10 5.00 0.00 0.00 0.00 0.00 0.00 4.40 2.90 8.50 1.20 1.00 0.10 0.00 0.00 8.10 2.70 10.20 3.30 1.00
- 1980 2 5.01 0.90 0.60 7.71 1.60 0.70 8.91 2.50 7.21 1.70 13.11 11.01 0.70 7.61 0.40 2.50 1.60 9.51 2.10 3.80 7.81 0.60 0.00 0.00 0.00 0.00 0.30 0.20 0.00 -99.99 -99.99
- 1980 3 0.20 0.20 0.00 3.89 2.50 6.99 1.00 1.30 4.49 2.79 8.28 0.30 0.60 0.00 0.00 12.48 3.89 0.40 0.20 5.49 3.59 1.10 5.99 9.88 4.19 5.69 2.20 2.59 0.00 3.59 5.99
- 1980 4 2.20 0.80 0.00 0.00 0.00 0.10 0.20 0.00 0.00 0.00 0.00 0.00 1.90 3.50 0.00 0.60 0.00 0.00 0.00 0.00 0.30 0.40 0.00 0.00 0.00 0.00 0.60 0.00 0.30 0.10 -99.99
- 1980 5 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.40 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.02 2.91 0.00 0.00 0.00 2.41 1.61 0.40 1.81 1.00 0.90 1.51 2.41
- 1980 6 0.10 8.03 0.80 7.22 10.43 1.91 9.23 0.70 0.80 0.80 2.01 0.10 0.60 23.47 2.31 5.12 0.10 7.42 11.03 2.21 2.71 5.22 6.42 2.71 5.12 0.00 4.21 1.50 1.10 3.61 -99.99
- 1980 7 0.00 0.80 10.21 4.30 0.50 0.10 0.10 1.20 0.00 0.50 1.70 1.60 0.30 1.30 0.20 0.90 14.41 4.90 4.90 0.00 7.21 17.01 4.60 1.10 12.21 17.41 0.10 0.10 0.00 15.81 0.10
- 1980 8 0.60 9.90 7.20 10.80 0.40 0.30 3.70 0.00 0.60 5.50 10.70 0.30 7.10 13.60 0.70 3.70 2.90 0.60 14.30 6.60 0.10 0.00 0.00 0.40 0.00 0.00 3.10 5.70 40.60 0.30 0.20
- 1980 9 2.20 3.60 2.50 8.99 6.69 9.29 4.00 2.90 6.49 8.29 24.37 7.39 13.88 4.40 5.69 4.50 4.79 7.09 0.70 0.10 0.70 5.29 4.50 1.80 8.69 20.38 0.30 2.40 4.10 5.09 -99.99
- 1980 10 0.50 1.60 16.78 1.40 10.09 28.57 9.09 3.70 1.20 0.20 0.00 0.30 1.80 0.40 0.80 2.10 0.10 5.89 0.10 8.09 14.48 14.68 28.67 5.79 3.00 7.49 1.20 6.39 0.00 0.70 0.00
- 1980 11 0.40 0.00 0.00 0.30 1.40 1.10 0.60 0.30 0.00 0.80 0.00 2.40 21.56 6.39 8.78 13.67 4.39 9.58 13.07 12.17 10.58 1.60 7.09 13.87 4.39 6.99 3.19 0.30 0.00 0.50 -99.99
- 1980 12 3.30 0.20 0.40 1.20 0.70 0.00 0.20 0.80 23.61 15.51 5.60 19.51 16.31 12.51 0.60 9.50 11.11 3.90 10.10 1.20 4.10 10.50 17.61 9.80 10.70 1.10 2.90 3.50 3.20 11.11 6.60
- 1981 1 18.20 17.90 2.90 0.10 6.00 2.50 1.00 6.20 1.10 0.30 5.80 0.30 17.60 5.00 0.90 17.20 0.70 10.30 2.10 5.10 1.70 2.30 2.10 2.00 5.50 0.60 0.00 1.90 1.20 0.00 0.10
- 1981 2 5.99 26.47 5.79 1.40 2.80 8.89 8.09 0.70 0.30 0.30 3.20 3.90 0.00 0.30 0.60 0.00 0.00 0.00 0.10 0.30 3.00 0.10 0.00 0.00 0.00 0.00 4.49 3.50 -99.99 -99.99 -99.99
- 1981 3 6.40 1.10 0.10 0.00 18.29 16.99 13.49 1.50 11.99 15.69 2.50 0.20 0.30 2.60 0.40 0.00 3.10 1.60 6.40 6.70 4.70 0.40 8.09 15.99 7.00 1.20 6.50 7.70 0.00 0.00 0.00
- 1981 4 0.00 0.00 0.00 0.00 0.00 0.40 1.20 0.00 0.00 2.80 2.20 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.00 7.20 0.40 0.00 1.50 0.70 3.80 0.10 -99.99
- 1981 5 0.00 9.20 6.20 0.80 4.70 5.40 9.30 0.00 3.60 1.10 0.00 3.90 0.80 0.60 1.70 4.70 0.90 7.20 0.10 7.50 0.80 6.20 3.80 0.50 0.00 1.30 5.60 0.20 1.10 1.20 3.80
- 1981 6 0.20 11.10 8.40 4.10 5.80 2.90 10.70 11.60 2.00 7.40 0.90 10.90 12.70 0.90 1.90 0.80 0.40 3.10 0.10 0.00 0.10 0.20 0.20 0.90 0.10 0.20 0.00 0.00 1.50 2.80 -99.99
- 1981 7 2.30 1.90 0.60 2.10 9.62 4.11 0.10 0.80 0.00 14.63 2.10 0.00 0.60 0.50 3.81 3.21 5.11 1.80 2.30 1.10 19.44 9.72 0.30 2.30 1.80 0.20 2.10 1.80 0.00 0.00 0.00
- 1981 8 0.00 2.81 6.92 0.00 0.10 0.00 0.20 1.60 0.00 0.90 1.20 1.20 1.20 0.10 0.60 0.30 3.71 3.11 9.62 0.40 0.40 0.00 0.50 3.21 0.10 0.00 0.00 0.00 0.40 0.00 0.00
- 1981 9 0.00 0.00 6.80 17.01 0.80 0.10 2.10 0.40 2.90 13.91 0.30 0.50 0.00 18.91 0.70 17.01 21.81 3.90 23.11 6.80 2.40 2.20 32.91 3.40 7.40 42.92 11.40 3.20 3.60 4.60 -99.99
- 1981 10 32.15 14.52 8.61 2.50 0.90 8.21 6.41 19.63 9.71 4.61 2.60 1.90 1.30 0.60 1.80 0.70 0.50 14.42 2.00 1.00 0.10 0.90 3.61 3.71 0.10 9.41 7.51 13.42 11.82 6.31 7.31
- 1981 11 18.30 6.70 8.50 0.00 0.00 0.00 0.00 1.30 7.70 10.60 0.80 1.00 1.00 0.80 9.10 6.20 5.20 8.30 15.10 4.10 12.60 13.50 3.90 3.30 10.80 14.70 6.80 1.10 12.90 3.00 -99.99
- 1981 12 0.10 0.60 7.69 0.70 1.00 0.60 0.10 0.00 0.10 0.00 0.10 0.40 8.19 2.00 0.00 0.00 0.00 0.00 9.98 11.38 0.80 0.00 0.10 0.70 0.90 0.00 2.10 0.60 1.90 6.09 0.20
- 1982 1 1.87 29.53 29.86 13.39 6.04 0.00 0.00 0.55 0.00 0.00 0.00 0.11 0.00 0.00 0.66 1.21 1.10 3.51 5.16 7.46 12.29 4.61 0.66 1.54 14.16 0.33 3.29 4.17 3.62 3.29 1.10
- 1982 2 6.01 0.00 0.20 7.91 6.11 4.70 6.81 15.81 8.61 4.30 2.60 8.31 2.90 0.00 0.00 0.00 0.30 1.00 0.00 0.10 2.70 0.10 0.20 8.41 8.81 1.70 2.50 12.91 -99.99 -99.99 -99.99
- 1982 3 8.40 16.30 5.50 0.00 8.80 4.90 5.90 4.00 24.90 6.80 20.60 4.30 5.20 6.90 10.60 5.80 0.20 0.00 3.70 4.80 2.30 0.00 0.00 0.00 0.00 0.00 0.00 0.90 0.00 0.00 0.60
- 1982 4 1.10 0.20 5.10 2.20 3.30 6.40 3.00 0.60 1.00 0.00 0.00 0.00 0.20 0.10 0.30 0.00 0.00 0.00 0.00 0.00 0.30 3.20 0.20 0.00 0.00 0.00 0.00 0.60 0.90 4.10 -99.99
- 1982 5 4.00 28.87 5.09 2.00 7.89 0.30 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 2.30 0.20 0.80 1.00 0.20 0.00 11.19 5.99 2.30 2.60 2.40 1.10 3.70 0.30 0.00 0.00 0.90
- 1982 6 0.00 0.00 0.20 0.00 1.60 7.51 0.00 0.00 3.10 2.70 20.72 0.30 0.50 0.00 14.92 0.00 0.00 0.00 1.20 0.60 0.00 1.00 0.00 2.50 12.11 4.00 8.41 4.10 0.40 6.61 -99.99
- 1982 7 0.80 2.80 1.20 4.90 3.60 1.10 0.50 2.70 0.90 0.60 0.20 0.70 0.20 9.90 8.40 0.90 0.10 0.00 0.00 0.00 0.00 0.00 0.00 2.20 0.00 0.00 0.00 0.00 0.00 0.00 7.10
- 1982 8 3.20 0.10 0.00 0.50 2.80 0.70 2.10 1.60 0.80 0.90 3.60 4.20 1.80 0.00 7.29 5.60 15.19 9.09 9.49 2.40 9.79 4.30 9.99 6.99 5.50 2.30 0.00 12.99 8.99 0.10 0.60
- 1982 9 0.10 0.30 2.10 21.71 6.00 8.00 3.80 1.10 6.90 8.70 3.80 1.20 0.10 0.00 0.90 0.10 0.20 1.80 9.81 9.81 0.80 5.50 3.20 23.31 8.20 11.91 25.51 9.61 2.40 9.10 -99.99
- 1982 10 21.08 0.19 3.63 6.49 1.43 0.67 0.95 0.00 1.53 2.48 1.34 12.12 4.96 0.00 4.39 8.01 17.36 6.77 16.12 4.10 1.72 1.53 3.34 1.91 3.72 3.43 0.00 0.19 14.79 23.75 6.11
- 1982 11 1.50 0.20 0.70 1.80 26.41 5.70 7.00 1.70 8.70 12.61 16.41 3.30 11.61 2.70 13.01 11.41 14.41 14.11 8.00 9.60 6.90 12.81 19.81 8.20 0.60 0.40 2.90 0.30 0.00 0.00 -99.99
- 1982 12 0.00 0.00 1.10 11.31 2.60 0.10 22.11 4.30 9.90 0.60 0.20 0.20 1.90 12.31 11.71 10.30 2.80 28.51 18.31 10.61 1.20 0.90 10.51 1.80 4.60 6.00 5.20 1.60 3.30 8.70 15.71
- 1983 1 11.57 15.50 8.99 11.26 14.88 6.51 5.99 9.40 2.58 3.82 15.29 7.03 10.54 3.82 1.34 4.44 9.20 2.38 0.93 3.31 1.24 0.10 10.44 0.72 1.65 9.61 8.89 9.51 6.51 6.10 22.32
- 1983 2 0.20 3.11 0.00 17.74 2.81 0.00 1.20 0.50 3.81 0.00 0.30 0.30 0.10 0.30 0.00 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.50 8.92 6.81 0.70 -99.99 -99.99 -99.99
- 1983 3 4.81 17.03 1.50 1.20 5.31 0.90 0.00 0.00 0.10 1.60 1.60 5.11 7.61 1.00 8.22 6.01 4.91 17.53 9.62 11.72 9.62 6.01 4.51 3.91 0.30 4.91 0.10 3.71 9.62 6.61 1.90
- 1983 4 0.80 0.10 5.10 3.20 0.90 0.20 1.50 0.30 0.30 0.40 0.20 1.90 0.80 0.10 4.00 8.60 1.40 1.90 1.70 2.10 0.00 5.50 3.60 1.60 0.10 0.50 0.90 1.80 0.30 0.00 -99.99
- 1983 5 0.80 1.20 0.00 0.00 21.72 4.50 2.10 6.81 3.00 9.21 5.61 9.71 7.91 2.40 2.20 2.30 1.90 0.50 2.70 5.51 2.40 0.80 0.00 0.80 0.10 0.00 2.80 0.80 0.70 2.40 0.70
- 1983 6 18.20 1.40 9.70 5.20 0.10 0.00 0.80 4.80 0.20 10.10 0.60 1.60 9.30 3.00 2.50 3.40 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.40 0.40 4.70 0.00 0.00 -99.99
- 1983 7 15.90 2.10 0.00 1.10 0.20 1.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.20 0.00 0.50 0.00 0.00 0.00 0.60 4.40 5.60 0.10 0.10 0.30 0.20 2.30 0.20
- 1983 8 0.90 0.80 6.77 0.00 0.10 0.10 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.10 2.79 0.10 7.36 0.10 0.00 1.59 1.29 4.38 9.45 0.00 0.00 0.20 0.00 0.00 0.00 0.00 3.58
- 1983 9 3.00 18.00 1.20 9.00 5.50 0.30 4.10 12.90 11.50 3.00 0.00 0.00 3.50 11.90 11.10 2.60 8.70 10.50 13.60 5.40 1.90 11.90 0.60 0.10 0.10 0.90 0.30 0.90 5.10 2.10 -99.99
- 1983 10 6.01 5.31 16.52 17.02 8.01 8.41 13.32 10.11 13.02 13.92 21.23 8.21 4.91 14.32 20.12 11.41 12.21 13.02 1.10 0.00 0.00 3.50 3.60 1.10 4.00 2.90 3.20 0.20 4.41 1.40 4.71
- 1983 11 1.92 2.12 0.30 0.10 0.30 0.61 0.51 0.30 0.00 0.20 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.71 2.43 0.00 0.00 1.52 0.51 6.16 7.38 6.97 3.84 1.62 0.00 0.71 -99.99
- 1983 12 0.70 0.90 1.80 6.60 0.20 0.10 6.60 6.10 0.30 0.00 5.70 14.81 19.91 1.40 0.00 3.30 8.40 0.70 2.10 5.10 0.30 6.80 7.80 14.31 6.20 10.91 14.11 2.50 4.30 10.61 15.61
- 1984 1 9.95 15.81 1.68 8.27 3.56 6.91 1.89 0.10 3.56 11.10 11.83 28.69 14.77 11.94 9.22 20.11 9.84 2.30 0.00 0.00 13.30 7.23 15.81 4.40 3.04 2.20 0.94 7.23 1.99 5.55 1.68
- 1984 2 14.62 7.51 12.92 20.43 14.62 11.82 13.42 0.30 0.70 1.60 0.90 0.00 0.60 0.10 0.00 4.21 1.40 0.20 0.30 2.90 5.41 0.80 3.61 1.40 0.50 0.00 0.00 1.20 0.90 -99.99 -99.99
- 1984 3 10.98 0.10 8.29 0.60 0.20 0.00 0.10 1.00 0.00 1.20 6.09 1.00 0.30 0.10 0.00 0.00 0.00 0.00 0.00 0.10 0.70 0.10 10.98 12.18 3.69 2.00 2.40 1.20 6.59 0.20 0.40
- 1984 4 0.00 0.00 0.00 0.30 0.00 0.40 0.00 0.00 1.00 7.30 1.90 0.60 0.40 1.60 1.50 0.10 10.10 12.40 1.90 4.20 1.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1984 5 0.00 0.00 0.00 0.35 0.00 0.00 0.12 0.00 0.00 0.24 0.00 0.00 0.00 1.54 0.12 1.54 0.47 0.00 0.12 0.59 2.25 0.24 0.24 2.25 3.07 1.66 0.00 0.00 0.71 0.71 2.60
- 1984 6 7.81 0.00 6.81 2.20 5.01 0.70 0.00 0.00 0.00 0.00 1.10 4.11 2.30 0.10 0.00 2.91 0.10 0.10 0.60 1.50 11.82 0.50 2.80 3.01 0.80 1.90 0.10 0.00 0.00 0.00 -99.99
- 1984 7 0.10 0.00 0.00 0.00 0.00 1.10 0.00 0.00 0.30 2.81 3.51 0.50 0.70 0.70 0.00 0.60 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.14 4.41 0.20 4.81 1.20
- 1984 8 2.20 5.71 0.10 0.00 2.20 4.81 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.80 1.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.30 12.33 1.00 9.02 6.01
- 1984 9 24.93 6.11 3.81 0.10 0.00 0.00 0.10 9.71 1.80 1.80 0.70 4.81 12.92 0.50 1.00 3.30 0.20 3.91 3.00 9.41 7.51 6.21 1.50 0.10 0.00 6.01 13.52 5.21 12.12 6.11 -99.99
- 1984 10 1.20 3.40 11.29 0.20 0.70 1.30 6.39 2.10 1.00 5.60 0.40 12.19 5.89 1.40 0.00 3.60 23.58 18.68 12.49 6.29 26.68 2.50 4.40 35.27 2.40 1.40 6.09 12.59 12.69 5.79 1.70
- 1984 11 12.80 2.80 23.30 0.00 0.00 2.60 2.90 14.50 16.90 2.00 13.80 0.70 3.50 1.40 1.70 1.40 1.40 0.70 0.00 1.00 16.20 4.20 9.50 11.10 4.20 13.30 24.50 2.10 2.70 5.10 -99.99
- 1984 12 7.31 2.00 4.11 9.81 6.51 10.01 8.71 5.21 0.90 0.20 0.00 0.00 4.21 1.80 0.20 7.71 7.61 8.21 19.52 7.31 4.81 10.81 8.41 5.81 4.71 0.00 0.40 5.91 4.31 1.90 0.00
- 1985 1 0.20 0.00 0.00 0.50 0.00 0.10 0.20 0.40 0.00 0.00 0.00 0.00 0.60 0.60 0.50 4.79 2.80 0.00 0.20 1.50 16.27 0.80 0.50 1.30 0.10 0.10 8.19 3.59 0.30 11.58 6.19
- 1985 2 3.61 1.70 0.60 0.00 0.00 0.40 2.01 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.30 0.80 6.92 6.12 0.10 0.10 14.04 2.51 0.00 0.20 0.00 0.00 0.00 -99.99 -99.99 -99.99
- 1985 3 0.40 0.40 11.39 1.00 0.20 2.00 0.30 0.70 2.50 0.20 0.70 1.60 1.20 1.70 1.40 0.80 0.50 0.00 0.00 0.00 0.10 0.80 3.60 6.49 0.50 0.50 0.70 4.20 21.38 9.99 13.38
- 1985 4 14.44 5.97 5.28 7.37 3.48 1.59 1.49 1.00 0.40 8.66 2.09 6.67 4.08 1.79 3.98 1.49 0.00 1.99 0.40 0.10 0.10 0.00 0.00 0.30 0.40 1.79 0.10 9.66 1.39 2.89 -99.99
- 1985 5 0.30 0.00 0.00 0.00 2.20 0.90 0.00 0.10 0.60 0.30 0.00 0.00 0.00 7.20 2.70 0.20 0.90 5.50 0.10 0.00 0.00 0.00 15.10 4.50 11.60 0.90 1.50 0.60 0.00 0.00 0.00
- 1985 6 0.00 0.00 0.00 0.00 0.40 1.90 2.30 6.30 0.60 2.50 7.40 1.30 2.10 0.40 0.00 0.00 4.70 0.40 1.70 2.20 4.40 4.40 12.00 1.10 4.50 1.20 2.80 1.20 0.10 0.50 -99.99
- 1985 7 0.19 0.10 0.78 9.32 5.53 0.39 5.24 2.91 0.00 3.88 15.05 6.51 4.27 0.10 13.79 7.48 20.00 4.85 3.98 4.85 10.00 12.23 10.49 2.04 9.22 20.78 1.75 14.08 0.58 0.00 0.00
- 1985 8 22.50 8.20 6.80 0.80 0.20 1.20 2.90 6.70 2.30 2.10 12.30 6.30 4.80 24.20 23.30 4.20 2.60 7.10 6.20 11.80 10.40 1.90 23.40 6.60 0.70 14.00 17.90 1.60 3.10 3.70 17.00
- 1985 9 0.20 18.00 0.10 5.60 1.30 5.10 18.10 1.10 0.90 0.00 0.00 6.50 10.40 16.20 5.40 7.40 5.00 36.60 1.40 41.30 27.90 23.10 2.60 0.10 5.20 1.40 0.00 0.00 1.00 30.60 -99.99
- 1985 10 4.09 12.77 11.50 8.77 12.87 3.12 5.95 4.48 2.92 6.04 0.19 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.10 0.10 0.00 0.68 0.00
- 1985 11 1.30 1.00 1.20 16.94 2.61 11.33 5.71 11.33 6.72 0.70 0.10 0.00 2.31 7.42 10.33 1.70 0.00 0.00 0.10 0.30 0.40 0.20 0.10 0.30 0.00 0.00 1.80 1.80 0.00 32.18 -99.99
- 1985 12 8.69 10.39 4.30 1.80 3.90 10.49 17.48 3.30 0.40 2.90 2.50 11.79 0.30 3.00 2.50 4.70 8.19 6.89 14.98 31.27 4.20 4.20 3.80 0.40 1.70 0.00 0.10 0.30 0.80 12.09 12.19
- 1986 1 1.00 0.50 0.10 8.20 0.40 0.00 1.10 4.20 19.30 8.20 7.20 11.90 16.10 5.80 0.00 0.00 11.30 11.60 8.20 12.10 10.50 11.60 1.30 0.00 0.50 3.20 2.10 3.30 3.30 0.90 0.40
- 1986 2 0.18 0.54 0.54 0.00 1.61 1.52 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.09 0.45 0.00 0.09 0.09 1.34 2.96 0.18 0.00 0.00 0.00 0.00 0.18 0.00 -99.99 -99.99 -99.99
- 1986 3 0.00 0.00 11.63 14.56 5.98 1.41 0.11 2.93 3.69 0.54 5.22 0.11 6.52 4.35 5.87 3.48 4.24 5.65 13.15 10.76 15.76 18.58 6.09 2.72 1.96 7.71 3.15 3.15 5.54 1.85 1.41
- 1986 4 1.15 1.25 0.10 0.31 0.00 0.42 0.21 0.00 0.42 0.00 0.10 0.83 2.61 2.61 6.36 1.67 0.10 0.83 17.62 0.83 1.15 4.17 2.19 0.10 0.42 1.98 4.28 3.75 17.62 2.40 -99.99
- 1986 5 0.00 1.20 4.60 2.20 9.70 10.99 14.59 1.00 17.39 9.50 6.50 11.99 5.10 3.30 1.10 0.40 19.19 1.00 1.00 8.40 7.50 5.10 2.30 9.99 11.09 13.99 4.70 1.50 0.10 6.00 4.10
- 1986 6 0.70 1.90 0.60 0.80 0.00 0.00 0.10 16.40 5.00 12.30 0.00 11.10 0.50 0.00 0.00 7.60 8.30 0.00 0.00 0.00 0.00 0.80 0.40 1.50 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1986 7 0.31 0.62 3.59 1.64 0.62 0.31 2.97 0.00 0.31 0.10 0.00 0.00 0.51 3.59 1.23 1.74 0.72 0.92 4.00 0.41 0.82 0.10 0.00 5.44 1.13 6.87 7.90 17.23 0.51 19.49 0.82
- 1986 8 15.37 4.63 2.07 3.35 13.10 13.00 1.67 0.00 0.00 0.00 0.00 3.64 21.87 12.41 6.11 0.30 0.99 2.56 0.89 0.00 3.25 0.00 0.00 0.00 5.71 0.89 0.49 0.00 0.10 0.00 6.70
- 1986 9 0.20 17.20 0.00 0.90 6.20 0.10 0.00 0.00 0.00 0.00 0.60 0.00 0.10 0.10 0.00 0.20 0.10 0.00 0.50 1.30 6.60 0.50 0.00 0.00 1.50 1.70 4.20 3.10 0.20 0.60 -99.99
- 1986 10 3.86 0.00 0.29 0.87 1.55 4.83 0.19 6.18 0.68 0.58 0.00 0.00 0.77 0.77 0.10 0.00 3.38 10.14 13.42 15.16 12.65 12.26 2.32 20.95 0.77 23.27 6.95 9.85 15.16 6.86 0.39
- 1986 11 0.00 6.54 1.44 18.86 1.06 13.57 13.09 12.22 14.24 2.60 2.79 1.83 6.35 11.26 20.21 4.43 8.47 3.08 3.75 3.95 9.43 17.51 4.04 39.93 3.75 4.62 0.38 1.44 1.35 4.91 -99.99
- 1986 12 6.80 26.81 10.80 24.61 3.00 5.90 10.40 14.51 2.00 15.31 2.40 10.40 1.80 16.11 4.40 10.20 15.51 8.50 7.10 0.00 0.00 0.00 2.00 8.70 2.30 5.10 6.10 16.21 13.00 21.91 6.90
- 1987 1 12.88 0.00 11.78 7.89 2.90 0.00 0.00 0.00 0.00 0.30 0.50 4.39 1.40 0.70 0.30 0.00 0.80 9.59 16.38 1.30 0.80 0.00 0.00 0.00 0.10 0.10 0.40 0.00 0.00 0.00 0.00
- 1987 2 4.30 3.30 0.00 10.70 9.10 1.00 6.80 0.40 20.60 1.30 0.70 0.00 0.00 0.00 0.00 0.00 0.00 1.60 0.30 0.00 0.00 0.40 0.00 0.00 1.80 12.80 5.80 4.20 -99.99 -99.99 -99.99
- 1987 3 22.20 0.00 5.20 3.10 12.00 5.10 6.40 0.00 0.00 0.00 0.00 0.00 3.90 1.90 0.40 10.30 3.10 1.10 0.50 0.60 1.90 0.90 0.00 13.60 3.50 25.90 12.80 0.20 1.10 3.60 5.40
- 1987 4 5.26 0.10 0.31 0.10 4.23 0.72 5.57 4.13 3.20 13.83 0.10 5.16 0.21 0.10 0.10 0.00 0.10 2.27 9.39 1.45 0.00 0.52 0.00 0.00 0.00 0.10 0.00 0.00 0.21 6.81 -99.99
- 1987 5 7.92 1.10 0.00 0.00 0.00 0.00 0.00 0.00 0.20 6.32 4.81 1.10 6.02 1.60 0.00 5.52 1.81 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 22.37 1.60 4.31
- 1987 6 0.10 8.19 1.80 0.20 27.47 11.29 1.30 0.40 0.10 3.10 3.70 2.50 1.30 5.10 1.30 0.10 0.00 0.00 0.00 0.10 13.99 3.40 3.50 0.00 0.20 0.30 11.99 1.00 0.50 3.40 -99.99
- 1987 7 0.30 0.00 0.70 0.30 0.60 0.00 0.40 0.10 17.42 22.52 1.40 0.80 0.00 2.50 7.41 3.00 3.00 3.70 1.70 0.10 0.00 0.00 0.90 0.10 4.50 11.61 0.70 2.50 1.20 3.60 2.10
- 1987 8 0.19 0.48 1.16 0.10 0.19 0.77 0.10 0.10 0.00 0.29 5.50 17.95 2.22 1.25 26.92 12.26 3.96 0.00 8.49 22.87 0.00 0.77 1.16 0.00 1.93 0.97 1.16 3.86 0.29 0.00 11.97
- 1987 9 0.09 0.74 2.23 10.57 2.97 5.66 2.78 4.17 16.69 5.38 15.39 4.36 2.41 6.86 1.48 0.28 1.76 0.00 8.53 10.75 15.02 4.82 3.80 2.23 0.93 0.74 0.09 0.00 0.00 0.28 -99.99
- 1987 10 0.00 0.00 0.00 1.07 15.34 3.91 16.60 3.61 2.34 0.10 5.47 3.42 3.42 1.76 11.14 12.99 15.43 4.69 13.28 7.23 14.46 2.25 0.10 2.15 5.08 7.23 4.49 0.00 0.00 1.95 0.20
- 1987 11 0.28 0.00 0.00 0.00 0.00 0.00 1.86 3.35 4.65 9.49 10.42 13.30 0.74 10.42 10.98 4.74 3.07 11.16 4.84 1.12 3.16 1.02 0.65 0.37 0.19 0.37 0.09 2.33 1.40 0.00 -99.99
- 1987 12 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 4.38 4.29 5.13 4.29 20.61 10.63 3.08 0.09 0.75 4.20 15.01 5.03 23.03 16.32 4.38 12.96 8.86
- 1988 1 17.48 7.26 6.11 3.26 4.11 0.84 4.42 7.79 6.74 7.90 14.63 10.74 0.84 4.53 1.26 4.00 2.74 19.05 5.05 4.84 6.21 4.32 15.90 2.74 6.32 0.21 0.00 3.79 1.89 10.95 17.69
- 1988 2 19.32 16.68 4.37 0.71 2.95 2.03 14.44 10.48 17.49 2.95 2.54 5.80 5.59 8.44 14.75 0.61 2.03 0.61 1.22 0.71 0.51 0.61 0.00 0.10 0.81 0.81 0.10 0.10 0.00 -99.99 -99.99
- 1988 3 0.00 7.24 0.00 0.00 2.35 0.61 0.92 1.53 2.35 2.65 5.92 5.10 0.10 7.45 25.71 0.10 0.00 26.02 0.20 1.94 0.41 11.22 10.41 10.10 11.84 0.82 12.04 5.61 6.94 2.55 3.47
- 1988 4 4.94 0.20 0.00 0.00 0.00 0.00 2.17 2.07 1.78 3.75 1.68 0.49 0.00 4.05 11.07 5.34 10.08 22.53 0.30 4.74 0.59 0.20 0.00 0.20 1.38 0.30 0.00 0.00 0.00 4.94 -99.99
- 1988 5 3.73 2.07 6.22 0.83 0.31 0.00 0.62 1.45 0.00 0.00 0.62 1.24 0.31 0.00 0.00 0.00 0.31 0.83 0.21 0.10 0.00 0.00 10.88 7.87 0.00 5.08 0.00 0.10 12.95 1.04 1.04
- 1988 6 1.31 4.85 3.34 0.20 0.30 9.50 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.20 0.00 0.10 0.30 1.21 1.31 0.00 0.00 0.20 3.03 0.00 0.00 0.00 0.20 2.22 -99.99
- 1988 7 3.95 5.82 0.83 2.91 3.12 4.15 6.75 4.57 24.30 5.82 2.80 11.32 4.36 0.83 1.45 13.09 1.14 4.47 0.52 4.78 8.62 12.77 11.11 10.39 11.11 5.19 4.67 11.53 6.75 3.12 1.87
- 1988 8 0.95 1.69 0.53 0.63 4.97 1.06 0.00 7.93 1.06 6.35 18.30 4.34 23.70 5.40 0.21 0.00 21.79 9.42 10.16 0.53 0.00 0.63 4.02 7.62 2.01 11.00 1.38 9.10 6.03 9.10 7.19
- 1988 9 13.81 12.25 2.49 17.44 0.00 19.21 5.50 1.56 1.14 1.87 10.07 0.93 0.21 0.00 0.00 0.00 0.21 0.93 0.10 0.42 3.53 19.93 9.55 0.52 15.47 3.84 6.96 3.53 0.52 0.21 -99.99
- 1988 10 9.67 6.56 6.45 7.42 11.61 22.25 4.19 9.24 4.19 0.00 1.83 3.01 1.40 0.00 0.00 0.00 0.00 25.36 13.65 5.16 4.41 2.04 7.95 7.20 27.51 0.54 1.07 0.11 0.00 0.00 0.00
- 1988 11 0.00 0.00 0.00 3.76 0.00 0.42 0.10 14.43 12.13 5.33 0.84 4.50 1.57 0.10 0.00 1.88 6.06 0.10 9.83 0.10 0.31 0.10 1.67 1.36 0.00 0.00 11.40 0.21 20.39 2.20 -99.99
- 1988 12 0.00 0.73 24.23 6.06 3.45 0.31 2.82 6.27 5.33 1.36 0.00 0.31 0.10 0.21 1.88 0.63 3.55 19.42 3.55 3.03 6.16 9.71 5.01 4.70 15.35 8.77 2.09 0.42 0.10 0.84 0.21
- 1989 1 0.30 0.10 7.96 8.36 13.70 0.91 2.02 11.89 2.22 5.04 24.08 4.84 15.42 4.63 0.60 2.42 1.31 0.00 0.30 11.59 2.32 6.05 0.71 3.73 5.84 5.64 17.13 0.20 0.30 0.30 0.00
- 1989 2 2.19 11.85 14.48 15.36 2.63 9.76 2.52 0.22 6.69 3.29 18.32 8.23 9.00 16.02 2.30 1.43 6.69 11.41 6.69 9.10 5.81 5.70 2.63 8.78 1.21 2.19 5.05 5.05 -99.99 -99.99 -99.99
- 1989 3 0.50 3.23 1.41 2.42 3.13 4.13 0.20 13.91 20.77 0.91 1.31 11.80 6.75 6.55 1.31 0.10 7.86 11.90 13.61 7.26 13.31 9.48 17.14 8.57 5.44 1.92 6.55 0.50 14.42 0.40 0.40
- 1989 4 0.60 0.90 0.40 2.31 4.31 6.92 3.81 1.60 5.72 3.81 13.44 1.30 9.93 1.70 0.20 0.40 0.00 0.00 0.10 0.10 3.41 0.50 0.20 0.30 0.60 2.01 0.10 1.60 0.30 5.31 -99.99
- 1989 5 0.70 4.39 0.50 0.00 0.00 0.00 0.00 0.30 0.30 3.49 8.28 0.30 0.00 0.10 5.39 0.40 2.69 3.09 0.10 0.00 0.00 0.00 0.40 5.69 0.00 0.00 0.00 0.00 0.10 0.40 2.49
- 1989 6 0.30 1.41 0.00 0.40 2.21 1.81 0.70 0.10 0.70 1.71 1.61 20.83 5.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 1.11 18.62 0.91 4.63 6.54 0.91 10.06 -99.99
- 1989 7 0.20 0.00 0.00 0.00 0.00 0.40 0.00 0.00 1.89 2.28 0.70 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.20 0.30 0.10 0.00 0.00 12.61 4.47 9.73 8.54 1.79 0.20 0.20
- 1989 8 0.88 0.88 1.07 1.46 6.54 1.07 2.83 9.18 5.08 17.67 2.25 17.47 7.61 17.18 4.39 7.03 8.20 1.76 18.94 13.86 1.76 0.49 7.71 7.22 5.76 2.73 0.00 3.42 3.32 14.25 0.98
- 1989 9 1.82 0.00 0.61 0.30 0.30 0.81 1.72 0.00 0.00 0.00 0.00 1.42 4.45 0.61 12.84 0.20 5.97 6.17 10.82 19.61 4.45 6.88 0.00 0.00 1.42 1.92 0.00 0.00 0.00 0.00 -99.99
- 1989 10 0.00 0.00 0.00 8.74 5.43 2.51 1.11 0.20 0.70 1.11 2.21 4.72 8.54 1.00 11.36 4.42 10.45 3.12 10.15 10.15 2.11 1.11 5.23 15.48 9.35 3.72 18.09 4.12 5.93 8.74 4.32
- 1989 11 6.16 5.55 8.18 5.65 0.30 3.03 2.93 1.11 5.15 7.57 0.91 3.84 0.10 0.00 0.00 0.00 1.11 0.71 0.00 0.00 0.00 0.00 0.40 0.91 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1989 12 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.10 2.91 43.29 5.11 0.10 1.40 4.71 4.61 6.31 10.02 13.53 1.80 0.00 0.00 0.00 0.00 0.00 3.51
- 1990 1 3.20 5.90 6.40 3.90 5.50 4.50 5.40 3.30 17.50 2.30 3.20 1.80 4.80 17.90 10.00 18.40 13.20 9.90 7.20 1.10 8.50 15.90 10.50 14.30 19.50 10.90 0.20 6.40 8.30 10.40 7.50
- 1990 2 10.64 8.15 8.55 7.95 9.54 16.80 6.76 5.87 2.49 11.63 18.29 5.07 12.03 5.77 0.99 10.34 12.53 9.35 4.67 3.78 1.69 0.20 18.39 24.46 22.07 8.05 5.47 7.06 -99.99 -99.99 -99.99
- 1990 3 2.50 1.00 6.41 5.31 19.22 14.02 8.51 19.12 20.52 10.11 1.90 3.80 5.41 11.51 3.60 1.20 0.70 6.21 1.50 7.81 7.41 3.10 5.61 5.01 0.10 0.00 0.70 1.10 0.10 0.00 0.00
- 1990 4 12.61 2.20 0.30 0.70 2.40 0.90 0.00 1.40 5.41 1.90 1.00 8.01 2.30 7.71 7.11 7.91 5.71 9.01 2.40 0.10 0.00 0.00 0.00 0.00 4.81 0.10 3.10 0.70 0.00 0.00 -99.99
- 1990 5 0.00 0.00 0.00 0.00 0.80 3.90 4.60 4.90 5.40 2.30 0.00 0.00 0.00 6.90 9.00 12.00 0.00 0.00 0.00 0.20 0.30 1.10 0.10 0.20 0.00 0.00 0.00 1.00 1.60 0.00 8.20
- 1990 6 3.71 1.30 4.21 4.21 5.31 24.74 6.61 2.40 0.70 0.90 0.00 0.00 0.00 0.00 0.00 0.10 1.00 6.01 0.80 7.21 3.31 1.90 0.60 8.41 0.20 17.03 1.40 1.90 5.31 11.82 -99.99
- 1990 7 1.20 0.80 5.79 14.38 0.00 8.39 10.09 6.49 1.80 2.30 0.10 2.80 0.00 0.30 5.99 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.69 0.90 3.59 0.40 0.00
- 1990 8 0.00 0.00 0.49 1.86 0.10 2.35 0.59 10.79 3.83 2.94 7.26 3.83 1.67 12.95 24.91 1.28 0.00 1.28 2.75 0.10 3.14 0.69 4.12 1.57 2.94 2.94 3.04 6.67 5.98 5.39 1.96
- 1990 9 3.91 5.11 1.40 1.30 8.22 9.82 0.40 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.51 15.63 16.73 1.60 8.02 3.91 0.40 1.40 1.90 0.00 0.00 0.30 10.12 4.91 0.30 -99.99
- 1990 10 7.70 33.01 8.80 11.60 31.21 17.31 0.20 2.20 8.00 7.80 7.20 0.50 1.70 5.40 15.91 3.20 16.91 0.10 0.10 0.10 0.00 0.00 0.10 2.60 4.70 2.70 11.00 13.81 15.31 10.10 0.70
- 1990 11 0.30 0.10 0.00 0.00 0.00 0.10 0.10 0.00 1.30 1.60 3.20 5.91 2.10 4.51 7.91 6.01 7.71 6.01 2.10 0.70 0.00 1.40 10.52 1.00 2.90 0.00 0.00 0.00 0.00 0.10 -99.99
- 1990 12 0.00 0.85 0.11 0.11 0.21 13.42 3.06 5.92 0.63 1.27 5.60 0.00 0.00 0.00 0.11 0.21 0.00 0.85 12.68 2.11 12.57 26.73 6.02 19.12 20.71 16.90 5.49 16.06 4.75 2.32 3.70
- 1991 1 31.13 6.12 8.16 16.74 14.49 8.05 9.55 5.15 10.09 5.69 4.51 0.00 0.21 0.00 0.00 0.75 0.97 8.48 5.37 3.54 0.32 1.29 1.61 0.00 0.11 0.64 0.00 2.15 3.65 0.32 0.11
- 1991 2 0.10 0.00 2.45 0.00 0.00 0.41 0.92 1.33 1.53 0.10 4.29 0.00 0.00 10.94 0.00 0.00 0.20 0.00 6.85 2.56 12.17 18.61 15.74 0.92 2.35 3.88 5.73 0.41 -99.99 -99.99 -99.99
- 1991 3 0.10 4.34 1.09 14.31 0.49 0.69 1.28 6.71 2.27 0.49 1.18 3.95 2.86 1.38 7.60 7.89 1.97 41.24 4.74 7.40 0.30 0.89 0.00 0.00 0.00 0.00 0.00 0.00 0.30 1.58 10.06
- 1991 4 27.48 3.33 8.29 6.26 3.13 13.44 5.46 0.71 20.71 6.37 12.23 16.07 0.00 0.00 0.00 0.00 0.61 0.30 0.40 2.22 0.51 0.30 1.21 1.01 0.00 0.00 0.00 1.52 4.65 0.00 -99.99
- 1991 5 0.00 0.30 1.70 0.00 0.10 1.70 0.00 0.30 0.00 1.00 0.60 6.80 2.00 0.00 0.00 0.30 0.10 1.70 1.60 1.10 0.60 0.10 0.80 0.00 0.40 0.10 0.00 0.00 0.00 0.00 0.00
- 1991 6 11.28 2.26 0.69 0.00 0.00 0.00 0.00 11.38 11.08 0.88 9.22 12.65 5.20 0.59 4.32 0.78 1.27 1.77 0.98 0.98 3.43 2.94 1.86 5.20 3.04 0.39 2.35 0.10 9.42 2.84 -99.99
- 1991 7 9.35 0.00 0.00 0.00 0.00 0.00 6.64 13.68 0.30 0.40 7.84 8.55 1.91 6.74 9.35 1.21 3.32 2.41 1.51 1.11 0.10 5.53 3.62 0.91 0.10 0.30 1.31 1.41 0.00 0.00 2.01
- 1991 8 0.90 0.50 1.11 5.73 2.41 0.70 0.20 20.30 1.61 0.80 2.01 0.50 0.80 0.80 1.11 6.23 1.31 5.02 0.20 0.10 3.01 2.71 2.21 0.10 0.00 0.00 0.20 0.00 0.00 0.00 0.00
- 1991 9 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.04 5.11 4.07 8.55 4.17 3.65 2.40 4.69 0.63 25.65 12.72 22.10 8.34 0.31 0.00 0.73 0.21 1.67 11.68 -99.99
- 1991 10 6.07 10.34 5.27 15.72 3.98 10.94 11.34 0.10 0.30 0.10 0.00 0.10 0.00 0.10 19.30 10.74 2.79 0.00 0.50 1.19 0.10 0.10 0.10 0.10 0.60 0.30 0.00 0.10 16.81 14.92 17.90
- 1991 11 17.70 19.59 5.06 0.53 3.27 19.80 18.22 3.48 1.79 27.07 4.95 32.97 2.21 0.00 0.42 1.69 8.43 4.21 1.16 4.53 4.32 2.32 2.21 8.95 2.84 1.58 8.32 5.16 1.16 1.26 -99.99
- 1991 12 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 1.64 1.13 1.43 2.25 5.63 16.17 21.18 10.64 15.66 25.18 33.36 2.15 0.31 6.55 0.41 0.92 0.72 0.61 2.56 17.40
- 1992 1 14.36 29.87 11.75 1.25 2.89 10.69 50.58 8.57 0.10 0.48 0.10 0.19 0.10 0.00 0.39 0.00 0.00 0.58 1.45 0.10 0.00 0.00 0.00 5.30 0.39 0.00 0.00 0.00 0.00 0.00 0.67
- 1992 2 0.90 10.52 12.62 2.60 0.20 0.10 5.21 5.61 7.51 2.30 0.20 9.72 6.91 9.62 3.71 1.30 10.02 0.00 0.20 3.21 19.13 19.73 7.81 2.60 4.51 6.91 2.70 2.70 14.22 -99.99 -99.99
- 1992 3 4.56 5.72 4.45 6.04 5.51 20.14 4.77 7.74 21.31 6.15 17.91 12.83 3.18 2.23 1.59 0.85 9.54 6.78 6.25 5.72 7.21 3.39 1.17 2.12 2.54 0.32 0.11 6.15 6.68 5.19 14.95
- 1992 4 1.81 0.00 0.00 3.62 5.13 0.30 0.30 0.00 0.60 1.11 2.71 3.32 5.13 5.93 0.30 2.61 8.95 0.40 0.40 3.32 0.20 3.22 9.75 7.44 11.26 8.55 4.63 3.72 8.35 6.23 -99.99
- 1992 5 1.39 0.60 0.70 4.97 5.77 6.56 6.26 5.96 2.09 2.78 16.40 2.09 0.00 0.20 0.00 0.00 0.00 0.00 0.40 7.65 1.09 0.00 0.00 0.00 0.40 0.00 0.00 1.19 0.00 0.20 0.70
- 1992 6 4.83 0.60 0.00 0.20 0.10 0.00 0.00 5.63 5.43 0.00 0.00 0.00 3.22 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 2.92 1.31 0.70 1.01 0.10 4.52 5.63 -99.99
- 1992 7 0.00 10.03 7.72 0.00 0.10 0.30 1.20 0.00 0.00 4.41 5.62 1.81 1.00 0.10 3.51 4.71 9.03 1.60 2.81 3.11 1.10 1.40 16.85 1.20 4.71 12.84 1.20 0.00 0.10 0.00 8.72
- 1992 8 1.10 20.28 6.59 17.48 1.70 0.00 0.00 10.59 7.99 5.40 15.39 15.69 0.30 3.10 10.49 11.59 1.20 1.50 0.20 1.20 2.40 22.68 4.40 9.19 5.69 11.29 7.79 1.30 12.29 13.59 3.40
- 1992 9 8.30 4.40 1.30 0.80 8.40 25.61 8.30 8.90 4.30 6.50 11.31 8.20 8.20 11.71 1.40 0.70 0.50 0.30 5.00 8.70 3.20 0.30 2.20 14.81 0.20 8.00 1.50 0.10 14.91 1.10 -99.99
- 1992 10 1.77 16.06 0.73 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.61 3.44 0.00 0.00 1.56 0.10 0.10 0.10 3.44 5.63 6.57 5.42 1.04 13.76 12.41 7.19 0.00 1.77 26.17
- 1992 11 28.72 9.16 6.01 4.48 0.19 8.87 0.10 14.03 10.02 8.30 8.11 1.62 0.00 5.34 3.63 1.24 3.24 8.78 4.48 3.63 13.17 11.45 4.96 7.63 5.15 10.02 18.32 1.24 4.48 10.11 -99.99
- 1992 12 24.16 8.15 7.76 4.67 4.37 14.02 0.89 2.09 0.10 6.76 3.48 4.08 2.19 0.40 5.07 0.80 23.56 2.78 0.80 0.10 1.09 0.00 0.60 0.30 3.38 0.10 0.00 0.00 0.00 0.10 0.00
- 1993 1 6.75 1.01 16.52 10.47 2.42 0.30 11.08 20.85 9.77 13.09 15.00 6.45 9.26 19.84 15.81 8.46 8.36 14.50 16.92 7.55 9.97 8.76 29.20 6.75 1.71 1.11 9.06 1.81 1.01 2.11 0.00
- 1993 2 1.93 0.32 0.54 0.11 3.54 1.39 0.11 0.21 0.00 0.43 0.00 0.00 0.43 5.04 1.39 0.21 0.75 5.14 0.21 1.50 0.43 0.32 1.18 6.96 5.68 0.75 0.11 0.32 -99.99 -99.99 -99.99
- 1993 3 0.30 0.41 0.61 1.01 0.10 0.10 0.20 0.00 0.20 5.27 1.22 6.49 0.91 1.62 5.27 19.68 13.29 2.43 0.91 6.70 0.51 9.23 3.75 0.41 0.00 1.72 3.35 2.84 27.29 2.74 1.12
- 1993 4 0.39 0.10 14.70 11.15 16.67 1.48 1.18 16.48 11.44 0.10 0.20 0.99 1.38 0.00 4.24 4.54 11.05 17.36 7.60 10.16 2.66 4.74 3.06 0.10 5.43 0.00 0.00 0.00 0.00 0.20 -99.99
- 1993 5 0.29 0.59 0.00 0.00 0.00 0.20 9.02 0.10 0.00 0.59 0.00 0.59 18.14 14.22 4.41 20.00 15.29 0.39 1.86 3.43 0.10 0.10 0.10 0.00 0.00 0.00 0.00 1.57 7.65 11.27 0.29
- 1993 6 12.97 2.05 0.20 0.98 0.00 0.00 0.39 0.00 6.83 7.71 3.22 0.00 5.56 0.10 0.88 2.34 7.80 3.71 2.54 0.59 0.98 0.00 0.10 0.00 12.09 0.20 0.00 0.00 0.00 0.10 -99.99
- 1993 7 0.41 7.67 3.38 0.92 1.33 1.13 4.81 9.10 2.05 0.92 0.82 0.20 1.94 6.34 14.22 6.14 0.31 1.74 3.07 0.41 0.92 6.85 2.86 3.48 4.60 1.43 2.76 9.10 2.15 4.60 1.84
- 1993 8 5.18 13.87 2.93 4.59 0.59 6.35 2.15 5.37 1.56 8.30 3.52 0.39 0.78 5.27 0.78 0.20 0.59 1.37 0.10 0.29 0.49 0.29 0.68 0.10 0.00 0.00 0.00 0.39 0.39 0.00 0.00
- 1993 9 0.00 0.00 0.00 0.00 0.00 0.00 0.19 16.17 2.01 13.01 1.34 0.10 1.53 1.44 0.38 0.10 0.48 3.92 14.16 2.39 1.34 1.91 4.69 1.82 0.19 0.19 0.00 0.57 2.39 2.77 -99.99
- 1993 10 7.18 1.97 7.87 0.30 13.47 4.92 6.20 2.75 9.24 0.10 0.20 0.10 0.00 0.59 0.49 0.39 0.00 0.79 7.67 1.08 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.10 0.00
- 1993 11 0.00 0.10 7.92 0.20 0.30 6.04 7.62 14.36 6.54 0.89 5.54 8.22 2.77 0.00 10.40 0.69 0.00 0.00 0.00 0.30 0.10 0.00 0.00 0.89 5.45 0.10 0.00 0.10 14.95 7.23 -99.99
- 1993 12 13.34 15.15 24.78 6.32 2.91 15.95 11.34 22.47 8.43 8.53 2.31 15.75 1.40 18.26 10.33 1.71 8.13 22.88 2.31 2.11 8.23 4.82 5.12 1.20 0.70 0.90 6.92 14.65 7.42 3.81 2.21
- 1994 1 8.71 1.39 11.39 4.06 13.37 2.08 0.69 3.27 5.35 0.20 11.29 8.02 15.25 3.86 0.20 0.00 4.55 11.98 7.03 6.44 4.16 14.45 2.48 14.06 9.21 17.42 3.17 4.75 7.43 1.98 20.20
- 1994 2 12.10 0.10 5.70 6.40 6.40 4.00 1.80 8.00 0.50 3.20 0.00 0.00 0.10 0.00 1.00 0.00 0.20 0.70 0.00 0.00 0.10 1.20 2.50 1.20 9.20 11.20 13.60 2.20 -99.99 -99.99 -99.99
- 1994 3 4.09 9.77 6.08 15.46 7.88 9.77 16.15 9.87 5.38 5.68 5.48 16.25 14.96 4.09 8.87 5.78 3.49 2.19 1.50 0.30 13.36 19.54 12.16 3.59 0.40 0.90 10.87 6.98 3.69 11.07 6.08
- 1994 4 5.38 5.48 18.63 8.97 4.48 9.57 6.18 8.97 2.99 0.10 2.59 0.20 0.00 0.00 0.00 0.20 0.20 1.69 0.50 0.00 6.28 5.08 4.58 2.29 6.38 0.90 2.79 5.18 2.09 0.10 -99.99
- 1994 5 1.01 0.71 8.48 6.87 8.48 2.73 0.50 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.20 0.00 0.50 0.61
- 1994 6 0.20 7.51 5.14 1.38 1.88 3.46 1.68 3.46 1.68 0.00 0.00 0.00 0.00 0.30 4.45 4.55 10.77 11.76 2.87 9.98 3.16 0.99 6.72 8.00 0.49 5.24 1.48 2.67 0.30 0.00 -99.99
- 1994 7 0.00 0.00 1.37 10.80 0.39 6.87 0.79 0.00 11.78 6.28 5.99 2.75 0.00 2.55 0.00 0.00 0.00 0.00 0.00 1.37 1.47 0.00 1.37 8.74 9.32 1.96 0.10 0.00 0.39 0.98 15.12
- 1994 8 0.88 7.35 11.17 2.06 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.88 7.35 0.39 3.72 1.18 0.78 0.00 19.20 7.93 6.46 3.62 5.97 12.34 12.14 0.69 0.29 0.10
- 1994 9 0.00 0.40 11.39 1.68 2.28 2.08 4.76 5.85 10.20 8.42 4.36 4.06 1.19 0.00 0.10 0.00 0.00 3.17 3.86 0.30 0.00 0.10 0.00 0.20 0.69 1.39 0.69 1.98 0.89 4.46 -99.99
- 1994 10 7.34 6.12 0.41 2.14 1.43 4.08 0.71 0.10 0.20 0.00 0.00 0.00 0.00 0.00 0.51 0.00 0.00 0.00 7.44 4.99 4.49 12.74 2.65 9.68 4.69 3.57 2.55 2.14 7.65 9.58 4.79
- 1994 11 0.50 6.62 10.03 2.11 1.81 0.90 0.30 7.93 5.42 2.81 4.52 16.76 19.67 23.28 7.53 8.33 15.25 21.67 5.92 3.41 1.61 4.72 1.00 0.60 3.11 1.30 0.20 0.10 0.20 0.10 -99.99
- 1994 12 1.38 4.43 5.02 5.61 12.41 10.54 16.45 5.52 10.93 55.36 37.33 2.96 0.20 0.30 3.94 2.27 8.77 10.05 6.80 0.00 0.30 1.58 7.19 2.66 10.05 7.49 10.24 14.97 9.85 4.83 0.49
- 1995 1 0.71 0.91 2.22 9.60 3.64 6.36 5.76 16.77 9.60 6.36 0.30 0.81 1.31 3.54 13.74 14.55 14.85 3.94 8.38 3.54 18.89 8.89 3.33 2.32 1.52 0.81 11.01 2.83 0.51 25.15 7.98
- 1995 2 1.89 14.33 7.46 2.49 8.06 4.18 0.00 0.00 1.29 4.68 14.93 5.17 9.55 11.34 4.98 8.46 3.68 15.23 9.25 5.08 17.02 12.04 1.19 3.48 1.39 6.77 9.95 19.30 -99.99 -99.99 -99.99
- 1995 3 7.76 2.39 1.19 13.92 2.19 7.66 2.59 1.79 9.74 9.15 0.60 0.80 2.68 7.06 8.55 15.41 7.46 7.66 2.29 0.00 0.00 0.00 5.97 13.03 7.06 6.56 3.28 1.89 4.77 3.58 1.59
- 1995 4 0.20 1.21 3.12 15.09 3.92 0.80 0.30 0.00 2.01 0.00 1.01 0.00 0.00 0.00 0.00 1.81 5.13 0.60 1.01 0.80 0.80 5.33 3.32 2.11 0.00 0.00 0.00 0.00 0.50 0.20 -99.99
- 1995 5 1.92 1.01 0.00 0.00 0.00 1.11 0.61 0.61 2.63 0.00 2.63 2.42 1.11 0.20 0.20 0.81 1.31 3.54 1.92 0.10 1.72 1.41 0.10 13.74 2.63 4.55 12.22 4.44 4.75 0.91 1.31
- 1995 6 1.34 4.40 2.39 0.29 1.15 0.86 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.84 1.34 4.59 15.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1995 7 0.00 0.76 0.47 0.38 6.82 2.37 0.00 2.18 0.00 1.04 15.81 0.00 1.33 11.17 3.41 4.17 3.31 0.57 9.37 12.69 0.76 4.35 2.08 0.47 0.00 1.99 0.00 3.22 0.00 0.00 1.89
- 1995 8 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.20 2.89 0.60 0.00 0.00 0.00 0.00 1.50 0.00 0.00 0.50 1.00 2.89 8.27 6.28 2.49 0.60 2.19 0.50 0.10 0.00
- 1995 9 7.86 12.44 2.09 8.76 2.09 1.29 7.96 0.60 0.20 0.60 1.19 5.17 1.29 0.00 0.00 0.00 0.60 0.00 0.00 0.30 1.29 1.19 20.59 3.58 10.35 9.15 2.79 1.69 0.00 13.73 -99.99
- 1995 10 8.50 18.78 6.03 12.46 17.80 8.70 6.53 0.69 2.27 0.49 9.69 20.56 0.79 3.06 1.68 22.74 2.37 2.37 6.62 0.00 25.31 23.53 0.99 20.17 25.11 21.75 2.77 0.49 2.47 2.08 1.98
- 1995 11 0.00 0.00 0.00 0.00 0.00 1.99 1.78 1.26 4.81 0.42 12.35 0.10 0.00 1.99 14.33 0.10 0.00 0.42 0.00 5.02 3.24 3.14 15.90 13.08 6.91 0.52 0.31 0.10 0.31 0.10 -99.99
- 1995 12 0.43 4.46 3.30 0.11 0.85 1.49 0.53 0.00 0.00 0.00 0.00 0.00 0.21 0.21 0.85 0.11 0.11 0.53 0.00 0.00 11.37 8.72 3.83 2.02 0.00 0.00 0.00 0.00 0.00 0.11 8.08
- 1996 1 7.08 1.18 11.78 15.52 6.61 8.93 3.80 14.16 5.03 0.20 9.68 11.37 9.39 5.20 0.52 0.67 0.63 3.61 1.29 0.08 0.30 0.02 0.00 0.00 0.09 4.41 3.97 0.00 0.00 0.00 0.00
- 1996 2 0.00 0.00 0.00 9.11 23.54 6.50 4.55 9.45 12.80 7.50 26.60 1.04 0.02 0.00 2.84 4.23 18.26 1.27 1.00 1.22 4.10 0.82 5.92 11.13 0.34 0.29 0.00 0.00 0.00 -99.99 -99.99
- 1996 3 0.00 0.00 0.00 0.00 0.00 0.15 0.00 2.79 1.15 0.39 27.23 17.71 0.06 0.03 7.79 15.02 0.14 0.02 0.00 0.11 0.46 0.39 0.00 0.00 0.04 0.00 1.38 0.02 0.00 0.00 0.03
- 1996 4 0.23 0.57 0.00 0.06 0.00 0.00 0.41 1.00 3.19 4.48 2.88 3.33 5.59 4.55 8.18 15.47 15.86 7.39 2.32 5.17 11.13 5.49 2.14 3.52 3.31 3.98 0.02 1.90 5.33 13.38 -99.99
- 1996 5 1.65 0.00 0.11 0.29 2.57 2.16 0.07 0.00 0.00 0.49 0.48 0.32 0.00 0.00 0.00 0.00 0.33 1.25 6.09 2.96 8.85 8.61 3.01 1.50 0.67 6.82 0.06 10.56 3.19 6.17 0.00
- 1996 6 3.90 0.52 8.20 5.46 2.05 0.00 0.00 1.87 10.35 4.90 11.37 0.31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.73 0.06 8.28 1.56 0.52 2.89 -99.99
- 1996 7 3.11 14.44 7.48 3.23 0.16 0.43 0.16 1.52 0.06 0.41 1.62 0.06 1.44 0.00 0.00 0.00 0.00 0.00 0.00 0.06 9.75 13.86 1.46 0.06 3.53 0.27 0.00 11.60 0.21 0.66 2.27
- 1996 8 1.31 0.00 0.00 0.00 4.43 4.84 0.00 4.55 6.96 1.15 0.06 0.00 0.00 0.00 1.02 0.26 0.06 0.00 1.64 12.51 6.47 10.59 0.06 2.72 5.89 5.73 1.10 0.00 0.25 0.09 0.00
- 1996 9 3.86 0.06 0.00 0.00 0.03 0.06 0.00 0.06 0.00 0.07 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.49 0.00 1.09 4.68 1.12 8.06 5.03 27.99 3.46 1.65 -99.99
- 1996 10 1.01 1.99 19.88 2.61 1.84 0.28 1.10 0.53 3.06 0.39 44.62 8.67 0.79 20.25 19.66 8.15 5.17 9.16 1.64 3.86 1.71 0.07 1.65 19.34 11.24 20.38 22.02 15.93 0.58 12.58 2.97
- 1996 11 8.39 13.64 14.64 6.23 25.79 9.70 0.45 5.39 0.97 0.22 5.83 0.07 3.85 0.21 2.02 4.46 0.67 0.06 2.39 0.52 5.63 0.71 1.31 23.42 0.44 0.28 1.45 20.36 9.16 2.81 -99.99
- 1996 12 14.34 5.98 21.39 0.39 0.12 0.00 8.88 1.38 0.46 0.00 0.02 0.43 0.57 1.21 1.75 1.27 8.49 24.83 1.06 0.00 0.00 0.00 0.00 0.02 0.31 5.03 0.32 0.10 0.44 1.47 0.77
- 1997 1 0.79 1.00 0.17 0.11 0.00 0.07 0.00 0.04 0.00 2.39 8.61 1.26 1.14 0.41 0.13 1.21 6.02 2.19 0.21 0.06 0.00 0.06 1.98 2.78 0.54 0.12 0.03 0.11 0.00 0.00 0.17
- 1997 2 4.73 0.74 30.13 0.41 3.50 3.91 0.45 16.62 18.39 11.64 10.16 10.58 1.75 0.99 5.91 15.44 28.82 14.05 19.47 18.46 0.30 13.70 13.15 4.07 1.48 7.01 19.09 6.43 -99.99 -99.99 -99.99
- 1997 3 12.41 1.61 0.06 0.34 3.15 3.86 7.82 0.06 0.02 0.00 0.48 1.57 5.79 3.36 3.13 2.12 3.89 5.91 0.86 0.06 0.00 14.93 4.80 0.51 6.45 5.04 10.62 0.65 0.00 0.00 0.12
- 1997 4 0.00 2.89 1.18 6.72 0.82 1.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.00 0.29 0.50 0.03 22.07 0.45 0.18 0.74 8.26 3.69 0.00 0.00 -99.99
- 1997 5 0.06 0.00 13.86 19.95 3.44 1.16 3.88 0.34 0.06 14.75 7.90 8.36 2.90 0.00 0.00 6.58 0.65 3.15 7.38 2.72 0.00 0.00 0.00 0.00 0.15 0.00 0.06 0.00 0.00 0.00 0.00
- 1997 6 0.06 0.00 0.00 0.00 4.36 2.97 3.04 0.55 2.90 7.01 9.27 4.93 8.57 0.35 0.06 0.00 2.30 9.54 9.30 6.44 2.76 0.65 0.00 5.28 2.22 1.96 1.56 1.31 0.00 5.93 -99.99
- 1997 7 4.75 4.21 4.02 0.00 0.14 1.93 1.01 0.00 0.00 1.25 1.44 3.44 1.64 2.21 3.17 0.00 0.00 0.00 0.00 10.59 0.00 0.34 8.57 5.09 0.85 10.20 6.62 0.71 8.96 7.62 4.36
- 1997 8 0.72 0.00 0.00 0.00 0.00 0.00 0.00 1.98 0.15 0.09 0.75 0.00 3.16 0.24 0.06 1.17 0.15 0.00 1.32 1.84 3.24 0.00 4.01 0.42 2.15 1.79 7.42 3.58 2.32 0.04 10.18
- 1997 9 1.21 32.20 15.59 4.77 0.23 3.19 0.51 0.00 0.00 0.00 4.21 4.20 4.91 10.88 19.96 24.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.46 0.13 1.14 -99.99
- 1997 10 0.00 0.00 0.03 0.69 2.65 7.04 0.48 3.93 18.96 1.66 0.00 0.31 0.22 9.29 12.41 20.72 3.42 0.13 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.24 1.37 0.16
- 1997 11 0.71 2.37 0.00 0.66 12.79 1.08 3.35 5.04 5.84 6.23 8.28 5.75 0.87 3.94 4.80 7.76 24.61 5.85 6.96 18.47 3.56 3.50 11.48 4.74 1.77 0.83 0.43 3.57 0.76 0.00 -99.99
- 1997 12 0.10 0.04 0.21 0.58 18.33 3.91 9.38 10.28 18.16 20.28 3.33 2.15 0.18 0.12 0.95 0.00 0.58 13.86 4.58 0.34 0.20 4.71 9.94 21.20 17.48 2.50 8.09 0.48 4.36 15.98 1.12
- 1998 1 18.73 13.84 8.47 8.31 0.25 10.25 2.77 15.62 2.57 0.53 1.73 0.72 17.40 6.90 1.67 4.56 6.87 2.33 0.17 4.82 1.36 7.63 0.62 0.08 0.00 0.06 0.00 0.00 0.06 0.17 1.32
- 1998 2 0.33 0.47 2.84 1.39 0.43 10.29 2.19 5.58 10.28 19.47 22.01 0.30 1.85 8.01 4.40 0.31 0.07 0.49 1.91 6.05 3.22 4.29 0.40 0.00 0.82 3.90 7.21 4.47 -99.99 -99.99 -99.99
- 1998 3 8.15 13.38 1.78 1.71 2.86 20.85 2.07 0.03 1.48 18.39 0.29 1.28 0.00 0.00 0.09 0.75 0.37 0.07 0.00 0.00 0.00 0.00 6.78 0.16 15.94 5.33 0.76 0.09 16.37 0.40 0.00
- 1998 4 1.36 12.62 4.76 4.25 1.10 5.53 9.16 13.35 0.50 0.16 0.25 0.11 0.40 0.17 0.10 0.57 0.00 0.00 4.51 0.59 11.73 10.38 0.06 4.77 7.66 4.57 1.79 0.99 2.56 0.00 -99.99
- 1998 5 0.00 0.00 0.06 3.32 4.91 13.54 5.99 0.23 0.06 0.00 2.04 0.00 0.00 0.76 0.00 0.06 0.00 0.00 0.00 0.27 0.05 0.06 0.00 0.13 0.15 0.34 1.96 8.19 12.87 5.08 3.71
- 1998 6 0.24 3.85 0.13 0.00 2.59 6.08 1.75 20.59 8.35 14.99 0.75 0.00 1.37 0.82 6.24 0.83 0.00 2.64 0.00 0.04 0.92 7.07 20.38 3.27 5.12 12.65 2.81 0.08 1.70 0.00 -99.99
- 1998 7 0.00 0.00 0.00 0.72 0.00 0.00 0.56 8.27 0.10 9.82 8.76 18.66 0.08 1.25 0.44 9.82 5.65 3.01 31.78 4.08 9.67 10.83 0.59 0.34 4.08 7.11 1.58 5.23 2.51 1.73 4.39
- 1998 8 0.00 17.98 3.83 1.84 7.32 3.14 15.16 1.34 0.00 0.10 6.27 1.04 6.44 7.36 1.79 21.66 0.28 0.00 4.46 15.85 1.20 0.63 4.22 0.00 2.51 0.00 0.09 0.00 0.06 0.00 10.38
- 1998 9 15.86 2.55 0.00 0.00 0.00 1.77 15.47 15.64 12.93 1.68 6.24 1.82 0.00 0.18 0.85 0.00 8.69 0.14 0.00 0.06 0.06 0.12 0.00 0.06 0.00 0.00 4.41 0.03 0.07 3.61 -99.99
- 1998 10 0.05 0.05 0.02 0.94 0.61 0.26 0.06 1.35 14.43 2.73 3.75 9.22 6.34 9.23 16.03 20.73 2.54 3.19 1.38 51.27 8.02 32.73 12.92 20.89 5.00 23.83 14.42 14.59 5.33 1.62 0.35
- 1998 11 1.22 29.48 0.55 2.61 4.68 1.07 7.29 15.07 6.85 3.42 7.74 8.43 0.82 0.24 0.00 0.00 1.44 1.19 2.15 4.85 21.23 3.29 15.23 0.77 16.64 3.93 24.69 2.45 0.76 1.21 -99.99
- 1998 12 1.23 0.80 0.19 0.54 0.00 0.39 10.52 3.31 2.01 8.81 11.86 5.58 18.06 6.46 1.22 0.54 9.96 4.55 0.06 0.00 1.87 8.88 0.82 13.95 4.05 20.18 3.63 0.55 5.42 1.22 2.30
- 1999 1 16.86 10.72 12.49 11.43 21.82 1.30 6.80 0.20 0.06 0.06 13.42 0.93 7.70 14.13 16.22 0.80 3.33 21.44 3.87 2.92 0.82 9.67 8.55 28.93 4.52 1.04 6.63 2.70 1.09 0.26 0.04
- 1999 2 0.00 0.95 7.02 3.09 0.46 0.66 0.00 0.00 0.00 0.09 0.04 0.83 3.42 0.98 2.19 2.79 6.44 10.71 2.68 9.08 6.73 0.00 0.56 0.39 1.20 1.22 13.29 10.68 -99.99 -99.99 -99.99
- 1999 3 4.42 11.10 1.70 0.20 0.08 0.23 0.03 0.20 0.42 0.00 3.38 1.37 0.18 1.77 4.21 0.27 2.46 0.54 0.43 14.68 0.53 8.39 3.70 1.05 0.16 0.08 0.00 43.57 0.97 0.27 0.61
- 1999 4 0.00 1.10 2.96 3.72 12.67 5.08 1.64 0.34 2.92 1.48 20.96 4.63 1.59 2.59 3.76 0.00 4.25 2.63 1.56 20.23 16.46 5.31 0.24 0.04 0.00 0.00 0.00 0.00 0.00 0.00 -99.99
- 1999 5 0.00 0.00 0.00 0.00 1.34 0.28 11.20 5.74 13.17 11.71 10.90 3.51 3.91 0.08 0.28 0.00 0.00 0.00 0.00 3.60 5.58 4.76 5.40 1.32 6.83 1.99 6.44 5.10 0.03 0.00 0.00
- 1999 6 0.08 18.02 2.64 3.04 2.53 0.87 2.86 0.03 0.00 0.00 0.17 4.24 2.56 0.48 1.53 2.90 0.00 1.75 27.95 0.75 0.11 0.04 0.00 0.00 0.00 16.49 0.56 5.03 1.21 0.48 -99.99
- 1999 7 2.37 7.28 5.11 0.17 0.14 0.99 2.70 0.08 2.69 3.27 0.00 0.49 4.30 0.37 12.83 4.74 0.98 7.32 14.18 4.51 1.77 0.00 0.24 0.12 0.08 0.08 0.00 0.00 0.08 0.00 0.00
- 1999 8 4.48 2.22 0.60 0.00 9.83 2.12 0.61 0.18 0.00 0.00 0.00 6.09 5.08 4.65 3.83 3.88 4.57 0.21 0.00 0.00 0.08 0.00 0.00 0.64 8.40 5.04 0.06 0.00 5.95 1.98 0.37
- 1999 9 0.35 0.10 1.34 0.63 3.54 17.99 2.78 11.42 0.00 2.26 11.38 2.08 2.52 0.00 6.47 11.72 5.64 4.08 23.49 11.34 1.67 6.03 11.57 3.86 0.31 0.95 3.76 12.20 3.29 6.77 -99.99
- 1999 10 6.63 0.96 0.38 0.08 0.00 4.09 2.97 3.58 1.03 6.32 1.09 0.03 0.37 0.12 0.00 1.46 0.00 0.37 0.37 0.15 15.59 5.29 8.72 3.53 0.00 0.22 6.09 0.37 1.60 6.02 15.18
- 1999 11 12.07 3.29 0.50 35.99 10.41 0.44 4.19 0.00 0.00 0.00 0.00 0.00 0.00 0.49 3.44 1.26 0.52 0.00 0.13 0.00 1.32 0.80 6.01 5.38 7.23 7.55 34.78 27.81 6.93 8.67 -99.99
- 1999 12 7.15 30.51 13.58 0.74 18.93 8.79 11.20 24.91 6.36 7.42 11.01 8.34 3.42 6.16 0.81 17.19 0.26 0.96 0.00 18.18 11.06 12.20 20.98 21.22 1.75 4.75 1.38 1.30 12.23 3.17 4.04
- 2000 1 0.36 8.37 1.28 11.18 8.94 7.31 9.36 2.41 0.14 5.99 15.19 1.51 0.06 0.00 0.08 0.00 0.00 0.00 2.02 0.00 0.33 0.00 0.15 0.00 0.15 0.27 5.01 16.61 3.75 12.60 23.70
- 2000 2 3.04 2.68 6.16 1.96 3.55 5.19 13.39 6.31 13.23 2.48 8.66 4.29 4.39 3.59 12.57 4.13 8.07 2.16 0.08 8.32 0.00 2.25 6.38 4.08 0.14 29.82 6.67 5.12 1.48 -99.99 -99.99
- 2000 3 8.72 19.75 0.49 0.19 3.62 7.41 5.97 16.85 2.23 0.93 0.08 1.20 2.12 0.31 0.43 0.08 0.00 0.48 0.06 0.08 0.65 0.00 23.08 2.80 1.04 0.25 0.00 0.00 0.64 0.49 3.20
- 2000 4 1.44 1.87 0.57 0.00 0.00 0.00 1.18 0.00 0.00 4.02 5.87 3.94 0.45 0.00 0.06 0.53 5.62 0.13 8.75 4.70 0.00 1.21 2.36 5.72 9.77 19.67 0.00 0.00 0.38 0.00 -99.99
- 2000 5 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 1.38 0.00 0.00 0.00 0.00 0.00 2.44 20.05 2.00 1.62 1.36 2.70 0.00 5.06 2.77 4.17 2.94 3.91 0.75 1.12 0.17 0.14 6.45
- 2000 6 3.62 3.19 6.31 0.14 3.05 2.37 7.31 0.75 9.13 5.77 1.49 5.75 0.00 0.00 0.00 0.00 0.00 0.00 0.36 12.83 5.45 5.66 0.90 1.61 0.00 0.00 0.00 0.17 0.00 0.00 -99.99
- 2000 7 0.00 0.06 0.00 0.00 9.91 0.17 0.41 8.03 9.56 0.12 0.00 0.85 0.33 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.38 3.05 2.45 3.88 14.15
- 2000 8 4.86 4.72 0.08 0.00 1.00 0.34 0.81 8.79 8.01 0.08 0.26 11.03 18.67 2.90 2.62 5.53 0.60 1.61 1.60 0.00 4.21 0.00 0.00 0.00 7.75 4.61 1.13 0.52 0.06 8.54 21.58
- 2000 9 3.47 0.06 0.00 15.35 10.69 7.64 1.58 2.09 3.55 28.52 2.58 0.08 3.63 2.81 2.09 0.62 11.93 1.96 32.22 2.14 10.67 2.54 1.48 16.99 1.46 8.57 20.43 9.91 5.66 0.56 -99.99
- 2000 10 4.46 8.19 7.66 13.15 0.20 5.15 6.56 2.35 24.03 6.56 1.27 0.29 3.32 0.00 3.92 2.26 20.22 3.26 2.80 4.94 0.08 9.77 11.63 46.13 4.66 11.57 0.86 14.72 4.09 0.67 8.47
- 2000 11 6.20 0.00 4.15 11.25 4.32 12.97 1.90 0.06 0.14 6.52 23.40 3.45 0.13 1.20 12.82 5.30 1.23 5.38 0.80 0.06 2.94 5.86 0.06 4.46 26.14 5.00 7.21 19.97 3.71 12.08 -99.99
- 2000 12 18.26 0.61 18.45 18.63 10.87 3.97 24.36 20.86 6.57 8.69 9.47 24.06 0.89 0.08 0.41 0.58 4.71 0.00 11.67 6.39 0.00 0.00 0.52 0.55 0.00 0.49 1.29 0.80 0.57 1.71 22.41
- 2001 1 7.20 1.62 6.76 0.44 2.65 7.70 2.02 0.22 0.13 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 7.89 3.03 14.61 14.84 1.99 4.07 3.69 1.90 2.74 2.80 4.56 0.35
- 2001 2 5.89 6.21 0.85 11.75 17.31 10.97 0.00 0.11 1.64 18.40 0.36 0.00 0.05 1.17 0.00 0.05 0.17 1.69 0.11 1.72 0.11 0.78 0.13 0.70 8.23 6.91 5.12 0.00 -99.99 -99.99 -99.99
- 2001 3 0.00 0.06 0.48 0.02 0.15 9.26 0.59 5.89 1.55 1.99 8.34 0.11 0.05 0.48 0.05 0.04 0.11 0.17 0.00 0.00 0.17 1.92 0.78 0.14 0.08 2.10 16.76 0.99 0.42 11.38 1.34
- 2001 4 1.58 1.66 1.43 0.88 7.40 11.94 0.68 1.20 5.74 0.18 0.00 0.00 0.56 1.10 0.14 0.20 2.46 0.05 0.09 0.00 9.01 11.72 0.40 2.75 2.13 0.21 7.53 5.90 3.17 0.09 -99.99
- 2001 5 0.00 0.63 0.13 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 1.08 1.96 12.32 1.83 1.97 0.21 0.09 0.00 0.00 0.00 0.00 0.00 3.94 0.50 0.42 1.92 5.74 3.93 0.80
- 2001 6 4.34 0.04 0.00 0.00 2.11 5.41 1.75 4.51 7.49 0.43 0.11 0.00 0.00 4.22 2.11 0.00 0.00 4.22 13.41 0.17 0.06 0.00 0.17 0.11 0.04 11.79 3.59 6.04 7.55 1.04 -99.99
- 2001 7 0.25 1.91 10.49 0.00 0.32 3.79 0.49 0.67 4.70 14.38 4.25 5.81 1.98 3.06 0.05 0.00 0.05 0.00 0.05 5.50 2.06 0.40 5.10 4.15 0.37 1.10 0.11 0.05 0.09 8.34 0.05
- 2001 8 7.84 3.41 0.17 2.92 0.30 1.34 5.51 3.09 1.73 3.05 7.45 6.98 10.60 10.97 5.56 6.32 0.00 4.86 12.39 2.06 4.50 0.00 0.05 0.45 3.14 0.12 0.00 0.06 7.41 3.20 0.00
- 2001 9 5.97 2.88 1.21 2.66 0.09 4.04 1.47 0.11 0.30 0.04 1.80 14.28 0.56 4.14 3.20 0.97 0.09 0.04 0.27 1.66 0.00 0.05 0.35 0.31 1.58 0.78 6.82 8.06 5.55 16.74 -99.99
- 2001 10 7.45 4.90 7.55 8.72 9.57 9.98 18.88 10.30 4.39 0.45 3.89 2.57 0.20 10.40 5.62 1.01 13.01 1.95 12.53 9.88 11.76 3.86 7.34 1.29 4.80 6.65 1.80 1.42 2.47 6.23 0.60
- 2001 11 0.00 1.16 1.97 2.68 10.56 2.68 7.70 0.83 1.97 0.91 10.99 0.59 1.35 0.41 0.63 0.99 2.77 4.27 1.77 6.23 10.02 0.61 1.49 5.51 3.49 10.56 7.85 12.88 10.37 9.61 -99.99
- 2001 12 4.82 0.53 18.52 18.25 6.28 11.17 1.28 0.05 0.00 0.00 0.00 0.16 0.04 0.00 0.09 0.17 0.09 0.60 0.57 1.42 1.62 2.51 4.20 2.82 0.22 4.20 7.67 0.26 1.80 0.23 1.26
- 2002 1 0.00 0.00 0.00 6.57 0.44 0.18 0.19 0.12 0.00 0.64 4.67 3.69 1.35 6.18 0.00 11.71 2.50 11.78 10.52 4.24 6.22 17.67 11.76 0.76 25.76 6.95 5.67 8.00 14.73 3.11 29.81
- 2002 2 17.23 7.29 6.27 16.19 6.92 10.78 5.18 13.71 5.49 22.62 1.30 2.44 0.00 0.08 0.00 0.28 1.20 9.88 23.82 1.99 14.12 9.66 5.90 9.24 16.77 14.96 16.55 0.61 -99.99 -99.99 -99.99
- 2002 3 0.24 0.83 0.16 0.00 12.93 2.74 0.06 6.81 22.30 4.79 0.27 0.08 0.00 1.44 12.13 4.70 2.60 0.08 2.23 9.75 5.12 0.40 0.00 3.80 0.39 0.00 0.00 0.08 0.00 2.52 5.13
- 2002 4 7.30 2.01 3.34 0.00 0.00 0.00 0.00 0.00 0.08 0.00 1.96 0.00 1.76 0.00 0.00 0.91 0.00 0.41 0.13 14.57 10.41 0.28 0.06 1.96 12.26 2.09 3.36 8.41 2.40 10.67 -99.99
- 2002 5 2.73 1.74 0.77 0.00 0.00 0.00 4.83 0.00 1.30 0.00 0.00 0.86 10.23 1.00 0.67 0.00 3.01 8.08 13.50 6.38 11.43 6.52 18.25 15.82 2.87 6.36 0.00 0.00 7.74 8.59 0.08
- 2002 6 6.45 7.12 3.57 1.50 0.28 0.00 0.85 7.03 17.09 12.81 6.26 15.17 12.40 9.62 7.96 11.96 0.08 1.66 0.14 2.50 5.37 2.61 0.32 1.61 0.82 1.63 0.00 0.33 2.16 15.87 -99.99
- 2002 7 3.88 3.29 0.48 3.56 1.77 1.93 4.88 0.65 4.60 1.60 4.00 3.20 0.00 1.00 0.76 0.00 0.00 3.96 4.51 5.62 1.34 6.54 0.87 1.38 0.08 0.06 0.20 8.27 10.66 11.31 1.70
- 2002 8 0.06 19.82 0.00 0.08 0.00 4.73 0.54 0.42 0.14 1.75 5.53 5.15 8.41 6.64 0.08 0.00 5.36 8.33 0.04 0.08 0.00 0.18 1.10 0.60 0.00 0.08 0.00 2.02 12.01 16.68 0.00
- 2002 9 0.00 0.00 0.00 0.79 6.04 5.54 17.38 4.39 20.32 0.76 0.08 0.00 0.00 0.16 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 1.11 1.29 0.00 0.00 0.31 1.66 -99.99
- 2002 10 0.00 6.76 0.81 3.70 0.62 0.08 4.37 0.85 0.00 2.05 33.88 3.28 2.31 0.69 0.05 0.18 0.37 0.20 0.86 19.03 49.35 8.09 3.21 26.97 7.05 27.31 3.32 6.13 1.54 0.07 4.25
- 2002 11 13.96 17.90 1.60 3.18 19.92 5.09 9.05 6.15 11.05 0.96 7.68 6.97 7.63 10.23 2.56 0.00 1.74 1.16 6.15 5.66 9.76 2.40 7.56 2.52 7.44 12.31 22.30 7.74 2.87 14.91 -99.99
- 2002 12 11.90 1.30 9.31 0.70 0.06 0.43 0.06 0.00 0.00 0.00 0.00 0.00 1.80 0.17 2.90 0.31 0.06 0.13 0.00 0.00 14.66 11.92 17.85 5.29 2.28 6.71 2.93 0.00 6.12 0.06 6.83
- 2003 1 10.32 5.31 0.22 0.00 0.08 0.00 0.00 1.13 0.00 0.00 0.70 6.29 2.25 6.25 1.89 11.06 1.20 17.95 7.45 17.29 1.67 0.96 1.42 18.39 4.42 0.83 11.67 3.39 0.04 0.00 6.59
- 2003 2 8.00 10.02 8.07 0.67 0.24 4.37 0.08 9.21 0.39 12.57 0.00 0.43 0.33 0.06 0.07 0.00 0.00 0.00 0.00 0.00 0.08 1.72 0.92 0.00 0.22 0.92 2.83 16.74 -99.99 -99.99 -99.99
- 2003 3 12.62 2.53 7.83 7.70 1.07 2.79 15.18 15.97 6.11 2.15 2.90 0.06 0.00 0.00 0.00 0.14 0.12 0.06 0.00 0.00 0.08 0.08 0.08 0.04 0.00 0.38 0.08 0.00 0.00 0.14 11.25
- 2003 4 2.23 0.24 2.84 0.12 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 2.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.26 0.09 0.00 1.42 12.25 1.81 9.48 7.51 6.40 1.56 -99.99
- 2003 5 2.87 5.85 16.61 17.97 2.40 1.75 4.54 0.29 2.89 1.55 3.70 4.22 0.44 0.07 4.40 13.80 5.48 6.60 2.22 3.12 9.82 2.50 4.82 2.75 0.06 0.06 0.52 3.74 0.34 0.00 0.06
- 2003 6 4.44 0.00 0.73 0.21 4.38 0.32 1.22 3.73 10.65 2.94 2.18 0.64 0.10 0.00 0.00 0.00 4.28 2.92 2.29 0.00 0.12 0.82 0.13 0.00 0.00 4.62 9.20 0.38 0.07 6.55 -99.99
- 2003 7 1.73 0.00 0.00 0.00 0.00 1.77 1.59 0.09 2.04 6.92 0.08 0.00 0.81 0.00 0.00 0.00 0.00 0.00 0.00 13.76 3.72 1.65 5.34 8.82 0.51 0.43 2.40 13.55 15.93 2.46 7.49
- 2003 8 0.36 0.00 0.00 0.11 0.66 0.00 0.00 0.00 1.46 0.18 0.06 0.81 0.09 0.00 0.00 0.00 7.57 0.54 0.15 5.71 8.19 0.13 0.00 0.00 0.00 0.00 0.00 0.96 0.00 0.00 0.00
- 2003 9 0.00 0.00 0.00 0.00 2.32 3.59 16.03 3.91 4.41 2.57 4.55 0.89 0.48 1.18 0.24 0.07 0.55 8.54 1.42 2.00 17.56 1.30 0.48 0.00 5.18 0.25 1.04 6.67 2.82 0.51 -99.99
- 2003 10 0.07 0.39 0.39 1.93 7.97 8.71 1.04 2.29 9.90 0.39 2.25 0.07 0.17 0.00 0.00 0.00 0.07 0.16 0.00 2.78 2.11 0.39 0.27 0.32 1.50 0.06 0.31 6.82 5.57 0.83 1.17
- 2003 11 18.79 5.22 3.52 1.83 1.06 0.00 0.00 0.19 0.15 2.22 13.83 4.60 16.26 9.87 1.87 2.24 3.12 6.14 4.83 0.43 0.74 1.59 0.27 4.80 10.26 15.51 2.26 24.56 32.81 2.88 -99.99
- 2003 12 1.49 0.72 0.04 0.32 0.26 0.00 0.00 1.06 1.21 8.92 1.80 11.65 8.91 0.73 0.31 0.88 0.00 0.40 19.03 14.13 0.46 7.22 2.87 7.40 10.48 11.23 3.11 0.71 0.04 0.05 31.23
- 2004 1 0.74 9.50 0.24 6.78 2.87 2.23 9.82 17.15 3.63 5.28 8.32 15.17 6.54 7.27 6.74 0.10 0.21 7.11 7.71 2.10 4.45 0.74 10.29 2.98 0.78 0.11 6.48 2.51 4.17 12.05 27.59
- 2004 2 14.80 17.98 1.77 6.66 2.34 6.50 9.60 0.00 1.54 0.03 0.04 1.62 0.34 0.06 0.17 2.42 0.00 0.16 0.14 0.05 0.00 0.04 2.97 0.32 0.66 0.86 0.06 0.00 0.12 -99.99 -99.99
- 2004 3 0.05 8.49 6.82 3.81 0.33 0.45 0.15 0.11 0.03 0.34 0.62 2.72 7.90 8.36 6.53 3.26 0.71 21.96 12.21 15.37 7.66 3.28 0.30 1.17 0.34 0.65 0.81 0.50 0.00 0.00 0.33
- 2004 4 2.98 7.00 3.91 6.63 3.12 1.22 0.10 2.30 0.08 0.36 0.51 0.64 8.11 8.85 4.28 0.00 20.60 5.77 8.09 7.54 2.52 3.79 0.80 0.14 0.10 0.67 0.10 0.44 0.17 0.00 -99.99
- 2004 5 0.19 1.38 18.94 3.38 5.93 0.64 0.25 2.26 0.15 2.98 0.00 0.10 0.46 0.06 0.00 0.13 0.06 1.40 0.00 0.65 1.45 0.00 0.07 0.13 0.07 0.00 0.87 4.46 1.64 0.00 1.87
- 2004 6 0.93 16.62 1.74 1.22 1.85 0.37 0.00 2.20 2.34 11.15 0.52 0.32 0.57 0.27 0.17 5.80 0.79 0.81 0.79 1.17 0.91 11.04 12.03 1.11 1.62 15.82 4.89 4.96 13.28 2.23 -99.99
- 2004 7 4.28 10.20 1.52 1.56 2.71 0.18 0.11 0.00 0.75 0.58 0.35 0.06 6.65 0.20 1.07 5.95 2.03 0.44 0.98 6.58 1.91 1.13 3.77 12.24 0.19 0.15 1.40 0.30 2.56 0.00 0.91
- 2004 8 0.00 3.57 14.97 1.90 5.16 0.18 0.07 38.07 13.53 23.43 3.59 11.83 0.09 0.89 15.29 4.95 3.18 9.54 5.95 0.78 0.00 4.27 1.14 1.03 0.07 17.55 0.12 3.61 4.90 0.52 0.00
- 2004 9 2.10 0.83 2.79 1.68 0.10 0.00 0.00 0.00 0.00 15.11 3.44 13.58 17.81 2.71 6.14 12.38 10.28 2.31 18.35 8.82 2.17 4.64 0.16 2.82 0.93 2.15 1.55 1.27 0.00 1.71 -99.99
- 2004 10 10.83 3.62 32.07 2.94 13.72 7.16 0.11 0.10 0.00 0.00 0.00 2.84 3.46 2.54 6.69 1.76 2.35 6.08 8.61 26.40 3.94 3.61 10.31 9.34 6.88 3.29 5.62 3.12 2.38 0.00 0.00
- 2004 11 0.00 3.32 6.14 3.86 12.96 3.28 2.00 2.80 3.84 0.25 2.97 0.48 0.55 1.44 6.75 7.90 0.62 0.12 1.01 5.14 15.26 3.94 1.07 0.80 6.50 0.41 6.71 0.05 1.13 3.82 -99.99
- 2004 12 0.23 3.36 1.66 1.12 1.09 1.21 0.81 0.65 1.70 1.14 0.58 0.00 7.20 7.01 95.15 8.77 8.18 0.07 1.39 4.11 17.83 2.04 6.80 10.48 3.25 0.41 13.14 7.02 3.37 10.27 1.53
- 2005 1 12.88 4.28 6.28 1.97 5.02 10.13 41.49 13.45 12.38 1.33 6.56 4.71 0.99 2.95 3.91 6.36 8.99 4.17 6.60 5.04 0.85 0.06 0.50 0.26 0.50 0.06 0.00 0.10 0.33 1.14 0.36
- 2005 2 1.18 1.06 0.88 11.78 0.30 0.06 3.27 8.31 7.19 0.29 28.05 6.56 1.20 0.00 0.07 1.24 1.18 1.29 0.16 0.86 2.36 0.64 0.65 1.65 0.24 0.09 0.36 6.24 -99.99 -99.99 -99.99
- 2005 3 0.19 0.19 2.65 0.22 0.84 0.38 0.05 0.08 1.39 1.43 1.79 0.05 0.14 22.02 7.40 5.65 7.43 1.41 0.04 1.78 14.81 1.63 6.29 1.99 0.08 0.06 7.38 0.90 0.00 0.00 6.36
- 2005 4 1.55 0.06 0.73 2.09 13.63 5.02 0.05 2.11 3.79 0.00 1.82 0.30 9.56 3.49 5.65 1.68 30.24 20.48 0.25 0.00 0.12 0.00 0.00 0.00 3.56 2.98 16.17 3.86 0.12 1.36 -99.99
- 2005 5 6.98 8.42 1.28 1.01 3.44 3.34 1.71 0.68 0.08 0.07 0.00 0.00 0.00 0.00 0.00 0.63 1.20 27.99 5.63 4.63 5.30 7.33 6.01 6.33 19.66 1.55 7.36 2.41 0.00 0.07 11.97
- 2005 6 19.31 8.38 4.75 4.56 0.22 0.00 0.00 0.00 0.13 0.00 0.08 0.87 3.30 11.25 4.36 6.04 0.23 2.79 0.07 4.07 3.82 0.12 0.55 4.36 0.07 0.07 0.12 1.18 7.20 4.04 -99.99
- 2005 7 0.09 2.66 3.59 0.05 3.45 0.19 0.66 1.23 0.10 0.09 0.00 0.29 0.22 5.94 0.10 5.29 2.60 3.07 1.64 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.32 0.29 0.00 0.00
- 2005 8 0.00 3.62 3.40 1.46 0.51 0.36 0.00 0.00 2.03 0.12 4.62 11.61 3.07 0.65 0.93 2.91 28.22 0.92 0.15 0.56 6.97 3.11 19.83 5.02 5.66 5.37 1.05 17.40 0.06 0.21 21.53
- 2005 9 0.00 0.07 0.05 0.41 0.07 0.52 6.39 4.69 2.12 0.00 0.00 6.12 4.55 3.99 0.92 0.74 3.91 1.77 10.89 0.25 1.43 7.92 2.29 9.52 1.98 16.68 3.18 5.94 15.21 6.05 -99.99
- 2005 10 0.69 0.14 0.06 0.00 0.00 0.39 12.54 1.05 4.99 21.65 20.29 1.75 0.10 0.05 0.00 0.25 0.00 11.44 8.83 3.99 2.12 10.30 20.93 15.63 4.75 8.34 0.22 4.88 11.24 4.53 7.37
- 2005 11 4.78 11.24 7.90 4.36 6.12 2.29 11.08 3.97 7.06 5.32 6.81 1.60 1.02 3.16 2.18 0.00 0.10 0.00 0.10 0.16 0.12 0.09 2.00 4.55 2.12 0.76 0.13 1.28 3.49 4.86 -99.99
- 2005 12 22.50 4.75 1.61 5.53 5.30 0.39 15.49 2.34 5.82 0.44 0.31 0.00 0.28 0.56 2.35 0.16 0.10 4.64 0.31 1.35 4.09 4.15 0.21 0.04 0.21 0.06 0.13 0.50 31.29 6.36 6.50
- 2006 1 0.42 5.22 0.08 0.18 0.10 0.13 0.10 0.04 9.81 12.17 1.63 5.52 7.03 1.85 8.16 7.96 3.04 14.86 7.05 5.71 0.05 0.01 0.04 0.00 0.07 0.25 0.24 0.21 0.14 0.16 0.02
- 2006 2 0.00 0.08 0.07 0.00 0.02 0.25 7.72 0.22 0.02 0.66 9.19 2.70 10.43 16.08 1.86 4.42 0.27 0.43 1.36 0.56 0.87 0.72 2.81 0.42 0.36 0.17 2.04 0.07 -99.99 -99.99 -99.99
- 2006 3 0.08 0.41 2.80 1.09 1.07 4.96 10.57 1.53 5.76 5.47 21.77 13.03 16.49 3.11 0.04 0.42 0.00 0.10 0.32 0.15 0.04 0.00 0.00 8.77 17.17 23.03 7.99 2.79 10.92 8.34 6.35
- 2006 4 3.76 3.15 0.69 0.11 3.64 1.84 7.44 0.88 2.27 10.45 2.19 2.61 3.66 0.12 0.95 3.41 0.61 0.94 1.86 1.17 0.00 4.84 0.00 7.09 0.90 0.08 0.02 0.00 0.00 15.65 -99.99
- 2006 5 1.51 7.40 1.07 13.31 0.00 3.79 1.73 0.00 0.03 0.00 0.06 0.08 0.14 9.34 6.65 5.32 12.94 5.55 3.56 3.56 11.04 1.42 5.61 1.92 7.67 2.59 2.24 4.88 0.21 0.00 0.30
- 2006 6 0.00 0.00 0.09 0.03 0.00 0.09 0.00 0.03 0.00 0.26 8.35 0.18 0.00 0.00 0.41 0.95 3.56 13.12 0.02 18.11 10.75 0.24 0.09 0.34 0.10 0.08 0.05 1.22 4.97 4.40 -99.99
- 2006 7 0.19 7.54 0.16 0.47 0.23 1.41 1.54 19.31 0.70 4.44 0.16 1.01 0.03 0.00 0.00 0.00 0.00 0.00 2.12 1.80 0.26 1.90 0.37 0.09 0.05 0.03 2.93 0.93 8.53 4.85 8.09
- 2006 8 1.75 1.97 1.22 3.35 2.11 1.18 0.03 2.81 1.13 0.03 0.00 0.00 0.03 0.16 1.85 0.29 6.68 19.78 2.84 12.41 0.47 3.27 0.20 0.03 4.69 4.27 7.10 3.75 0.58 11.90 5.66
- 2006 9 4.28 20.76 1.24 10.31 15.09 0.44 0.08 0.07 0.03 0.00 12.24 2.01 2.84 14.42 0.13 1.49 1.29 16.38 12.25 23.89 0.17 0.95 5.01 9.30 4.28 4.32 9.56 3.37 6.93 11.54 -99.99
- 2006 10 3.38 8.36 2.03 3.20 6.41 4.70 4.82 7.64 0.55 4.73 10.34 0.12 0.09 0.14 0.11 2.30 7.47 0.86 8.15 3.00 9.69 3.85 4.38 3.48 33.56 6.90 6.17 1.35 7.38 7.48 0.23
- 2006 11 0.09 0.10 0.49 0.01 0.00 1.01 9.42 2.69 0.29 19.94 6.21 7.67 5.50 5.38 37.73 10.96 13.64 8.29 23.02 10.68 2.09 12.37 13.44 12.30 7.48 4.06 7.39 1.14 0.24 24.15 -99.99
- 2006 12 3.09 17.51 11.21 11.25 4.15 12.09 4.58 3.02 10.46 22.97 10.01 18.58 26.64 11.77 3.43 4.42 2.87 0.15 0.11 0.08 0.18 0.33 0.00 0.02 0.63 0.23 6.38 2.99 11.64 1.43 19.60
- 2007 1 17.64 7.91 10.16 3.71 2.22 9.77 19.78 15.92 4.82 17.18 15.16 4.63 12.34 1.08 3.49 6.70 19.69 8.91 13.59 6.41 2.19 0.18 1.51 0.16 0.47 0.23 0.83 0.44 0.17 0.92 1.05
- 2007 2 0.56 0.06 0.32 0.40 0.08 0.18 0.06 0.90 2.03 11.00 6.48 1.38 4.68 0.23 7.62 0.24 0.07 0.65 7.89 5.66 13.83 4.01 5.10 1.07 0.78 13.98 19.18 6.92 -99.99 -99.99 -99.99
- 2007 3 0.34 10.03 1.41 7.79 15.95 2.09 0.95 5.56 3.07 0.27 17.86 1.53 0.55 1.29 1.09 3.07 11.11 4.86 0.54 0.05 3.73 0.09 0.00 0.07 0.00 0.04 0.08 4.21 1.41 0.04 0.00
- 2007 4 0.06 0.00 0.00 0.08 0.02 0.12 0.00 0.45 0.95 0.03 0.00 0.00 0.00 0.00 0.28 0.30 0.47 0.47 0.54 1.20 6.78 2.92 11.37 11.27 0.43 0.00 0.00 0.00 0.00 0.00 -99.99
- 2007 5 0.00 0.00 0.00 0.00 5.12 4.35 8.25 1.00 15.25 2.43 5.73 1.46 0.52 3.61 2.76 11.93 5.26 7.84 4.03 0.25 0.60 0.64 1.24 3.58 2.15 1.18 1.71 0.09 1.55 0.63 3.25
- 2007 6 1.41 19.71 12.41 0.03 0.00 0.00 0.18 0.09 0.00 0.00 0.15 8.57 3.63 4.75 9.88 3.43 0.08 2.99 16.86 1.92 3.91 2.17 5.58 7.13 0.08 4.51 2.82 16.06 0.55 9.19 -99.99
- 2007 7 4.84 4.58 2.03 1.15 9.99 5.75 1.40 0.21 1.28 0.13 3.32 0.74 28.22 0.73 8.76 0.44 1.42 4.01 1.97 0.26 3.70 0.17 1.29 4.62 11.02 2.33 1.44 0.15 0.32 0.04 3.29
- 2007 8 2.97 0.05 6.18 9.44 14.91 4.04 1.20 0.00 2.14 6.08 23.11 7.06 3.79 5.61 1.09 0.87 8.31 22.61 0.51 0.03 0.16 0.00 0.15 0.50 0.89 0.10 0.28 0.39 0.90 1.13 0.77
- 2007 9 3.38 0.65 0.00 2.22 0.92 0.09 0.27 0.21 1.35 0.21 0.51 0.08 3.52 0.29 13.02 16.28 0.78 7.29 1.29 6.50 0.54 2.57 14.29 7.41 0.11 0.05 0.27 0.44 0.17 0.08 -99.99
- 2007 10 0.05 1.19 14.70 0.05 0.10 0.17 0.42 17.16 1.13 1.31 1.02 0.78 0.43 1.29 8.07 0.87 0.45 0.07 0.02 0.04 0.02 0.29 0.11 0.02 0.21 7.12 19.66 4.63 4.41 0.42 3.17
- 2007 11 2.65 0.28 0.05 1.72 0.58 0.78 5.34 1.57 4.18 2.97 0.08 4.43 2.68 0.81 0.11 0.30 24.29 3.62 2.86 5.19 10.61 0.05 9.91 2.71 0.42 1.12 5.35 12.90 8.77 13.19 -99.99
- 2007 12 6.64 3.96 5.65 5.16 9.87 6.31 3.26 21.62 0.29 0.13 0.64 0.77 0.14 0.00 0.00 0.05 0.03 0.02 0.14 0.08 3.26 2.56 11.33 0.61 3.98 6.12 17.32 9.71 4.43 2.01 12.67
- 2008 1 8.03 0.10 4.15 12.41 11.26 9.69 3.02 28.08 9.29 8.71 1.88 17.94 12.22 9.05 9.76 7.10 8.36 8.12 3.56 10.42 12.39 10.11 12.53 7.67 9.60 5.94 0.02 7.02 10.02 14.95 10.70
- 2008 2 1.36 8.92 3.79 5.67 4.12 5.58 2.27 0.60 0.06 0.10 0.02 0.17 0.09 0.05 0.06 0.11 0.03 0.10 0.26 6.91 10.27 8.71 4.17 1.46 14.79 4.94 0.85 4.85 18.37 -99.99 -99.99
- 2008 3 6.10 5.10 4.60 0.81 1.31 6.58 8.69 8.90 10.21 7.98 6.75 5.06 8.10 4.08 0.88 0.28 0.09 0.19 2.63 6.92 0.40 3.37 0.41 0.31 2.33 8.41 14.04 3.67 11.92 1.18 9.32
- 2008 4 5.31 0.46 0.38 1.00 1.56 0.79 2.99 0.93 3.75 4.41 5.43 1.43 0.26 2.66 1.18 0.00 1.08 0.44 0.00 0.00 0.00 7.20 5.28 1.61 5.24 0.68 0.35 1.65 3.88 8.06 -99.99
- 2008 5 0.46 1.39 1.89 4.30 0.00 0.00 0.00 0.15 1.16 2.15 0.23 0.00 0.00 0.00 0.00 2.17 0.10 0.00 0.00 0.00 0.00 6.46 0.03 0.00 0.00 0.00 2.62 9.63 0.10 0.62 0.00
- 2008 6 5.26 1.36 0.05 1.95 4.13 0.67 0.05 0.03 0.16 0.16 1.75 0.09 0.30 0.58 0.04 0.75 5.91 6.34 1.46 0.61 30.44 7.94 0.18 9.41 4.18 2.01 7.27 2.59 1.97 4.47 -99.99
- 2008 7 3.49 4.65 5.69 0.35 6.76 5.81 3.13 0.23 24.06 5.14 1.08 0.07 0.72 0.97 0.88 5.88 5.46 6.44 0.97 0.00 0.60 0.55 0.02 0.00 1.85 0.11 0.00 1.93 3.09 16.03 19.57
- 2008 8 10.91 4.62 12.57 0.03 10.30 24.05 1.29 9.05 22.02 4.51 9.93 10.78 3.90 1.96 6.87 9.40 8.64 7.73 6.61 2.47 5.03 0.11 9.71 2.08 3.00 5.59 1.92 0.28 0.10 0.58 10.00
- 2008 9 7.87 4.02 5.29 0.57 11.60 1.72 1.12 0.21 10.69 10.22 3.77 4.07 0.89 9.25 22.98 8.35 0.19 4.52 1.50 0.22 0.23 0.09 0.14 0.00 0.00 0.03 2.90 2.94 13.37 12.34 -99.99
- 2008 10 6.38 2.63 9.65 13.25 0.03 15.04 14.01 0.47 39.46 15.12 0.18 0.26 0.95 2.70 4.71 1.54 2.68 1.90 13.96 6.34 8.18 9.28 18.63 9.76 27.86 6.41 2.53 1.46 2.90 0.33 1.22
- 2008 11 0.02 0.12 0.21 0.05 0.55 2.75 12.40 12.95 2.80 11.47 5.58 2.48 4.53 3.72 0.02 3.50 4.43 0.44 1.81 0.40 0.80 9.48 5.23 0.33 0.55 4.43 6.07 2.26 0.99 0.93 -99.99
- 2008 12 5.06 3.21 17.58 2.84 1.34 0.22 4.81 1.72 0.41 0.12 3.17 33.94 4.67 0.53 3.41 9.83 9.10 9.10 21.72 4.30 1.01 0.45 0.32 0.07 0.02 0.00 0.20 0.09 0.02 0.08 0.00
- 2009 1 1.53 0.04 0.00 2.85 0.02 2.15 0.87 0.09 0.05 16.20 14.24 0.92 0.57 22.70 6.43 8.10 9.01 11.85 7.33 4.97 20.42 1.72 3.59 10.35 2.64 2.54 2.95 0.14 3.83 7.41 0.00
- 2009 2 0.60 6.34 0.57 1.68 1.52 0.34 0.27 4.66 0.04 2.41 0.65 4.05 0.81 0.96 0.05 0.80 0.02 0.59 0.07 1.33 0.87 0.28 0.27 0.57 1.09 6.10 0.07 5.23 -99.99 -99.99 -99.99
- 2009 3 1.76 5.14 16.00 4.47 2.44 2.17 16.32 10.91 4.18 1.17 6.39 0.61 3.87 0.17 2.58 1.96 0.06 0.19 0.03 0.12 0.21 1.92 0.61 8.65 11.94 6.62 1.83 0.00 1.09 0.34 0.07
- 2009 4 0.02 0.07 10.73 2.33 0.00 4.00 19.66 6.95 3.19 0.64 0.29 0.08 0.37 1.40 0.08 0.00 0.00 0.06 0.00 1.27 0.28 5.70 5.05 3.46 2.32 13.16 4.20 1.90 7.43 4.70 -99.99
- 2009 5 7.58 1.89 5.75 5.63 12.23 8.98 8.62 4.26 7.61 0.24 0.00 0.00 0.78 0.66 6.06 3.27 9.74 4.69 2.59 0.91 1.58 2.02 2.22 1.03 4.33 3.58 1.32 0.21 0.03 0.00 0.00
- 2009 6 0.00 0.00 0.00 0.77 1.44 0.88 0.02 0.05 0.03 0.03 0.56 1.17 1.50 6.21 3.66 19.48 8.74 7.67 4.28 2.01 2.33 0.24 0.03 0.00 0.00 0.18 0.46 1.01 0.49 0.21 -99.99
- 2009 7 4.02 6.34 10.57 0.66 3.95 3.69 0.74 0.12 0.00 0.00 15.97 1.25 9.20 2.66 3.23 3.47 3.48 2.58 1.78 1.93 14.80 5.48 5.48 0.20 9.90 4.37 1.74 6.74 0.63 0.31 10.65
- 2009 8 2.99 1.07 8.86 1.94 0.55 0.71 0.93 0.45 15.28 0.17 5.33 0.36 0.88 43.39 4.58 7.58 2.05 10.92 36.83 10.40 4.79 18.13 14.27 1.40 14.83 5.29 9.92 3.22 2.08 20.29 14.36
- 2009 9 5.49 20.06 5.30 3.52 1.17 10.82 11.14 6.72 0.10 0.06 0.10 0.09 0.02 0.00 0.09 0.05 0.00 0.10 1.02 1.44 5.52 1.22 1.24 0.57 0.06 0.15 1.30 1.64 4.07 0.59 -99.99
- 2009 10 4.05 8.86 4.14 2.11 4.12 0.89 0.68 0.14 11.99 0.55 0.09 0.00 2.32 2.21 0.18 0.02 0.46 9.21 1.95 7.57 0.06 0.84 3.19 20.36 3.18 5.26 9.86 0.04 3.18 16.37 13.72
- 2009 11 33.32 11.29 9.45 7.88 2.32 9.94 2.45 0.13 10.91 0.66 6.32 8.39 18.33 7.26 5.91 14.67 12.74 22.40 32.60 1.78 10.06 13.73 12.68 14.35 10.03 7.58 2.34 3.09 0.27 0.12 -99.99
- 2009 12 14.86 10.58 1.53 14.71 10.64 4.79 4.95 6.29 5.07 0.23 0.22 0.11 0.33 0.97 0.32 1.27 0.89 0.93 3.03 12.09 2.32 6.30 0.74 0.27 6.50 10.77 2.06 0.39 3.88 0.92 0.20
- 2010 1 0.86 1.24 0.13 2.32 0.09 0.06 0.03 0.07 0.30 0.59 2.95 0.84 1.67 4.75 15.93 1.58 7.92 0.55 1.90 3.36 16.20 0.33 4.27 0.48 0.00 1.12 1.14 2.21 0.19 0.70 0.52
- 2010 2 8.72 1.23 4.62 2.47 5.58 0.76 0.67 0.18 0.04 0.12 0.12 0.19 1.47 1.74 4.37 2.47 2.27 1.02 0.15 0.73 1.42 0.18 3.19 19.15 16.24 9.31 0.39 0.88 -99.99 -99.99 -99.99
- 2010 3 2.28 0.12 0.04 0.15 0.12 0.02 0.02 0.05 0.05 0.13 0.35 0.10 0.17 0.25 0.83 1.24 0.19 4.54 2.10 0.42 6.62 3.98 1.72 7.94 17.93 9.08 0.67 4.31 29.73 22.70 0.81
- 2010 4 0.42 4.15 2.58 18.79 10.60 9.51 1.16 0.31 0.07 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.46 1.31 0.76 0.02 0.00 0.00 1.20 2.12 4.33 0.11 11.67 0.75 0.53 0.73 -99.99
- 2010 5 1.76 0.85 0.00 2.26 2.53 0.57 0.06 0.00 0.33 0.00 1.85 0.16 9.47 0.94 4.52 0.15 0.02 4.70 0.49 0.04 0.03 0.15 0.00 0.23 1.20 1.30 2.51 0.59 3.76 0.12 5.88
- 2010 6 1.17 0.02 0.00 0.00 1.29 5.77 5.87 8.86 0.13 0.05 0.16 2.66 0.88 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02 1.09 0.10 0.00 0.03 0.78 5.92 0.03 9.41 -99.99
- 2010 7 6.20 0.73 15.28 7.81 0.63 5.95 0.70 2.81 9.26 16.29 0.30 0.27 9.62 19.47 15.24 2.23 2.75 5.70 7.65 5.97 17.09 0.38 0.00 1.00 2.63 1.79 0.34 0.53 0.74 2.13 1.29
- 2010 8 3.07 0.68 0.35 0.53 1.32 4.66 0.10 2.96 8.13 2.92 1.11 1.07 0.00 0.05 0.00 15.91 0.24 2.44 8.61 2.45 3.86 5.76 12.11 0.88 0.00 0.12 1.65 3.26 0.01 0.03 0.10
- 2010 9 0.12 0.00 0.03 1.29 0.83 26.58 1.32 1.16 8.42 7.16 1.23 6.92 16.48 7.23 0.87 0.37 0.59 7.92 6.99 0.48 7.26 18.55 3.43 0.02 0.05 0.15 0.31 20.31 6.62 4.73 -99.99
- 2010 10 7.37 10.19 0.34 5.32 5.12 1.36 0.27 0.00 0.00 0.05 0.12 0.02 0.05 0.33 0.19 0.06 1.56 3.96 0.87 6.95 7.04 15.03 0.48 0.12 22.50 5.62 6.03 8.45 16.79 0.29 0.05
- 2010 11 20.19 9.74 15.08 8.50 4.08 8.05 22.44 7.70 0.53 18.84 18.50 2.96 8.56 1.31 3.16 4.17 6.04 6.91 0.59 1.61 1.33 0.90 0.05 0.09 0.32 0.31 0.36 0.84 0.67 0.40 -99.99
- 2010 12 0.26 0.74 5.36 0.68 3.14 4.09 0.32 0.55 2.17 0.75 0.00 0.14 0.09 0.21 3.12 1.49 2.09 0.82 0.19 0.30 0.00 0.18 0.14 0.97 0.39 21.15 8.88 3.87 1.78 0.14 0.95
- 2011 1 0.24 0.09 9.74 9.99 3.83 0.32 2.00 6.46 2.42 22.47 8.15 10.59 6.14 9.44 16.38 0.16 1.54 0.93 0.08 0.18 0.35 0.19 0.61 0.74 0.29 1.05 0.07 0.03 0.17 1.01 10.99
- 2011 2 5.43 12.67 15.77 17.65 4.24 26.83 4.95 8.58 13.65 0.65 13.94 16.41 4.14 4.46 4.20 1.63 4.60 14.66 0.94 2.55 3.26 8.40 3.35 2.86 1.75 2.19 0.18 0.21 -99.99 -99.99 -99.99
- 2011 3 0.02 0.08 0.14 0.36 0.02 0.02 0.02 7.28 9.93 5.73 10.88 9.78 6.09 5.19 8.67 2.27 0.45 0.43 3.48 0.89 0.17 0.39 0.05 0.17 0.05 0.07 0.11 0.02 3.64 16.05 5.31
- 2011 4 5.27 2.01 2.03 14.24 13.71 3.76 0.00 0.02 0.00 2.33 1.76 2.58 2.65 0.35 0.42 0.00 0.02 0.29 0.00 0.02 0.02 1.29 2.86 0.24 0.00 0.00 0.03 0.00 0.03 0.00 -99.99
- 2011 5 0.00 0.00 0.03 2.26 14.76 7.15 18.34 1.41 4.50 3.82 5.64 2.95 3.14 2.56 4.54 7.64 2.67 2.17 3.90 1.18 22.47 16.72 6.53 1.65 6.92 0.49 4.51 6.41 1.04 0.48 0.46
- 2011 6 0.04 0.00 0.02 0.00 11.64 4.59 6.61 5.71 2.54 2.10 1.82 5.15 0.07 2.43 0.60 4.24 24.61 5.90 0.84 2.37 15.58 11.65 1.20 8.04 1.46 2.49 0.26 0.30 2.95 0.22 -99.99
- 2011 7 0.00 0.02 0.02 0.53 6.95 9.92 3.41 6.40 2.85 1.72 0.42 0.91 0.00 0.02 16.34 14.68 9.47 3.20 2.95 1.44 0.81 1.72 0.00 0.03 0.00 0.00 4.87 1.83 0.02 0.59 4.17
- 2011 8 4.26 2.83 6.43 2.57 0.04 14.13 3.00 0.06 23.58 25.66 7.20 8.86 1.54 2.87 5.32 4.37 0.34 0.93 2.34 2.06 0.24 0.00 2.76 10.78 3.25 7.50 1.92 1.17 0.54 1.62 0.08
- 2011 9 4.68 11.99 1.57 9.07 12.56 8.57 7.75 6.32 4.38 5.16 21.00 4.98 7.06 0.09 0.03 12.54 6.54 1.84 7.28 2.12 12.48 1.79 4.35 0.23 12.55 1.84 0.31 0.88 0.10 14.42 -99.99
- 2011 10 19.60 1.83 1.21 4.71 11.61 10.40 3.39 18.50 14.89 6.28 19.76 2.67 0.12 3.74 6.92 4.59 30.63 3.59 1.02 10.77 2.20 9.70 8.16 7.58 5.14 7.06 2.09 8.33 7.32 5.03 6.66
- 2011 11 0.44 3.63 4.20 2.68 0.21 0.21 0.16 0.64 8.46 0.10 6.43 0.25 0.02 0.00 0.08 5.59 12.10 2.07 0.71 1.99 9.07 0.95 10.69 13.38 6.99 15.40 1.47 25.78 17.97 13.40 -99.99
- 2011 12 4.19 5.65 8.68 7.80 8.79 13.30 18.78 11.68 6.05 6.50 3.75 10.39 14.58 3.57 12.34 6.18 2.07 6.41 8.82 7.80 0.96 4.45 3.25 5.64 2.78 4.56 10.93 12.10 6.89 17.49 8.11
- 2012 1 4.21 16.53 9.48 27.33 1.15 3.86 1.03 2.17 0.07 1.38 2.43 0.26 0.19 0.00 0.09 0.07 5.89 5.08 14.52 13.92 7.28 2.88 12.20 5.20 12.63 7.30 3.32 0.72 2.75 0.02 0.00
- 2012 2 0.00 0.02 3.61 12.29 0.69 0.08 0.16 8.56 4.76 6.85 1.65 0.42 0.07 0.08 0.25 1.13 10.60 1.61 4.05 10.74 11.73 3.47 1.44 0.32 1.01 5.04 2.01 0.25 0.02 -99.99 -99.99
- 2012 3 0.90 2.03 5.56 0.50 0.12 13.74 4.77 0.53 2.19 0.39 0.17 0.08 0.00 0.00 5.60 7.45 0.17 0.36 2.93 0.00 0.00 0.07 0.02 0.04 0.00 0.03 0.00 0.03 0.00 0.06 0.07
- 2012 4 0.13 7.02 1.45 0.15 0.38 1.37 0.60 3.83 10.91 2.96 2.77 0.51 0.64 0.16 0.00 16.97 3.21 2.33 1.01 1.88 2.89 4.46 0.77 1.97 3.39 1.76 1.15 0.03 5.48 0.13 -99.99
- 2012 5 0.42 0.00 1.01 0.12 0.00 0.60 9.46 0.68 8.54 14.59 1.57 0.26 13.84 2.29 1.42 4.67 5.66 1.16 0.03 0.00 0.03 0.00 0.05 0.00 0.00 0.03 0.00 0.03 0.07 6.96 1.90
- 2012 6 0.05 0.03 0.00 0.00 10.62 0.82 13.48 5.79 2.36 3.11 2.58 0.15 0.18 7.57 26.95 14.87 0.75 2.42 0.05 0.27 24.90 27.94 8.69 0.25 0.00 7.63 11.45 8.88 7.70 3.65 -99.99
- 2012 7 4.98 5.04 8.38 8.05 4.47 9.28 2.63 4.73 3.58 4.71 1.06 0.00 2.32 0.50 1.07 0.24 22.14 11.49 2.24 0.05 0.56 6.61 16.23 2.37 0.10 0.54 2.59 4.27 3.40 1.29 16.77
- 2012 8 5.49 0.12 2.88 2.68 6.21 3.02 0.06 0.00 0.00 0.03 0.00 6.11 3.81 0.66 21.18 23.05 4.36 0.19 2.35 2.72 7.45 4.16 4.32 3.34 5.76 6.93 17.95 5.51 6.85 0.07 3.73
- 2012 9 3.14 0.10 2.19 0.11 0.07 3.09 0.94 0.00 8.31 10.04 8.18 0.73 4.02 0.48 4.82 6.42 8.25 4.41 9.26 19.11 0.38 0.06 3.09 25.11 4.60 1.85 2.56 4.81 20.28 2.39 -99.99
- 2012 10 6.74 17.54 3.24 7.77 1.54 0.13 0.13 0.03 0.04 4.29 39.86 1.66 7.64 2.10 7.71 4.75 22.20 13.25 2.90 0.14 0.15 0.21 0.43 0.02 0.11 0.04 7.74 5.82 1.41 10.74 12.24
- 2012 11 5.77 4.39 2.04 1.33 1.56 0.86 4.39 7.46 5.70 3.37 3.99 9.26 12.35 2.44 1.73 4.39 3.11 31.22 5.75 2.84 8.39 19.36 1.56 7.63 5.05 0.71 0.02 0.12 0.35 4.86 -99.99
- 2012 12 0.26 10.64 6.68 4.23 3.17 16.29 0.17 2.66 0.02 0.03 0.65 2.73 0.30 15.08 0.37 7.09 0.56 0.13 17.22 27.94 4.48 26.56 3.25 11.65 4.57 19.66 10.19 4.95 5.55 17.88 5.07
- 2013 1 7.26 2.75 0.94 0.49 3.36 10.35 24.26 0.35 0.12 1.69 1.36 1.79 8.14 1.60 0.92 1.90 1.31 0.43 0.29 0.33 4.81 0.53 0.36 3.29 18.70 25.41 2.90 14.24 11.26 13.87 8.81
- 2013 2 0.27 5.50 4.93 8.05 4.26 0.37 1.76 4.47 7.78 6.70 0.00 6.48 25.13 0.65 0.43 0.74 0.00 0.09 0.24 0.00 0.02 0.00 0.12 1.08 0.10 0.10 0.33 0.02 -99.99 -99.99 -99.99
- 2013 3 0.02 0.12 0.00 0.10 0.00 4.58 2.95 2.93 3.02 0.70 0.51 0.07 0.65 5.66 3.35 6.85 7.77 1.43 2.78 0.24 1.44 7.58 0.70 0.06 0.03 0.23 0.47 0.10 0.04 0.08 0.00
- 2013 4 0.00 0.00 0.03 0.38 0.04 0.00 0.00 0.00 0.02 0.95 6.94 1.53 13.49 2.91 6.21 10.35 15.20 1.61 0.00 4.31 1.49 0.91 8.42 3.77 2.29 3.04 1.22 3.57 0.19 0.31 -99.99
- 2013 5 0.22 3.10 10.87 0.45 1.14 0.36 1.69 5.24 6.70 8.11 2.85 9.35 10.44 0.88 0.95 0.25 9.48 21.95 0.00 1.28 0.02 0.48 0.49 0.02 0.26 15.41 9.43 1.15 1.05 0.52 0.16
- 2013 6 0.30 0.37 0.00 0.05 0.16 0.02 0.00 0.18 0.03 0.51 15.01 1.15 0.48 15.87 6.12 0.26 0.02 0.00 0.04 2.20 6.11 5.59 0.34 0.00 0.11 3.08 7.67 3.16 0.46 2.99 -99.99
- 2013 7 1.38 13.23 6.56 0.47 0.05 0.87 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.02 0.22 0.02 0.05 0.02 0.00 0.00 0.00 5.55 8.99 7.86 7.68 0.23 15.13 4.11 6.83 4.16 14.74
- 2013 8 3.86 0.93 3.03 1.02 0.20 1.56 0.15 10.98 0.38 2.26 2.99 0.93 0.00 13.94 10.51 2.40 6.61 0.97 0.14 5.75 1.70 0.08 1.75 0.39 0.08 0.23 0.13 0.29 0.24 1.04 0.67
- 2013 9 0.84 0.66 0.04 0.42 0.26 25.14 7.62 2.10 1.00 0.28 4.99 6.68 1.41 12.22 14.38 4.99 2.95 10.93 3.22 0.40 0.19 0.16 0.35 0.66 0.14 2.15 0.31 0.00 0.00 1.66 -99.99
- 2013 10 5.63 17.56 19.28 1.50 0.69 3.50 4.92 1.64 0.98 0.00 0.00 0.00 0.66 0.75 0.13 15.45 3.04 23.19 12.84 5.41 21.21 7.79 2.77 8.41 7.00 11.45 8.47 5.73 2.31 8.51 5.38
- 2013 11 1.33 27.77 1.11 1.50 5.58 3.04 3.95 2.81 1.93 11.03 2.13 0.80 6.71 0.41 0.11 0.36 9.08 3.82 13.15 0.27 0.05 0.35 0.14 0.05 0.28 1.25 0.16 3.29 0.98 0.51 -99.99
- 2013 12 0.22 0.32 2.83 15.68 2.43 3.74 3.12 6.94 0.00 0.58 11.08 6.57 2.48 14.28 8.51 1.67 4.59 17.14 3.77 17.59 11.11 4.11 18.86 7.48 1.58 13.39 9.61 3.45 37.27 16.56 5.17
- 2014 1 15.55 7.22 8.02 5.12 12.23 5.06 3.51 0.89 1.65 9.54 0.47 4.69 8.82 16.01 9.62 5.06 2.76 13.07 3.18 2.82 12.93 8.08 3.43 12.63 26.69 13.81 9.03 4.01 0.62 0.41 14.07
- 2014 2 11.20 0.65 9.04 5.27 4.00 1.56 9.70 9.76 2.92 7.49 3.35 21.29 3.72 23.74 2.85 5.05 6.96 5.16 13.36 4.99 3.64 8.33 15.35 6.38 5.72 16.19 2.70 1.14 -99.99 -99.99 -99.99
- 2014 3 10.71 7.68 1.35 0.37 9.20 26.19 4.77 1.13 5.30 0.18 0.06 0.25 0.25 0.33 0.22 0.51 3.03 2.68 4.69 14.84 8.22 3.45 0.36 1.42 3.48 0.75 2.04 5.25 0.66 0.04 8.62
- 2014 4 1.68 2.71 11.29 2.45 7.28 3.64 4.86 2.31 0.83 0.61 1.61 0.54 2.06 0.08 0.02 1.03 0.26 0.02 0.06 0.00 0.27 3.95 2.03 0.18 8.03 0.59 2.15 0.05 0.07 8.65 -99.99
- 2014 5 0.82 0.00 2.45 2.12 5.40 6.21 12.90 5.87 8.33 6.52 6.52 4.03 0.39 0.30 0.00 0.52 8.11 3.76 18.49 4.04 0.06 2.19 0.47 4.43 8.63 2.23 1.60 4.81 0.00 0.00 0.07
- 2014 6 6.72 2.00 5.05 7.69 0.62 1.57 17.35 1.48 5.56 2.58 0.02 0.81 0.75 0.88 0.15 0.11 0.05 0.03 0.00 0.00 0.03 0.11 0.47 2.51 7.34 0.39 0.00 0.31 0.02 0.03 -99.99
- 2014 7 0.07 1.62 4.74 8.89 2.46 1.23 2.98 1.19 0.00 0.15 0.46 13.96 0.17 8.33 4.11 4.60 0.01 0.26 12.89 0.08 0.02 0.00 0.03 0.00 0.00 6.43 6.16 0.33 1.24 1.31 2.80
- 2014 8 3.38 22.81 4.90 0.00 8.74 2.56 1.40 7.18 8.29 17.68 5.17 8.20 0.97 2.57 0.59 3.15 1.73 0.93 0.36 4.41 1.91 0.65 1.39 0.03 0.00 0.03 5.84 8.16 6.49 0.17 4.10
- 2014 9 0.00 0.00 0.00 0.52 2.26 0.10 0.03 0.00 0.00 0.02 0.03 0.05 0.00 0.12 2.36 0.07 0.06 0.19 0.29 0.12 0.00 2.06 4.51 0.87 1.82 0.10 0.84 1.12 0.03 3.48 -99.99
- 2014 10 0.27 2.91 36.67 1.22 18.65 3.23 3.32 8.60 6.75 3.90 1.34 0.33 0.49 0.03 4.87 15.18 8.01 6.34 4.45 10.34 3.03 2.87 3.73 3.47 2.91 11.23 19.85 11.08 4.65 1.32 7.64
- 2014 11 16.75 7.19 5.09 1.02 9.24 36.41 1.81 12.25 1.12 5.06 11.03 4.63 8.67 5.41 0.06 2.86 0.51 0.50 0.64 0.11 18.14 2.94 0.97 1.70 1.56 0.25 0.27 0.08 1.03 0.16 -99.99
- 2014 12 3.74 0.48 0.25 2.52 2.29 17.36 11.18 1.14 17.82 10.96 4.85 2.34 5.07 3.55 2.60 12.31 6.19 8.73 7.94 2.19 33.45 21.93 6.12 6.01 2.35 5.25 0.46 0.18 0.30 2.07 9.83
- 2015 1 19.05 7.62 0.00 0.24 9.30 8.97 16.53 9.31 19.07 11.05 16.66 5.24 3.88 24.27 8.67 9.76 3.24 0.26 0.31 6.09 0.22 1.17 9.99 1.68 7.41 1.85 8.68 7.39 7.16 1.63 0.28
- 2015 2 0.02 0.37 0.28 0.02 0.13 0.10 0.13 0.41 0.27 0.00 0.25 1.27 3.31 0.00 16.95 1.81 2.32 6.94 3.99 2.91 2.84 22.10 9.42 6.50 13.51 3.12 3.49 20.18 -99.99 -99.99 -99.99
- 2015 3 5.94 4.58 6.49 0.12 1.41 6.67 8.42 1.26 5.95 0.89 9.85 23.19 0.19 0.00 0.12 0.24 0.09 0.04 0.52 0.24 0.00 0.68 1.37 1.21 14.97 0.60 16.42 9.83 5.97 15.86 6.29
- 2015 4 2.66 7.28 1.12 0.22 0.22 0.04 0.05 0.02 0.06 6.85 6.94 2.47 1.17 6.00 0.16 0.00 0.06 0.02 0.21 0.02 0.02 0.04 0.00 5.09 0.04 0.10 6.44 11.16 4.31 0.58 -99.99
- 2015 5 0.00 19.90 7.34 4.35 18.24 1.94 0.24 6.99 3.86 11.89 1.11 0.72 0.00 0.00 5.48 3.89 11.87 3.24 0.94 0.21 1.08 0.11 6.27 0.50 0.16 0.13 13.73 5.06 0.75 12.40 2.69
- 2015 6 19.53 1.57 0.53 3.29 3.38 2.85 0.05 0.00 0.00 0.00 0.00 0.02 0.42 0.02 0.76 4.46 0.37 0.85 1.96 2.29 2.63 0.12 0.07 1.75 7.52 4.03 16.25 1.43 1.25 0.02 -99.99
- 2015 7 1.39 0.78 15.84 2.48 3.97 14.86 4.21 0.53 4.63 2.40 7.62 4.38 1.91 0.07 0.03 14.79 5.37 10.56 1.51 3.86 0.78 1.11 0.91 0.17 0.65 7.42 12.51 5.01 0.97 0.43 8.87
- 2015 8 2.51 4.55 5.71 2.41 16.28 0.05 0.08 2.16 1.01 4.75 0.05 0.00 2.77 2.12 0.12 0.34 0.00 0.36 9.90 3.08 0.50 14.29 6.13 0.06 15.50 7.68 2.36 1.65 1.05 0.24 3.94
- 2015 9 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99
- 2015 10 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99
- 2015 11 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99
- 2015 12 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99
diff --git a/data/ccpp_data.npz b/data/ccpp_data.npz
deleted file mode 100644
index a507ba2..0000000
Binary files a/data/ccpp_data.npz and /dev/null differ
diff --git a/data_providers.py b/data_providers.py
new file mode 100644
index 0000000..03f7d94
--- /dev/null
+++ b/data_providers.py
@@ -0,0 +1,740 @@
+# -*- coding: utf-8 -*-
+"""Data providers.
+
+This module provides classes for loading datasets and iterating over batches of
+data points.
+"""
+
+import os
+
+import numpy as np
+DEFAULT_SEED = 22012018
+
+
+class DataProvider(object):
+ """Generic data provider."""
+
+ def __init__(self, inputs, targets, batch_size, max_num_batches=-1,
+ random_sampling=True, rng=None):
+ """Create a new data provider object.
+
+ Args:
+ inputs (ndarray): Array of data input features of shape
+ (num_data, input_dim).
+ targets (ndarray): Array of data output targets of shape
+ (num_data, output_dim) or (num_data,) if output_dim == 1.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ self.inputs = inputs
+ self.targets = targets
+
+ if batch_size < 1:
+ raise ValueError('batch_size must be >= 1')
+
+ self._batch_size = batch_size
+
+ if max_num_batches == 0 or max_num_batches < -1:
+ raise ValueError('max_num_batches must be -1 or > 0')
+
+ self._max_num_batches = max_num_batches
+ self._update_num_batches()
+ self.random_sampling = random_sampling
+ self._current_order = np.arange(inputs.shape[0])
+
+ if rng is None:
+ rng = np.random.RandomState(DEFAULT_SEED)
+
+ self.rng = rng
+ self.new_epoch()
+
+ @property
+ def batch_size(self):
+ """Number of data points to include in each batch."""
+ return self._batch_size
+
+ @batch_size.setter
+ def batch_size(self, value):
+ if value < 1:
+ raise ValueError('batch_size must be >= 1')
+ self._batch_size = value
+ self._update_num_batches()
+
+ @property
+ def max_num_batches(self):
+ """Maximum number of batches to iterate over in an epoch."""
+ return self._max_num_batches
+
+ @max_num_batches.setter
+ def max_num_batches(self, value):
+ if value == 0 or value < -1:
+ raise ValueError('max_num_batches must be -1 or > 0')
+ self._max_num_batches = value
+ self._update_num_batches()
+
+ def _update_num_batches(self):
+ """Updates number of batches to iterate over."""
+ # maximum possible number of batches is equal to number of whole times
+ # batch_size divides in to the number of data points which can be
+ # found using integer division
+ possible_num_batches = self.inputs.shape[0] // self.batch_size
+ if self.max_num_batches == -1:
+ self.num_batches = possible_num_batches
+ else:
+ self.num_batches = min(self.max_num_batches, possible_num_batches)
+
+ def __iter__(self):
+ """Implements Python iterator interface.
+
+ This should return an object implementing a `next` method which steps
+ through a sequence returning one element at a time and raising
+ `StopIteration` when at the end of the sequence. Here the object
+ returned is the DataProvider itself.
+ """
+ return self
+
+ def new_epoch(self):
+ """Starts a new epoch (pass through data), possibly shuffling first."""
+ self._curr_batch = 0
+
+ def __next__(self):
+ return self.next()
+
+ def reset(self):
+ """Resets the provider to the initial state."""
+ inv_perm = np.argsort(self._current_order)
+ self._current_order = self._current_order[inv_perm]
+ self.inputs = self.inputs[inv_perm]
+ self.targets = self.targets[inv_perm]
+ self.new_epoch()
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ if self._curr_batch + 1 > self.num_batches:
+ # no more batches in current iteration through data set so start
+ # new epoch ready for another pass and indicate iteration is at end
+ self.new_epoch()
+ raise StopIteration()
+ # create an index slice corresponding to current batch number
+ if self.random_sampling:
+ batch_slice = self.rng.choice(self.inputs.shape[0], size=self.batch_size, replace=False)
+
+ else:
+ batch_slice = slice(self._curr_batch * self.batch_size,
+ (self._curr_batch + 1) * self.batch_size)
+
+ inputs_batch = self.inputs[batch_slice]
+ targets_batch = self.targets[batch_slice]
+ self._curr_batch += 1
+ return inputs_batch, targets_batch
+
+class MNISTDataProvider(DataProvider):
+ """Data provider for MNIST handwritten digit images."""
+
+ def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+ random_sampling=False, rng=None):
+ """Create a new MNIST data provider object.
+
+ Args:
+ which_set: One of 'train', 'valid' or 'eval'. Determines which
+ portion of the MNIST data this object should provide.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ # check a valid which_set was provided
+ assert which_set in ['train', 'valid', 'test'], (
+ 'Expected which_set to be either train, valid or eval. '
+ 'Got {0}'.format(which_set)
+ )
+ self.which_set = which_set
+ self.num_classes = 10
+ # construct path to data using os.path.join to ensure the correct path
+ # separator for the current platform / OS is used
+ # MLP_DATA_DIR environment variable should point to the data directory
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'mnist-{0}.npz'.format(which_set))
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ # load data from compressed numpy file
+ loaded = np.load(data_path)
+ inputs, targets = loaded['inputs'], loaded['targets']
+ inputs = inputs.astype(np.float32)
+ # pass the loaded data to the parent class __init__
+ super(MNISTDataProvider, self).__init__(
+ inputs, targets, batch_size, max_num_batches, random_sampling, rng)
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
+ return inputs_batch, self.to_one_of_k(targets_batch)
+
+ def to_one_of_k(self, int_targets):
+ """Converts integer coded class target to 1 of K coded targets.
+
+ Args:
+ int_targets (ndarray): Array of integer coded class targets (i.e.
+ where an integer from 0 to `num_classes` - 1 is used to
+ indicate which is the correct class). This should be of shape
+ (num_data,).
+
+ Returns:
+ Array of 1 of K coded targets i.e. an array of shape
+ (num_data, num_classes) where for each row all elements are equal
+ to zero except for the column corresponding to the correct class
+ which is equal to one.
+ """
+ one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+ one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+ return one_of_k_targets
+
+class EMNISTDataProvider(DataProvider):
+ """Data provider for EMNIST handwritten digit images."""
+
+ def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+ random_sampling=False, rng=None, flatten=False, one_hot=False):
+ """Create a new EMNIST data provider object.
+
+ Args:
+ which_set: One of 'train', 'valid' or 'eval'. Determines which
+ portion of the EMNIST data this object should provide.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ # check a valid which_set was provided
+ assert which_set in ['train', 'valid', 'test'], (
+ 'Expected which_set to be either train, valid or eval. '
+ 'Got {0}'.format(which_set)
+ )
+ self.one_hot = one_hot
+ self.which_set = which_set
+ self.num_classes = 47
+ # construct path to data using os.path.join to ensure the correct path
+ # separator for the current platform / OS is used
+ # MLP_DATA_DIR environment variable should point to the data directory
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'emnist-{0}.npz'.format(which_set))
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ # load data from compressed numpy file
+ loaded = np.load(data_path)
+
+ inputs, targets = loaded['inputs'], loaded['targets']
+ inputs = inputs.astype(np.float32)
+ if flatten:
+ inputs = np.reshape(inputs, newshape=(-1, 28*28))
+ else:
+ inputs = np.expand_dims(inputs, axis=3)
+ inputs = inputs / 255.0
+
+ # pass the loaded data to the parent class __init__
+ super(EMNISTDataProvider, self).__init__(
+ inputs, targets, batch_size, max_num_batches, random_sampling, rng)
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ inputs_batch, targets_batch = super(EMNISTDataProvider, self).next()
+ if self.one_hot:
+ return inputs_batch, self.to_one_of_k(targets_batch)
+ else:
+ return inputs_batch, targets_batch
+
+ def to_one_of_k(self, int_targets):
+ """Converts integer coded class target to 1 of K coded targets.
+
+ Args:
+ int_targets (ndarray): Array of integer coded class targets (i.e.
+ where an integer from 0 to `num_classes` - 1 is used to
+ indicate which is the correct class). This should be of shape
+ (num_data,).
+
+ Returns:
+ Array of 1 of K coded targets i.e. an array of shape
+ (num_data, num_classes) where for each row all elements are equal
+ to zero except for the column corresponding to the correct class
+ which is equal to one.
+ """
+ one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+ one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+ return one_of_k_targets
+
+class CIFAR10DataProvider(DataProvider):
+ """Data provider for CIFAR-10 object images."""
+
+ def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+ random_sampling=False, rng=None, flatten=False, one_hot=False):
+ """Create a new EMNIST data provider object.
+
+ Args:
+ which_set: One of 'train', 'valid' or 'eval'. Determines which
+ portion of the EMNIST data this object should provide.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ # check a valid which_set was provided
+ assert which_set in ['train', 'valid', 'test'], (
+ 'Expected which_set to be either train, valid or eval. '
+ 'Got {0}'.format(which_set)
+ )
+ self.one_hot = one_hot
+ self.which_set = which_set
+ self.num_classes = 10
+ # construct path to data using os.path.join to ensure the correct path
+ # separator for the current platform / OS is used
+ # MLP_DATA_DIR environment variable should point to the data directory
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'cifar10-{0}.npz'.format(which_set))
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ # load data from compressed numpy file
+ loaded = np.load(data_path)
+
+ inputs, targets = loaded['inputs'], loaded['targets']
+ inputs = inputs.astype(np.float32)
+ if flatten:
+ inputs = np.reshape(inputs, newshape=(-1, 32*32*3))
+ else:
+ inputs = np.reshape(inputs, newshape=(-1, 3, 32, 32))
+ inputs = np.transpose(inputs, axes=(0, 2, 3, 1))
+
+ inputs = inputs / 255.0
+ # label map gives strings corresponding to integer label targets
+
+
+ # pass the loaded data to the parent class __init__
+ super(CIFAR10DataProvider, self).__init__(
+ inputs, targets, batch_size, max_num_batches, random_sampling, rng)
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ inputs_batch, targets_batch = super(CIFAR10DataProvider, self).next()
+ if self.one_hot:
+ return inputs_batch, self.to_one_of_k(targets_batch)
+ else:
+ return inputs_batch, targets_batch
+
+ def to_one_of_k(self, int_targets):
+ """Converts integer coded class target to 1 of K coded targets.
+
+ Args:
+ int_targets (ndarray): Array of integer coded class targets (i.e.
+ where an integer from 0 to `num_classes` - 1 is used to
+ indicate which is the correct class). This should be of shape
+ (num_data,).
+
+ Returns:
+ Array of 1 of K coded targets i.e. an array of shape
+ (num_data, num_classes) where for each row all elements are equal
+ to zero except for the column corresponding to the correct class
+ which is equal to one.
+ """
+ one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+ one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+ return one_of_k_targets
+
+
+
+class CIFAR100DataProvider(DataProvider):
+ """Data provider for CIFAR-100 object images."""
+
+ def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+ random_sampling=False, rng=None, flatten=False, one_hot=False):
+ """Create a new EMNIST data provider object.
+
+ Args:
+ which_set: One of 'train', 'valid' or 'eval'. Determines which
+ portion of the EMNIST data this object should provide.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ # check a valid which_set was provided
+ assert which_set in ['train', 'valid', 'test'], (
+ 'Expected which_set to be either train, valid or eval. '
+ 'Got {0}'.format(which_set)
+ )
+ self.one_hot = one_hot
+ self.which_set = which_set
+ self.num_classes = 100
+ # construct path to data using os.path.join to ensure the correct path
+ # separator for the current platform / OS is used
+ # MLP_DATA_DIR environment variable should point to the data directory
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'cifar100-{0}.npz'.format(which_set))
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ # load data from compressed numpy file
+ loaded = np.load(data_path)
+
+ inputs, targets = loaded['inputs'], loaded['targets']
+ inputs = inputs.astype(np.float32)
+ if flatten:
+ inputs = np.reshape(inputs, newshape=(-1, 32*32*3))
+ else:
+ inputs = np.reshape(inputs, newshape=(-1, 3, 32, 32))
+ inputs = np.transpose(inputs, axes=(0, 2, 3, 1))
+ inputs = inputs / 255.0
+
+ # pass the loaded data to the parent class __init__
+ super(CIFAR100DataProvider, self).__init__(
+ inputs, targets, batch_size, max_num_batches, random_sampling, rng)
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ inputs_batch, targets_batch = super(CIFAR100DataProvider, self).next()
+ if self.one_hot:
+ return inputs_batch, self.to_one_of_k(targets_batch)
+ else:
+ return inputs_batch, targets_batch
+
+ def to_one_of_k(self, int_targets):
+ """Converts integer coded class target to 1 of K coded targets.
+
+ Args:
+ int_targets (ndarray): Array of integer coded class targets (i.e.
+ where an integer from 0 to `num_classes` - 1 is used to
+ indicate which is the correct class). This should be of shape
+ (num_data,).
+
+ Returns:
+ Array of 1 of K coded targets i.e. an array of shape
+ (num_data, num_classes) where for each row all elements are equal
+ to zero except for the column corresponding to the correct class
+ which is equal to one.
+ """
+ one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+ one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+ return one_of_k_targets
+
+
+class MSD10GenreDataProvider(DataProvider):
+ """Data provider for Million Song Dataset 10-genre classification task."""
+
+ def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+ random_sampling=False, rng=None, one_hot=False, flatten=True):
+ """Create a new EMNIST data provider object.
+
+ Args:
+ which_set: One of 'train', 'valid' or 'eval'. Determines which
+ portion of the EMNIST data this object should provide.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ # check a valid which_set was provided
+ assert which_set in ['train', 'valid', 'test'], (
+ 'Expected which_set to be either train, valid or eval. '
+ 'Got {0}'.format(which_set)
+ )
+ self.one_hot = one_hot
+ self.which_set = which_set
+ self.num_classes = 10
+ # construct path to data using os.path.join to ensure the correct path
+ # separator for the current platform / OS is used
+ # MLP_DATA_DIR environment variable should point to the data directory
+ if which_set is not "test":
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'msd10-{0}.npz'.format(which_set))
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ # load data from compressed numpy file
+ loaded = np.load(data_path)
+
+ inputs, target = loaded['inputs'], loaded['targets']
+ else:
+ input_data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'msd-10-genre-test-inputs.npz')
+ assert os.path.isfile(input_data_path), (
+ 'Data file does not exist at expected path: ' + input_data_path
+ )
+ target_data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'msd-10-genre-test-targets.npz')
+ assert os.path.isfile(input_data_path), (
+ 'Data file does not exist at expected path: ' + input_data_path
+ )
+ # load data from compressed numpy file
+ inputs = np.load(input_data_path)['inputs']
+ target = np.load(target_data_path)['targets']
+ if flatten:
+ inputs = inputs.reshape((-1, 120*25))
+ #inputs, targets = loaded['inputs'], loaded['targets']
+
+
+ # label map gives strings corresponding to integer label targets
+
+ # pass the loaded data to the parent class __init__
+ super(MSD10GenreDataProvider, self).__init__(
+ inputs, target, batch_size, max_num_batches, random_sampling, rng)
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ inputs_batch, targets_batch = super(MSD10GenreDataProvider, self).next()
+ if self.one_hot:
+ return inputs_batch, self.to_one_of_k(targets_batch)
+ else:
+ return inputs_batch, targets_batch
+
+ def to_one_of_k(self, int_targets):
+ """Converts integer coded class target to 1 of K coded targets.
+
+ Args:
+ int_targets (ndarray): Array of integer coded class targets (i.e.
+ where an integer from 0 to `num_classes` - 1 is used to
+ indicate which is the correct class). This should be of shape
+ (num_data,).
+
+ Returns:
+ Array of 1 of K coded targets i.e. an array of shape
+ (num_data, num_classes) where for each row all elements are equal
+ to zero except for the column corresponding to the correct class
+ which is equal to one.
+ """
+ one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+ one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+ return one_of_k_targets
+
+class MSD25GenreDataProvider(DataProvider):
+ """Data provider for Million Song Dataset 25-genre classification task."""
+
+ def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+ random_sampling=False, rng=None, one_hot=False, flatten=True):
+ """Create a new EMNIST data provider object.
+
+ Args:
+ which_set: One of 'train', 'valid' or 'eval'. Determines which
+ portion of the EMNIST data this object should provide.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ # check a valid which_set was provided
+ assert which_set in ['train', 'valid', 'test'], (
+ 'Expected which_set to be either train or valid. '
+ 'Got {0}'.format(which_set)
+ )
+ self.one_hot = one_hot
+ self.which_set = which_set
+ self.num_classes = 25
+ # construct path to data using os.path.join to ensure the correct path
+ # separator for the current platform / OS is used
+ # MLP_DATA_DIR environment variable should point to the data directory
+
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'msd10-{0}.npz'.format(which_set))
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ # load data from compressed numpy file
+ loaded = np.load(data_path)
+
+ inputs, target = loaded['inputs'], loaded['targets']
+
+ if flatten:
+ inputs = inputs.reshape((-1, 120*25))
+ #inputs, target
+ # pass the loaded data to the parent class __init__
+ super(MSD25GenreDataProvider, self).__init__(
+ inputs, target, batch_size, max_num_batches, random_sampling, rng)
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ inputs_batch, targets_batch = super(MSD25GenreDataProvider, self).next()
+ if self.one_hot:
+ return inputs_batch, self.to_one_of_k(targets_batch)
+ else:
+ return inputs_batch, targets_batch
+
+ def to_one_of_k(self, int_targets):
+ """Converts integer coded class target to 1 of K coded targets.
+
+ Args:
+ int_targets (ndarray): Array of integer coded class targets (i.e.
+ where an integer from 0 to `num_classes` - 1 is used to
+ indicate which is the correct class). This should be of shape
+ (num_data,).
+
+ Returns:
+ Array of 1 of K coded targets i.e. an array of shape
+ (num_data, num_classes) where for each row all elements are equal
+ to zero except for the column corresponding to the correct class
+ which is equal to one.
+ """
+ one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+ one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+ return one_of_k_targets
+
+
+
+class MetOfficeDataProvider(DataProvider):
+ """South Scotland Met Office weather data provider."""
+
+ def __init__(self, window_size, batch_size=10, max_num_batches=-1,
+ random_sampling=False, rng=None):
+ """Create a new Met Office data provider object.
+
+ Args:
+ window_size (int): Size of windows to split weather time series
+ data into. The constructed input features will be the first
+ `window_size - 1` entries in each window and the target outputs
+ the last entry in each window.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'HadSSP_daily_qc.txt')
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ raw = np.loadtxt(data_path, skiprows=3, usecols=range(2, 32))
+ assert window_size > 1, 'window_size must be at least 2.'
+ self.window_size = window_size
+ # filter out all missing datapoints and flatten to a vector
+ filtered = raw[raw >= 0].flatten()
+ # normalise data to zero mean, unit standard deviation
+ mean = np.mean(filtered)
+ std = np.std(filtered)
+ normalised = (filtered - mean) / std
+ # create a view on to array corresponding to a rolling window
+ shape = (normalised.shape[-1] - self.window_size + 1, self.window_size)
+ strides = normalised.strides + (normalised.strides[-1],)
+ windowed = np.lib.stride_tricks.as_strided(
+ normalised, shape=shape, strides=strides)
+ # inputs are first (window_size - 1) entries in windows
+ inputs = windowed[:, :-1]
+ # targets are last entry in windows
+ targets = windowed[:, -1]
+ super(MetOfficeDataProvider, self).__init__(
+ inputs, targets, batch_size, max_num_batches, random_sampling, rng)
+
+class CCPPDataProvider(DataProvider):
+
+ def __init__(self, which_set='train', input_dims=None, batch_size=10,
+ max_num_batches=-1, random_sampling=False, rng=None):
+ """Create a new Combined Cycle Power Plant data provider object.
+
+ Args:
+ which_set: One of 'train' or 'valid'. Determines which portion of
+ data this object should provide.
+ input_dims: Which of the four input dimension to use. If `None` all
+ are used. If an iterable of integers are provided (consisting
+ of a subset of {0, 1, 2, 3}) then only the corresponding
+ input dimensions are included.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ """
+ data_path = os.path.join(
+ os.environ['MLP_DATA_DIR'], 'ccpp_data.npz')
+ assert os.path.isfile(data_path), (
+ 'Data file does not exist at expected path: ' + data_path
+ )
+ # check a valid which_set was provided
+ assert which_set in ['train', 'valid'], (
+ 'Expected which_set to be either train or valid '
+ 'Got {0}'.format(which_set)
+ )
+ # check input_dims are valid
+ if not input_dims is not None:
+ input_dims = set(input_dims)
+ assert input_dims.issubset({0, 1, 2, 3}), (
+ 'input_dims should be a subset of {0, 1, 2, 3}'
+ )
+ loaded = np.load(data_path)
+ inputs = loaded[which_set + '_inputs']
+ if input_dims is not None:
+ inputs = inputs[:, input_dims]
+ targets = loaded[which_set + '_targets']
+ super(CCPPDataProvider, self).__init__(
+ inputs, targets, batch_size, max_num_batches, random_sampling, rng)
+
+
+class AugmentedMNISTDataProvider(MNISTDataProvider):
+ """Data provider for MNIST dataset which randomly transforms images."""
+
+ def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+ random_sampling=False, rng=None, transformer=None):
+ """Create a new augmented MNIST data provider object.
+
+ Args:
+ which_set: One of 'train', 'valid' or 'test'. Determines which
+ portion of the MNIST data this object should provide.
+ batch_size (int): Number of data points to include in each batch.
+ max_num_batches (int): Maximum number of batches to iterate over
+ in an epoch. If `max_num_batches * batch_size > num_data` then
+ only as many batches as the data can be split into will be
+ used. If set to -1 all of the data will be used.
+ random_sampling (bool): Whether to randomly permute the order of
+ the data before each epoch.
+ rng (RandomState): A seeded random number generator.
+ transformer: Function which takes an `inputs` array of shape
+ (batch_size, input_dim) corresponding to a batch of input
+ images and a `rng` random number generator object (i.e. a
+ call signature `transformer(inputs, rng)`) and applies a
+ potentiall random set of transformations to some / all of the
+ input images as each new batch is returned when iterating over
+ the data provider.
+ """
+ super(AugmentedMNISTDataProvider, self).__init__(
+ which_set, batch_size, max_num_batches, random_sampling, rng)
+ self.transformer = transformer
+
+ def next(self):
+ """Returns next data batch or raises `StopIteration` if at end."""
+ inputs_batch, targets_batch = super(
+ AugmentedMNISTDataProvider, self).next()
+ transformed_inputs_batch = self.transformer(inputs_batch, self.rng)
+ return transformed_inputs_batch, targets_batch
diff --git a/emnist_network_trainer.py b/emnist_network_trainer.py
new file mode 100644
index 0000000..b848c29
--- /dev/null
+++ b/emnist_network_trainer.py
@@ -0,0 +1,181 @@
+import argparse
+import numpy as np
+import tensorflow as tf
+import tqdm
+from data_providers import EMNISTDataProvider
+from network_builder import ClassifierNetworkGraph
+from utils.parser_utils import ParserClass
+from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
+
+tf.reset_default_graph() # resets any previous graphs to clear memory
+parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
+parser_extractor = ParserClass(parser=parser) # creates a parser class to process the parsed input
+
+batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, batch_norm, \
+strided_dim_reduction, experiment_prefix, dropout_rate_value = parser_extractor.get_argument_variables()
+# returns a list of objects that contain
+# our parsed input
+
+experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_prefix,
+ batch_size, batch_norm,
+ strided_dim_reduction)
+# generate experiment name
+
+rng = np.random.RandomState(seed=seed) # set seed
+
+train_data = EMNISTDataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
+val_data = EMNISTDataProvider(which_set="valid", batch_size=batch_size, rng=rng)
+test_data = EMNISTDataProvider(which_set="test", batch_size=batch_size, rng=rng)
+# setup our data providers
+
+print("Running {}".format(experiment_name))
+print("Starting from epoch {}".format(continue_from_epoch))
+
+saved_models_filepath, logs_filepath = build_experiment_folder(experiment_name, logs_path) # generate experiment dir
+
+# Placeholder setup
+data_inputs = tf.placeholder(tf.float32, [batch_size, train_data.inputs.shape[1], train_data.inputs.shape[2],
+ train_data.inputs.shape[3]], 'data-inputs')
+data_targets = tf.placeholder(tf.int32, [batch_size], 'data-targets')
+
+training_phase = tf.placeholder(tf.bool, name='training-flag')
+rotate_data = tf.placeholder(tf.bool, name='rotate-flag')
+dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
+
+classifier_network = ClassifierNetworkGraph(input_x=data_inputs, target_placeholder=data_targets,
+ dropout_rate=dropout_rate, batch_size=batch_size,
+ n_classes=train_data.num_classes,
+ is_training=training_phase, augment_rotate_flag=rotate_data,
+ strided_dim_reduction=strided_dim_reduction,
+ use_batch_normalization=batch_norm) # initialize our computational graph
+
+if continue_from_epoch == -1: # if this is a new experiment and not continuation of a previous one then generate a new
+ # statistics file
+ save_statistics(logs_filepath, "result_summary_statistics", ["epoch", "train_c_loss", "train_c_accuracy",
+ "val_c_loss", "val_c_accuracy",
+ "test_c_loss", "test_c_accuracy"], create=True)
+
+start_epoch = continue_from_epoch if continue_from_epoch != -1 else 0 # if new experiment start from 0 otherwise
+# continue where left off
+
+summary_op, losses_ops, c_error_opt_op = classifier_network.init_train() # get graph operations (ops)
+
+total_train_batches = train_data.num_batches
+total_val_batches = val_data.num_batches
+total_test_batches = test_data.num_batches
+
+if tensorboard_enable:
+ print("saved tensorboard file at", logs_filepath)
+ writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
+
+init = tf.global_variables_initializer() # initialization op for the graph
+
+with tf.Session() as sess:
+ sess.run(init) # actually running the initialization op
+ train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
+ val_saver = tf.train.Saver()
+ best_val_accuracy = 0.
+ best_epoch = 0
+ # training or inference
+
+ if continue_from_epoch != -1:
+ train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
+ continue_from_epoch)) # restore previous graph to continue operations
+ best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
+ print(best_val_accuracy, best_epoch)
+
+ with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
+ for e in range(start_epoch, epochs):
+ total_c_loss = 0.
+ total_accuracy = 0.
+ with tqdm.tqdm(total=total_train_batches) as pbar_train:
+ for batch_idx, (x_batch, y_batch) in enumerate(train_data):
+ iter_id = e * total_train_batches + batch_idx
+ _, c_loss_value, acc = sess.run(
+ [c_error_opt_op, losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ # Here we execute the c_error_opt_op which trains the network and also the ops that compute the
+ # loss and accuracy, we save those in _, c_loss_value and acc respectively.
+ total_c_loss += c_loss_value # add loss of current iter to sum
+ total_accuracy += acc # add acc of current iter to sum
+
+ iter_out = "iter_num: {}, train_loss: {}, train_accuracy: {}".format(iter_id,
+ total_c_loss / (batch_idx + 1),
+ total_accuracy / (
+ batch_idx + 1)) # show
+ # iter statistics using running averages of previous iter within this epoch
+ pbar_train.set_description(iter_out)
+ pbar_train.update(1)
+ if tensorboard_enable and batch_idx % 25 == 0: # save tensorboard summary every 25 iterations
+ _summary = sess.run(
+ summary_op,
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ writer.add_summary(_summary, global_step=iter_id)
+
+ total_c_loss /= total_train_batches # compute mean of los
+ total_accuracy /= total_train_batches # compute mean of accuracy
+
+ save_path = train_saver.save(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ # save graph and weights
+ print("Saved current model at", save_path)
+
+ total_val_c_loss = 0.
+ total_val_accuracy = 0. # run validation stage, note how training_phase placeholder is set to False
+ # and that we do not run the c_error_opt_op which runs gradient descent, but instead only call the loss ops
+ # to collect losses on the validation set
+ with tqdm.tqdm(total=total_val_batches) as pbar_val:
+ for batch_idx, (x_batch, y_batch) in enumerate(val_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_val_c_loss += c_loss_value
+ total_val_accuracy += acc
+ iter_out = "val_loss: {}, val_accuracy: {}".format(total_val_c_loss / (batch_idx + 1),
+ total_val_accuracy / (batch_idx + 1))
+ pbar_val.set_description(iter_out)
+ pbar_val.update(1)
+
+ total_val_c_loss /= total_val_batches
+ total_val_accuracy /= total_val_batches
+
+ if best_val_accuracy < total_val_accuracy: # check if val acc better than the previous best and if
+ # so save current as best and save the model as the best validation model to be used on the test set
+ # after the final epoch
+ best_val_accuracy = total_val_accuracy
+ best_epoch = e
+ save_path = val_saver.save(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ print("Saved best validation score model at", save_path)
+
+ epoch_pbar.update(1)
+ # save statistics of this epoch, train and val without test set performance
+ save_statistics(logs_filepath, "result_summary_statistics",
+ [e, total_c_loss, total_accuracy, total_val_c_loss, total_val_accuracy,
+ -1, -1])
+
+ val_saver.restore(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, best_epoch))
+ # restore model with best performance on validation set
+ total_test_c_loss = 0.
+ total_test_accuracy = 0.
+ # computer test loss and accuracy and save
+ with tqdm.tqdm(total=total_test_batches) as pbar_test:
+ for batch_idx, (x_batch, y_batch) in enumerate(test_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_test_c_loss += c_loss_value
+ total_test_accuracy += acc
+ iter_out = "test_loss: {}, test_accuracy: {}".format(total_test_c_loss / (batch_idx + 1),
+ total_test_accuracy / (batch_idx + 1))
+ pbar_test.set_description(iter_out)
+ pbar_test.update(1)
+
+ total_test_c_loss /= total_test_batches
+ total_test_accuracy /= total_test_batches
+
+ save_statistics(logs_filepath, "result_summary_statistics",
+ ["test set performance", -1, -1, -1, -1,
+ total_test_c_loss, total_test_accuracy])
diff --git a/gpu_cluster_environment_script.sh b/gpu_cluster_environment_script.sh
new file mode 100644
index 0000000..d20d13d
--- /dev/null
+++ b/gpu_cluster_environment_script.sh
@@ -0,0 +1,21 @@
+#!/bin/sh
+#To be used before srun so that interactive sessions are run with gpu support
+export CUDA_HOME=/opt/cuda-8.0.44
+
+export CUDNN_HOME=/opt/cuDNN-6.0_8.0
+
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:$LIBRARY_PATH
+
+export CPATH=${CUDNN_HOME}/include:$CPATH
+
+export PATH=${CUDA_HOME}/bin:${PATH}
+
+export PYTHON_PATH=$PATH
+
+# Activate the relevant virtual environment:
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp
diff --git a/gpu_cluster_tutorial_training_script.sh b/gpu_cluster_tutorial_training_script.sh
new file mode 100644
index 0000000..8f6c963
--- /dev/null
+++ b/gpu_cluster_tutorial_training_script.sh
@@ -0,0 +1,33 @@
+#!/bin/sh
+#SBATCH -N 1 # nodes requested
+#SBATCH -n 1 # tasks requested
+#SBATCH --gres=gpu:1
+#SBATCH --mem=16000 # memory in Mb
+#SBATCH -o sample_experiment_outfile # send stdout to sample_experiment_outfile
+#SBATCH -e sample_experiment_errfile # send stderr to sample_experiment_errfile
+#SBATCH -t 2:00:00 # time requested in hour:minute:secon
+export CUDA_HOME=/opt/cuda-8.0.44
+
+export CUDNN_HOME=/opt/cuDNN-6.0_8.0
+
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:$LIBRARY_PATH
+
+export CPATH=${CUDNN_HOME}/include:$CPATH
+
+export PATH=${CUDA_HOME}/bin:${PATH}
+
+export PYTHON_PATH=$PATH
+
+mkdir -p /disk/scratch/${STUDENT_ID}
+
+export TMPDIR=/disk/scratch/${STUDENT_ID}/
+export TMP=/disk/scratch/${STUDENT_ID}/
+# Activate the relevant virtual environment:
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp
+
+python emnist_network_trainer.py --batch_size 128 --epochs 200 --experiment_prefix vgg-net-emnist-sample-exp --dropout_rate 0.4 --batch_norm_use True --strided_dim_reduction True --seed 25012018
diff --git a/mlp/__init__.py b/mlp/__init__.py
deleted file mode 100644
index b41e667..0000000
--- a/mlp/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Machine Learning Practical package."""
-
-__authors__ = ['Pawel Swietojanski', 'Steve Renals', 'Matt Graham']
-
-DEFAULT_SEED = 123456 # Default random number generator seed if none provided.
diff --git a/mlp/data_providers.py b/mlp/data_providers.py
deleted file mode 100644
index aac04ee..0000000
--- a/mlp/data_providers.py
+++ /dev/null
@@ -1,401 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Data providers.
-
-This module provides classes for loading datasets and iterating over batches of
-data points.
-"""
-
-import pickle
-import gzip
-import numpy as np
-import os
-from mlp import DEFAULT_SEED
-
-
-class DataProvider(object):
- """Generic data provider."""
-
- def __init__(self, inputs, targets, batch_size, max_num_batches=-1,
- shuffle_order=True, rng=None):
- """Create a new data provider object.
-
- Args:
- inputs (ndarray): Array of data input features of shape
- (num_data, input_dim).
- targets (ndarray): Array of data output targets of shape
- (num_data, output_dim) or (num_data,) if output_dim == 1.
- batch_size (int): Number of data points to include in each batch.
- max_num_batches (int): Maximum number of batches to iterate over
- in an epoch. If `max_num_batches * batch_size > num_data` then
- only as many batches as the data can be split into will be
- used. If set to -1 all of the data will be used.
- shuffle_order (bool): Whether to randomly permute the order of
- the data before each epoch.
- rng (RandomState): A seeded random number generator.
- """
- self.inputs = inputs
- self.targets = targets
- if batch_size < 1:
- raise ValueError('batch_size must be >= 1')
- self._batch_size = batch_size
- if max_num_batches == 0 or max_num_batches < -1:
- raise ValueError('max_num_batches must be -1 or > 0')
- self._max_num_batches = max_num_batches
- self._update_num_batches()
- self.shuffle_order = shuffle_order
- self._current_order = np.arange(inputs.shape[0])
- if rng is None:
- rng = np.random.RandomState(DEFAULT_SEED)
- self.rng = rng
- self.new_epoch()
-
- @property
- def batch_size(self):
- """Number of data points to include in each batch."""
- return self._batch_size
-
- @batch_size.setter
- def batch_size(self, value):
- if value < 1:
- raise ValueError('batch_size must be >= 1')
- self._batch_size = value
- self._update_num_batches()
-
- @property
- def max_num_batches(self):
- """Maximum number of batches to iterate over in an epoch."""
- return self._max_num_batches
-
- @max_num_batches.setter
- def max_num_batches(self, value):
- if value == 0 or value < -1:
- raise ValueError('max_num_batches must be -1 or > 0')
- self._max_num_batches = value
- self._update_num_batches()
-
- def _update_num_batches(self):
- """Updates number of batches to iterate over."""
- # maximum possible number of batches is equal to number of whole times
- # batch_size divides in to the number of data points which can be
- # found using integer division
- possible_num_batches = self.inputs.shape[0] // self.batch_size
- if self.max_num_batches == -1:
- self.num_batches = possible_num_batches
- else:
- self.num_batches = min(self.max_num_batches, possible_num_batches)
-
- def __iter__(self):
- """Implements Python iterator interface.
-
- This should return an object implementing a `next` method which steps
- through a sequence returning one element at a time and raising
- `StopIteration` when at the end of the sequence. Here the object
- returned is the DataProvider itself.
- """
- return self
-
- def new_epoch(self):
- """Starts a new epoch (pass through data), possibly shuffling first."""
- self._curr_batch = 0
- if self.shuffle_order:
- self.shuffle()
-
- def __next__(self):
- return self.next()
-
- def reset(self):
- """Resets the provider to the initial state."""
- inv_perm = np.argsort(self._current_order)
- self._current_order = self._current_order[inv_perm]
- self.inputs = self.inputs[inv_perm]
- self.targets = self.targets[inv_perm]
- self.new_epoch()
-
- def shuffle(self):
- """Randomly shuffles order of data."""
- perm = self.rng.permutation(self.inputs.shape[0])
- self._current_order = self._current_order[perm]
- self.inputs = self.inputs[perm]
- self.targets = self.targets[perm]
-
- def next(self):
- """Returns next data batch or raises `StopIteration` if at end."""
- if self._curr_batch + 1 > self.num_batches:
- # no more batches in current iteration through data set so start
- # new epoch ready for another pass and indicate iteration is at end
- self.new_epoch()
- raise StopIteration()
- # create an index slice corresponding to current batch number
- batch_slice = slice(self._curr_batch * self.batch_size,
- (self._curr_batch + 1) * self.batch_size)
- inputs_batch = self.inputs[batch_slice]
- targets_batch = self.targets[batch_slice]
- self._curr_batch += 1
- return inputs_batch, targets_batch
-
-class MNISTDataProvider(DataProvider):
- """Data provider for MNIST handwritten digit images."""
-
- def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
- shuffle_order=True, rng=None):
- """Create a new MNIST data provider object.
-
- Args:
- which_set: One of 'train', 'valid' or 'eval'. Determines which
- portion of the MNIST data this object should provide.
- batch_size (int): Number of data points to include in each batch.
- max_num_batches (int): Maximum number of batches to iterate over
- in an epoch. If `max_num_batches * batch_size > num_data` then
- only as many batches as the data can be split into will be
- used. If set to -1 all of the data will be used.
- shuffle_order (bool): Whether to randomly permute the order of
- the data before each epoch.
- rng (RandomState): A seeded random number generator.
- """
- # check a valid which_set was provided
- assert which_set in ['train', 'valid', 'test'], (
- 'Expected which_set to be either train, valid or eval. '
- 'Got {0}'.format(which_set)
- )
- self.which_set = which_set
- self.num_classes = 10
- # construct path to data using os.path.join to ensure the correct path
- # separator for the current platform / OS is used
- # MLP_DATA_DIR environment variable should point to the data directory
- data_path = os.path.join(
- os.environ['MLP_DATA_DIR'], 'mnist-{0}.npz'.format(which_set))
- assert os.path.isfile(data_path), (
- 'Data file does not exist at expected path: ' + data_path
- )
- # load data from compressed numpy file
- loaded = np.load(data_path)
- inputs, targets = loaded['inputs'], loaded['targets']
- inputs = inputs.astype(np.float32)
- # pass the loaded data to the parent class __init__
- super(MNISTDataProvider, self).__init__(
- inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
-
- def next(self):
- """Returns next data batch or raises `StopIteration` if at end."""
- inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
- return inputs_batch, self.to_one_of_k(targets_batch)
-
- def to_one_of_k(self, int_targets):
- """Converts integer coded class target to 1 of K coded targets.
-
- Args:
- int_targets (ndarray): Array of integer coded class targets (i.e.
- where an integer from 0 to `num_classes` - 1 is used to
- indicate which is the correct class). This should be of shape
- (num_data,).
-
- Returns:
- Array of 1 of K coded targets i.e. an array of shape
- (num_data, num_classes) where for each row all elements are equal
- to zero except for the column corresponding to the correct class
- which is equal to one.
- """
- one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
- one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
- return one_of_k_targets
-
-class EMNISTDataProvider(DataProvider):
- """Data provider for EMNIST handwritten digit images."""
-
- def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
- shuffle_order=True, rng=None):
- """Create a new EMNIST data provider object.
-
- Args:
- which_set: One of 'train', 'valid' or 'eval'. Determines which
- portion of the EMNIST data this object should provide.
- batch_size (int): Number of data points to include in each batch.
- max_num_batches (int): Maximum number of batches to iterate over
- in an epoch. If `max_num_batches * batch_size > num_data` then
- only as many batches as the data can be split into will be
- used. If set to -1 all of the data will be used.
- shuffle_order (bool): Whether to randomly permute the order of
- the data before each epoch.
- rng (RandomState): A seeded random number generator.
- """
- # check a valid which_set was provided
- assert which_set in ['train', 'valid', 'test'], (
- 'Expected which_set to be either train, valid or eval. '
- 'Got {0}'.format(which_set)
- )
- self.which_set = which_set
- self.num_classes = 47
- # construct path to data using os.path.join to ensure the correct path
- # separator for the current platform / OS is used
- # MLP_DATA_DIR environment variable should point to the data directory
- data_path = os.path.join(
- os.environ['MLP_DATA_DIR'], 'emnist-{0}.npz'.format(which_set))
- assert os.path.isfile(data_path), (
- 'Data file does not exist at expected path: ' + data_path
- )
- # load data from compressed numpy file
- loaded = np.load(data_path)
- print(loaded.keys())
- inputs, targets = loaded['inputs'], loaded['targets']
- inputs = inputs.astype(np.float32)
- inputs = np.reshape(inputs, newshape=(-1, 28*28))
- inputs = inputs / 255.0
- # pass the loaded data to the parent class __init__
- super(EMNISTDataProvider, self).__init__(
- inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
-
- def next(self):
- """Returns next data batch or raises `StopIteration` if at end."""
- inputs_batch, targets_batch = super(EMNISTDataProvider, self).next()
- return inputs_batch, self.to_one_of_k(targets_batch)
-
- def to_one_of_k(self, int_targets):
- """Converts integer coded class target to 1 of K coded targets.
-
- Args:
- int_targets (ndarray): Array of integer coded class targets (i.e.
- where an integer from 0 to `num_classes` - 1 is used to
- indicate which is the correct class). This should be of shape
- (num_data,).
-
- Returns:
- Array of 1 of K coded targets i.e. an array of shape
- (num_data, num_classes) where for each row all elements are equal
- to zero except for the column corresponding to the correct class
- which is equal to one.
- """
- one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
- one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
- return one_of_k_targets
-
-
-class MetOfficeDataProvider(DataProvider):
- """South Scotland Met Office weather data provider."""
-
- def __init__(self, window_size, batch_size=10, max_num_batches=-1,
- shuffle_order=True, rng=None):
- """Create a new Met Office data provider object.
-
- Args:
- window_size (int): Size of windows to split weather time series
- data into. The constructed input features will be the first
- `window_size - 1` entries in each window and the target outputs
- the last entry in each window.
- batch_size (int): Number of data points to include in each batch.
- max_num_batches (int): Maximum number of batches to iterate over
- in an epoch. If `max_num_batches * batch_size > num_data` then
- only as many batches as the data can be split into will be
- used. If set to -1 all of the data will be used.
- shuffle_order (bool): Whether to randomly permute the order of
- the data before each epoch.
- rng (RandomState): A seeded random number generator.
- """
- data_path = os.path.join(
- os.environ['MLP_DATA_DIR'], 'HadSSP_daily_qc.txt')
- assert os.path.isfile(data_path), (
- 'Data file does not exist at expected path: ' + data_path
- )
- raw = np.loadtxt(data_path, skiprows=3, usecols=range(2, 32))
- assert window_size > 1, 'window_size must be at least 2.'
- self.window_size = window_size
- # filter out all missing datapoints and flatten to a vector
- filtered = raw[raw >= 0].flatten()
- # normalise data to zero mean, unit standard deviation
- mean = np.mean(filtered)
- std = np.std(filtered)
- normalised = (filtered - mean) / std
- # create a view on to array corresponding to a rolling window
- shape = (normalised.shape[-1] - self.window_size + 1, self.window_size)
- strides = normalised.strides + (normalised.strides[-1],)
- windowed = np.lib.stride_tricks.as_strided(
- normalised, shape=shape, strides=strides)
- # inputs are first (window_size - 1) entries in windows
- inputs = windowed[:, :-1]
- # targets are last entry in windows
- targets = windowed[:, -1]
- super(MetOfficeDataProvider, self).__init__(
- inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
-
-class CCPPDataProvider(DataProvider):
-
- def __init__(self, which_set='train', input_dims=None, batch_size=10,
- max_num_batches=-1, shuffle_order=True, rng=None):
- """Create a new Combined Cycle Power Plant data provider object.
-
- Args:
- which_set: One of 'train' or 'valid'. Determines which portion of
- data this object should provide.
- input_dims: Which of the four input dimension to use. If `None` all
- are used. If an iterable of integers are provided (consisting
- of a subset of {0, 1, 2, 3}) then only the corresponding
- input dimensions are included.
- batch_size (int): Number of data points to include in each batch.
- max_num_batches (int): Maximum number of batches to iterate over
- in an epoch. If `max_num_batches * batch_size > num_data` then
- only as many batches as the data can be split into will be
- used. If set to -1 all of the data will be used.
- shuffle_order (bool): Whether to randomly permute the order of
- the data before each epoch.
- rng (RandomState): A seeded random number generator.
- """
- data_path = os.path.join(
- os.environ['MLP_DATA_DIR'], 'ccpp_data.npz')
- assert os.path.isfile(data_path), (
- 'Data file does not exist at expected path: ' + data_path
- )
- # check a valid which_set was provided
- assert which_set in ['train', 'valid'], (
- 'Expected which_set to be either train or valid '
- 'Got {0}'.format(which_set)
- )
- # check input_dims are valid
- if not input_dims is not None:
- input_dims = set(input_dims)
- assert input_dims.issubset({0, 1, 2, 3}), (
- 'input_dims should be a subset of {0, 1, 2, 3}'
- )
- loaded = np.load(data_path)
- inputs = loaded[which_set + '_inputs']
- if input_dims is not None:
- inputs = inputs[:, input_dims]
- targets = loaded[which_set + '_targets']
- super(CCPPDataProvider, self).__init__(
- inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
-
-
-class AugmentedMNISTDataProvider(MNISTDataProvider):
- """Data provider for MNIST dataset which randomly transforms images."""
-
- def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
- shuffle_order=True, rng=None, transformer=None):
- """Create a new augmented MNIST data provider object.
-
- Args:
- which_set: One of 'train', 'valid' or 'test'. Determines which
- portion of the MNIST data this object should provide.
- batch_size (int): Number of data points to include in each batch.
- max_num_batches (int): Maximum number of batches to iterate over
- in an epoch. If `max_num_batches * batch_size > num_data` then
- only as many batches as the data can be split into will be
- used. If set to -1 all of the data will be used.
- shuffle_order (bool): Whether to randomly permute the order of
- the data before each epoch.
- rng (RandomState): A seeded random number generator.
- transformer: Function which takes an `inputs` array of shape
- (batch_size, input_dim) corresponding to a batch of input
- images and a `rng` random number generator object (i.e. a
- call signature `transformer(inputs, rng)`) and applies a
- potentiall random set of transformations to some / all of the
- input images as each new batch is returned when iterating over
- the data provider.
- """
- super(AugmentedMNISTDataProvider, self).__init__(
- which_set, batch_size, max_num_batches, shuffle_order, rng)
- self.transformer = transformer
-
- def next(self):
- """Returns next data batch or raises `StopIteration` if at end."""
- inputs_batch, targets_batch = super(
- AugmentedMNISTDataProvider, self).next()
- transformed_inputs_batch = self.transformer(inputs_batch, self.rng)
- return transformed_inputs_batch, targets_batch
diff --git a/mlp/errors.py b/mlp/errors.py
deleted file mode 100644
index 3f0ae4f..0000000
--- a/mlp/errors.py
+++ /dev/null
@@ -1,176 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Error functions.
-
-This module defines error functions, with the aim of model training being to
-minimise the error function given a set of inputs and target outputs.
-
-The error functions will typically measure some concept of distance between the
-model outputs and target outputs, averaged over all data points in the data set
-or batch.
-"""
-
-import numpy as np
-
-
-class SumOfSquaredDiffsError(object):
- """Sum of squared differences (squared Euclidean distance) error."""
-
- def __call__(self, outputs, targets):
- """Calculates error function given a batch of outputs and targets.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Scalar cost function value.
- """
- return 0.5 * np.mean(np.sum((outputs - targets)**2, axis=1))
-
- def grad(self, outputs, targets):
- """Calculates gradient of error function with respect to outputs.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Gradient of error function with respect to outputs.
- """
- return (outputs - targets) / outputs.shape[0]
-
- def __repr__(self):
- return 'MeanSquaredErrorCost'
-
-
-class BinaryCrossEntropyError(object):
- """Binary cross entropy error."""
-
- def __call__(self, outputs, targets):
- """Calculates error function given a batch of outputs and targets.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Scalar error function value.
- """
- return -np.mean(
- targets * np.log(outputs) + (1. - targets) * np.log(1. - ouputs))
-
- def grad(self, outputs, targets):
- """Calculates gradient of error function with respect to outputs.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Gradient of error function with respect to outputs.
- """
- return ((1. - targets) / (1. - outputs) -
- (targets / outputs)) / outputs.shape[0]
-
- def __repr__(self):
- return 'BinaryCrossEntropyError'
-
-
-class BinaryCrossEntropySigmoidError(object):
- """Binary cross entropy error with logistic sigmoid applied to outputs."""
-
- def __call__(self, outputs, targets):
- """Calculates error function given a batch of outputs and targets.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Scalar error function value.
- """
- probs = 1. / (1. + np.exp(-outputs))
- return -np.mean(
- targets * np.log(probs) + (1. - targets) * np.log(1. - probs))
-
- def grad(self, outputs, targets):
- """Calculates gradient of error function with respect to outputs.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Gradient of error function with respect to outputs.
- """
- probs = 1. / (1. + np.exp(-outputs))
- return (probs - targets) / outputs.shape[0]
-
- def __repr__(self):
- return 'BinaryCrossEntropySigmoidError'
-
-
-class CrossEntropyError(object):
- """Multi-class cross entropy error."""
-
- def __call__(self, outputs, targets):
- """Calculates error function given a batch of outputs and targets.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Scalar error function value.
- """
- return -np.mean(np.sum(targets * np.log(outputs), axis=1))
-
- def grad(self, outputs, targets):
- """Calculates gradient of error function with respect to outputs.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Gradient of error function with respect to outputs.
- """
- return -(targets / outputs) / outputs.shape[0]
-
- def __repr__(self):
- return 'CrossEntropyError'
-
-
-class CrossEntropySoftmaxError(object):
- """Multi-class cross entropy error with Softmax applied to outputs."""
-
- def __call__(self, outputs, targets):
- """Calculates error function given a batch of outputs and targets.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Scalar error function value.
- """
- normOutputs = outputs - outputs.max(-1)[:, None]
- logProb = normOutputs - np.log(np.sum(np.exp(normOutputs), axis=-1)[:, None])
- return -np.mean(np.sum(targets * logProb, axis=1))
-
- def grad(self, outputs, targets):
- """Calculates gradient of error function with respect to outputs.
-
- Args:
- outputs: Array of model outputs of shape (batch_size, output_dim).
- targets: Array of target outputs of shape (batch_size, output_dim).
-
- Returns:
- Gradient of error function with respect to outputs.
- """
- probs = np.exp(outputs - outputs.max(-1)[:, None])
- probs /= probs.sum(-1)[:, None]
- return (probs - targets) / outputs.shape[0]
-
- def __repr__(self):
- return 'CrossEntropySoftmaxError'
diff --git a/mlp/initialisers.py b/mlp/initialisers.py
deleted file mode 100644
index 8c8e252..0000000
--- a/mlp/initialisers.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Parameter initialisers.
-
-This module defines classes to initialise the parameters in a layer.
-"""
-
-import numpy as np
-from mlp import DEFAULT_SEED
-
-
-class ConstantInit(object):
- """Constant parameter initialiser."""
-
- def __init__(self, value):
- """Construct a constant parameter initialiser.
-
- Args:
- value: Value to initialise parameter to.
- """
- self.value = value
-
- def __call__(self, shape):
- return np.ones(shape=shape) * self.value
-
-
-class UniformInit(object):
- """Random uniform parameter initialiser."""
-
- def __init__(self, low, high, rng=None):
- """Construct a random uniform parameter initialiser.
-
- Args:
- low: Lower bound of interval to sample from.
- high: Upper bound of interval to sample from.
- rng (RandomState): Seeded random number generator.
- """
- self.low = low
- self.high = high
- if rng is None:
- rng = np.random.RandomState(DEFAULT_SEED)
- self.rng = rng
-
- def __call__(self, shape):
- return self.rng.uniform(low=self.low, high=self.high, size=shape)
-
-
-class NormalInit(object):
- """Random normal parameter initialiser."""
-
- def __init__(self, mean, std, rng=None):
- """Construct a random uniform parameter initialiser.
-
- Args:
- mean: Mean of distribution to sample from.
- std: Standard deviation of distribution to sample from.
- rng (RandomState): Seeded random number generator.
- """
- self.mean = mean
- self.std = std
- if rng is None:
- rng = np.random.RandomState(DEFAULT_SEED)
- self.rng = rng
-
- def __call__(self, shape):
- return self.rng.normal(loc=self.mean, scale=self.std, size=shape)
-
-class GlorotUniformInit(object):
- """Glorot and Bengio (2010) random uniform weights initialiser.
-
- Initialises an two-dimensional parameter array using the 'normalized
- initialisation' scheme suggested in [1] which attempts to maintain a
- roughly constant variance in the activations and backpropagated gradients
- of a multi-layer model consisting of interleaved affine and logistic
- sigmoidal transformation layers.
-
- Weights are sampled from a zero-mean uniform distribution with standard
- deviation `sqrt(2 / (input_dim * output_dim))` where `input_dim` and
- `output_dim` are the input and output dimensions of the weight matrix
- respectively.
-
- References:
- [1]: Understanding the difficulty of training deep feedforward neural
- networks, Glorot and Bengio (2010)
- """
-
- def __init__(self, gain=1., rng=None):
- """Construct a normalised initilisation random initialiser object.
-
- Args:
- gain: Multiplicative factor to scale initialised weights by.
- Recommended values is 1 for affine layers followed by
- logistic sigmoid layers (or another affine layer).
- rng (RandomState): Seeded random number generator.
- """
- self.gain = gain
- if rng is None:
- rng = np.random.RandomState(DEFAULT_SEED)
- self.rng = rng
-
- def __call__(self, shape):
- assert len(shape) == 2, (
- 'Initialiser should only be used for two dimensional arrays.')
- std = self.gain * (2. / (shape[0] + shape[1]))**0.5
- half_width = 3.**0.5 * std
- return self.rng.uniform(low=-half_width, high=half_width, size=shape)
-
-
-class GlorotNormalInit(object):
- """Glorot and Bengio (2010) random normal weights initialiser.
-
- Initialises an two-dimensional parameter array using the 'normalized
- initialisation' scheme suggested in [1] which attempts to maintain a
- roughly constant variance in the activations and backpropagated gradients
- of a multi-layer model consisting of interleaved affine and logistic
- sigmoidal transformation layers.
-
- Weights are sampled from a zero-mean normal distribution with standard
- deviation `sqrt(2 / (input_dim * output_dim))` where `input_dim` and
- `output_dim` are the input and output dimensions of the weight matrix
- respectively.
-
- References:
- [1]: Understanding the difficulty of training deep feedforward neural
- networks, Glorot and Bengio (2010)
- """
-
- def __init__(self, gain=1., rng=None):
- """Construct a normalised initilisation random initialiser object.
-
- Args:
- gain: Multiplicative factor to scale initialised weights by.
- Recommended values is 1 for affine layers followed by
- logistic sigmoid layers (or another affine layer).
- rng (RandomState): Seeded random number generator.
- """
- self.gain = gain
- if rng is None:
- rng = np.random.RandomState(DEFAULT_SEED)
- self.rng = rng
-
- def __call__(self, shape):
- std = self.gain * (2. / (shape[0] + shape[1]))**0.5
- return self.rng.normal(loc=0., scale=std, size=shape)
diff --git a/mlp/layers.py b/mlp/layers.py
deleted file mode 100644
index 6393803..0000000
--- a/mlp/layers.py
+++ /dev/null
@@ -1,1002 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Layer definitions.
-
-This module defines classes which encapsulate a single layer.
-
-These layers map input activations to output activation with the `fprop`
-method and map gradients with repsect to outputs to gradients with respect to
-their inputs with the `bprop` method.
-
-Some layers will have learnable parameters and so will additionally define
-methods for getting and setting parameter and calculating gradients with
-respect to the layer parameters.
-"""
-
-import numpy as np
-import mlp.initialisers as init
-from mlp import DEFAULT_SEED
-
-class Layer(object):
- """Abstract class defining the interface for a layer."""
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- raise NotImplementedError()
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- raise NotImplementedError()
-
-
-class LayerWithParameters(Layer):
- """Abstract class defining the interface for a layer with parameters."""
-
- def grads_wrt_params(self, inputs, grads_wrt_outputs):
- """Calculates gradients with respect to layer parameters.
-
- Args:
- inputs: Array of inputs to layer of shape (batch_size, input_dim).
- grads_wrt_to_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- List of arrays of gradients with respect to the layer parameters
- with parameter gradients appearing in same order in tuple as
- returned from `get_params` method.
- """
- raise NotImplementedError()
-
- def params_penalty(self):
- """Returns the parameter dependent penalty term for this layer.
-
- If no parameter-dependent penalty terms are set this returns zero.
- """
- raise NotImplementedError()
-
- @property
- def params(self):
- """Returns a list of parameters of layer.
-
- Returns:
- List of current parameter values. This list should be in the
- corresponding order to the `values` argument to `set_params`.
- """
- raise NotImplementedError()
-
- @params.setter
- def params(self, values):
- """Sets layer parameters from a list of values.
-
- Args:
- values: List of values to set parameters to. This list should be
- in the corresponding order to what is returned by `get_params`.
- """
- raise NotImplementedError()
-
-class StochasticLayerWithParameters(Layer):
- """Specialised layer which uses a stochastic forward propagation."""
-
- def __init__(self, rng=None):
- """Constructs a new StochasticLayer object.
-
- Args:
- rng (RandomState): Seeded random number generator object.
- """
- if rng is None:
- rng = np.random.RandomState(DEFAULT_SEED)
- self.rng = rng
-
- def fprop(self, inputs, stochastic=True):
- """Forward propagates activations through the layer transformation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- stochastic: Flag allowing different deterministic
- forward-propagation mode in addition to default stochastic
- forward-propagation e.g. for use at test time. If False
- a deterministic forward-propagation transformation
- corresponding to the expected output of the stochastic
- forward-propagation is applied.
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- raise NotImplementedError()
- def grads_wrt_params(self, inputs, grads_wrt_outputs):
- """Calculates gradients with respect to layer parameters.
-
- Args:
- inputs: Array of inputs to layer of shape (batch_size, input_dim).
- grads_wrt_to_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- List of arrays of gradients with respect to the layer parameters
- with parameter gradients appearing in same order in tuple as
- returned from `get_params` method.
- """
- raise NotImplementedError()
-
- def params_penalty(self):
- """Returns the parameter dependent penalty term for this layer.
-
- If no parameter-dependent penalty terms are set this returns zero.
- """
- raise NotImplementedError()
-
- @property
- def params(self):
- """Returns a list of parameters of layer.
-
- Returns:
- List of current parameter values. This list should be in the
- corresponding order to the `values` argument to `set_params`.
- """
- raise NotImplementedError()
-
- @params.setter
- def params(self, values):
- """Sets layer parameters from a list of values.
-
- Args:
- values: List of values to set parameters to. This list should be
- in the corresponding order to what is returned by `get_params`.
- """
- raise NotImplementedError()
-
-class StochasticLayer(Layer):
- """Specialised layer which uses a stochastic forward propagation."""
-
- def __init__(self, rng=None):
- """Constructs a new StochasticLayer object.
-
- Args:
- rng (RandomState): Seeded random number generator object.
- """
- if rng is None:
- rng = np.random.RandomState(DEFAULT_SEED)
- self.rng = rng
-
- def fprop(self, inputs, stochastic=True):
- """Forward propagates activations through the layer transformation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- stochastic: Flag allowing different deterministic
- forward-propagation mode in addition to default stochastic
- forward-propagation e.g. for use at test time. If False
- a deterministic forward-propagation transformation
- corresponding to the expected output of the stochastic
- forward-propagation is applied.
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- raise NotImplementedError()
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs. This should correspond to
- default stochastic forward-propagation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- raise NotImplementedError()
-
-
-class AffineLayer(LayerWithParameters):
- """Layer implementing an affine tranformation of its inputs.
-
- This layer is parameterised by a weight matrix and bias vector.
- """
-
- def __init__(self, input_dim, output_dim,
- weights_initialiser=init.UniformInit(-0.1, 0.1),
- biases_initialiser=init.ConstantInit(0.),
- weights_penalty=None, biases_penalty=None):
- """Initialises a parameterised affine layer.
-
- Args:
- input_dim (int): Dimension of inputs to the layer.
- output_dim (int): Dimension of the layer outputs.
- weights_initialiser: Initialiser for the weight parameters.
- biases_initialiser: Initialiser for the bias parameters.
- weights_penalty: Weights-dependent penalty term (regulariser) or
- None if no regularisation is to be applied to the weights.
- biases_penalty: Biases-dependent penalty term (regulariser) or
- None if no regularisation is to be applied to the biases.
- """
- self.input_dim = input_dim
- self.output_dim = output_dim
- self.weights = weights_initialiser((self.output_dim, self.input_dim))
- self.biases = biases_initialiser(self.output_dim)
- self.weights_penalty = weights_penalty
- self.biases_penalty = biases_penalty
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x`, outputs `y`, weights `W` and biases `b` the layer
- corresponds to `y = W.dot(x) + b`.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- return self.weights.dot(inputs.T).T + self.biases
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- return grads_wrt_outputs.dot(self.weights)
-
- def grads_wrt_params(self, inputs, grads_wrt_outputs):
- """Calculates gradients with respect to layer parameters.
-
- Args:
- inputs: array of inputs to layer of shape (batch_size, input_dim)
- grads_wrt_to_outputs: array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim)
-
- Returns:
- list of arrays of gradients with respect to the layer parameters
- `[grads_wrt_weights, grads_wrt_biases]`.
- """
-
- grads_wrt_weights = np.dot(grads_wrt_outputs.T, inputs)
- grads_wrt_biases = np.sum(grads_wrt_outputs, axis=0)
-
- if self.weights_penalty is not None:
- grads_wrt_weights += self.weights_penalty.grad(self.weights)
-
- if self.biases_penalty is not None:
- grads_wrt_biases += self.biases_penalty.grad(self.biases)
-
- return [grads_wrt_weights, grads_wrt_biases]
-
- def params_penalty(self):
- """Returns the parameter dependent penalty term for this layer.
-
- If no parameter-dependent penalty terms are set this returns zero.
- """
- params_penalty = 0
- if self.weights_penalty is not None:
- params_penalty += self.weights_penalty(self.weights)
- if self.biases_penalty is not None:
- params_penalty += self.biases_penalty(self.biases)
- return params_penalty
-
- @property
- def params(self):
- """A list of layer parameter values: `[weights, biases]`."""
- return [self.weights, self.biases]
-
- @params.setter
- def params(self, values):
- self.weights = values[0]
- self.biases = values[1]
-
- def __repr__(self):
- return 'AffineLayer(input_dim={0}, output_dim={1})'.format(
- self.input_dim, self.output_dim)
-
-class BatchNormalizationLayer(StochasticLayerWithParameters):
- """Layer implementing an affine tranformation of its inputs.
-
- This layer is parameterised by a weight matrix and bias vector.
- """
-
- def __init__(self, input_dim, rng=None):
- """Initialises a parameterised affine layer.
-
- Args:
- input_dim (int): Dimension of inputs to the layer.
- output_dim (int): Dimension of the layer outputs.
- weights_initialiser: Initialiser for the weight parameters.
- biases_initialiser: Initialiser for the bias parameters.
- weights_penalty: Weights-dependent penalty term (regulariser) or
- None if no regularisation is to be applied to the weights.
- biases_penalty: Biases-dependent penalty term (regulariser) or
- None if no regularisation is to be applied to the biases.
- """
- super(BatchNormalizationLayer, self).__init__(rng)
- self.beta = np.random.normal(size=(input_dim))
- self.gamma = np.random.normal(size=(input_dim))
- self.epsilon = 0.00001
- self.cache = None
- self.input_dim = input_dim
-
- def fprop(self, inputs, stochastic=True):
- """Forward propagates inputs through a layer."""
-
- raise NotImplementedError
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
-
- raise NotImplementedError
-
- def grads_wrt_params(self, inputs, grads_wrt_outputs):
- """Calculates gradients with respect to layer parameters.
-
- Args:
- inputs: array of inputs to layer of shape (batch_size, input_dim)
- grads_wrt_to_outputs: array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim)
-
- Returns:
- list of arrays of gradients with respect to the layer parameters
- `[grads_wrt_weights, grads_wrt_biases]`.
- """
- raise NotImplementedError
-
- def params_penalty(self):
- """Returns the parameter dependent penalty term for this layer.
-
- If no parameter-dependent penalty terms are set this returns zero.
- """
- params_penalty = 0
-
- return params_penalty
-
- @property
- def params(self):
- """A list of layer parameter values: `[gammas, betas]`."""
- return [self.gamma, self.beta]
-
- @params.setter
- def params(self, values):
- self.gamma = values[0]
- self.beta = values[1]
-
- def __repr__(self):
- return 'BatchNormalizationLayer(input_dim={0})'.format(
- self.input_dim)
-
-
-class SigmoidLayer(Layer):
- """Layer implementing an element-wise logistic sigmoid transformation."""
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x` and outputs `y` this corresponds to
- `y = 1 / (1 + exp(-x))`.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- return 1. / (1. + np.exp(-inputs))
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- return grads_wrt_outputs * outputs * (1. - outputs)
-
- def __repr__(self):
- return 'SigmoidLayer'
-
-class ConvolutionalLayer(LayerWithParameters):
- """Layer implementing a 2D convolution-based transformation of its inputs.
- The layer is parameterised by a set of 2D convolutional kernels, a four
- dimensional array of shape
- (num_output_channels, num_input_channels, kernel_dim_1, kernel_dim_2)
- and a bias vector, a one dimensional array of shape
- (num_output_channels,)
- i.e. one shared bias per output channel.
- Assuming no-padding is applied to the inputs so that outputs are only
- calculated for positions where the kernel filters fully overlap with the
- inputs, and that unit strides are used the outputs will have spatial extent
- output_dim_1 = input_dim_1 - kernel_dim_1 + 1
- output_dim_2 = input_dim_2 - kernel_dim_2 + 1
- """
-
- def __init__(self, num_input_channels, num_output_channels,
- input_dim_1, input_dim_2,
- kernel_dim_1, kernel_dim_2,
- kernels_init=init.UniformInit(-0.01, 0.01),
- biases_init=init.ConstantInit(0.),
- kernels_penalty=None, biases_penalty=None):
- """Initialises a parameterised convolutional layer.
- Args:
- num_input_channels (int): Number of channels in inputs to
- layer (this may be number of colour channels in the input
- images if used as the first layer in a model, or the
- number of output channels, a.k.a. feature maps, from a
- a previous convolutional layer).
- num_output_channels (int): Number of channels in outputs
- from the layer, a.k.a. number of feature maps.
- input_dim_1 (int): Size of first input dimension of each 2D
- channel of inputs.
- input_dim_2 (int): Size of second input dimension of each 2D
- channel of inputs.
- kernel_dim_1 (int): Size of first dimension of each 2D channel of
- kernels.
- kernel_dim_2 (int): Size of second dimension of each 2D channel of
- kernels.
- kernels_intialiser: Initialiser for the kernel parameters.
- biases_initialiser: Initialiser for the bias parameters.
- kernels_penalty: Kernel-dependent penalty term (regulariser) or
- None if no regularisation is to be applied to the kernels.
- biases_penalty: Biases-dependent penalty term (regulariser) or
- None if no regularisation is to be applied to the biases.
- """
- self.num_input_channels = num_input_channels
- self.num_output_channels = num_output_channels
- self.input_dim_1 = input_dim_1
- self.input_dim_2 = input_dim_2
- self.kernel_dim_1 = kernel_dim_1
- self.kernel_dim_2 = kernel_dim_2
- self.kernels_init = kernels_init
- self.biases_init = biases_init
- self.kernels_shape = (
- num_output_channels, num_input_channels, kernel_dim_1, kernel_dim_2
- )
- self.inputs_shape = (
- None, num_input_channels, input_dim_1, input_dim_2
- )
- self.kernels = self.kernels_init(self.kernels_shape)
- self.biases = self.biases_init(num_output_channels)
- self.kernels_penalty = kernels_penalty
- self.biases_penalty = biases_penalty
-
- self.cache = None
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
- For inputs `x`, outputs `y`, kernels `K` and biases `b` the layer
- corresponds to `y = conv2d(x, K) + b`.
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- raise NotImplementedError
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
- Args:
- inputs: Array of layer inputs of shape
- (batch_size, num_input_channels, input_dim_1, input_dim_2).
- outputs: Array of layer outputs calculated in forward pass of
- shape
- (batch_size, num_output_channels, output_dim_1, output_dim_2).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape
- (batch_size, num_output_channels, output_dim_1, output_dim_2).
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- # Pad the grads_wrt_outputs
-
- raise NotImplementedError
-
- def grads_wrt_params(self, inputs, grads_wrt_outputs):
- """Calculates gradients with respect to layer parameters.
- Args:
- inputs: array of inputs to layer of shape (batch_size, input_dim)
- grads_wrt_to_outputs: array of gradients with respect to the layer
- outputs of shape
- (batch_size, num_output-_channels, output_dim_1, output_dim_2).
- Returns:
- list of arrays of gradients with respect to the layer parameters
- `[grads_wrt_kernels, grads_wrt_biases]`.
- """
-
- raise NotImplementedError
-
- def params_penalty(self):
- """Returns the parameter dependent penalty term for this layer.
- If no parameter-dependent penalty terms are set this returns zero.
- """
- params_penalty = 0
- if self.kernels_penalty is not None:
- params_penalty += self.kernels_penalty(self.kernels)
- if self.biases_penalty is not None:
- params_penalty += self.biases_penalty(self.biases)
- return params_penalty
-
- @property
- def params(self):
- """A list of layer parameter values: `[kernels, biases]`."""
- return [self.kernels, self.biases]
-
- @params.setter
- def params(self, values):
- self.kernels = values[0]
- self.biases = values[1]
-
- def __repr__(self):
- return (
- 'ConvolutionalLayer(\n'
- ' num_input_channels={0}, num_output_channels={1},\n'
- ' input_dim_1={2}, input_dim_2={3},\n'
- ' kernel_dim_1={4}, kernel_dim_2={5}\n'
- ')'
- .format(self.num_input_channels, self.num_output_channels,
- self.input_dim_1, self.input_dim_2, self.kernel_dim_1,
- self.kernel_dim_2)
- )
-
-
-class ReluLayer(Layer):
- """Layer implementing an element-wise rectified linear transformation."""
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- return np.maximum(inputs, 0.)
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- return (outputs > 0) * grads_wrt_outputs
-
- def __repr__(self):
- return 'ReluLayer'
-
-class LeakyReluLayer(Layer):
- """Layer implementing an element-wise rectified linear transformation."""
- def __init__(self, alpha=0.01):
- self.alpha = alpha
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.
- """
- positive_inputs = np.maximum(inputs, 0.)
-
- negative_inputs = inputs
- negative_inputs[negative_inputs>0] = 0.
- negative_inputs = negative_inputs * self.alpha
-
- outputs = positive_inputs + negative_inputs
- return outputs
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
- """
- positive_gradients = (outputs > 0) * grads_wrt_outputs
- negative_gradients = self.alpha * (outputs < 0) * grads_wrt_outputs
- gradients = positive_gradients + negative_gradients
- return gradients
-
- def __repr__(self):
- return 'LeakyReluLayer'
-
-class ELULayer(Layer):
- """Layer implementing an ELU activation."""
- def __init__(self, alpha=1.0):
- self.alpha = alpha
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.
- """
- positive_inputs = np.maximum(inputs, 0.)
-
- negative_inputs = np.copy(inputs)
- negative_inputs[negative_inputs>0] = 0.
- negative_inputs = self.alpha * (np.exp(negative_inputs) - 1)
-
- outputs = positive_inputs + negative_inputs
- return outputs
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
- """
- positive_gradients = (outputs >= 0) * grads_wrt_outputs
- outputs_to_use = (outputs < 0) * outputs
- negative_gradients = (outputs_to_use + self.alpha)
- negative_gradients[outputs >= 0] = 0.
- negative_gradients = negative_gradients * grads_wrt_outputs
- gradients = positive_gradients + negative_gradients
- return gradients
-
- def __repr__(self):
- return 'ELULayer'
-
-class SELULayer(Layer):
- """Layer implementing an element-wise rectified linear transformation."""
- #α01 ≈ 1.6733 and λ01 ≈ 1.0507
- def __init__(self):
- self.alpha = 1.6733
- self.lamda = 1.0507
- self.elu = ELULayer(alpha=self.alpha)
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.
- """
- outputs = self.lamda * self.elu.fprop(inputs)
- return outputs
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
- """
- scaled_outputs = outputs / self.lamda
- gradients = self.lamda * self.elu.bprop(inputs=inputs, outputs=scaled_outputs,
- grads_wrt_outputs=grads_wrt_outputs)
- return gradients
-
- def __repr__(self):
- return 'SELULayer'
-
-class TanhLayer(Layer):
- """Layer implementing an element-wise hyperbolic tangent transformation."""
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x` and outputs `y` this corresponds to `y = tanh(x)`.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- return np.tanh(inputs)
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- return (1. - outputs**2) * grads_wrt_outputs
-
- def __repr__(self):
- return 'TanhLayer'
-
-
-class SoftmaxLayer(Layer):
- """Layer implementing a softmax transformation."""
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- For inputs `x` and outputs `y` this corresponds to
-
- `y = exp(x) / sum(exp(x))`.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- # subtract max inside exponential to improve numerical stability -
- # when we divide through by sum this term cancels
- exp_inputs = np.exp(inputs - inputs.max(-1)[:, None])
- return exp_inputs / exp_inputs.sum(-1)[:, None]
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- return (outputs * (grads_wrt_outputs -
- (grads_wrt_outputs * outputs).sum(-1)[:, None]))
-
- def __repr__(self):
- return 'SoftmaxLayer'
-
-
-class RadialBasisFunctionLayer(Layer):
- """Layer implementing projection to a grid of radial basis functions."""
-
- def __init__(self, grid_dim, intervals=[[0., 1.]]):
- """Creates a radial basis function layer object.
-
- Args:
- grid_dim: Integer specifying how many basis function to use in
- grid across input space per dimension (so total number of
- basis functions will be grid_dim**input_dim)
- intervals: List of intervals (two element lists or tuples)
- specifying extents of axis-aligned region in input-space to
- tile basis functions in grid across. For example for a 2D input
- space spanning [0, 1] x [0, 1] use intervals=[[0, 1], [0, 1]].
- """
- num_basis = grid_dim**len(intervals)
- self.centres = np.array(np.meshgrid(*[
- np.linspace(low, high, grid_dim) for (low, high) in intervals])
- ).reshape((len(intervals), -1))
- self.scales = np.array([
- [(high - low) * 1. / grid_dim] for (low, high) in intervals])
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- return np.exp(-(inputs[..., None] - self.centres[None, ...])**2 /
- self.scales**2).reshape((inputs.shape[0], -1))
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- num_basis = self.centres.shape[1]
- return -2 * (
- ((inputs[..., None] - self.centres[None, ...]) / self.scales**2) *
- grads_wrt_outputs.reshape((inputs.shape[0], -1, num_basis))
- ).sum(-1)
-
- def __repr__(self):
- return 'RadialBasisFunctionLayer(grid_dim={0})'.format(self.grid_dim)
-
-class DropoutLayer(StochasticLayer):
- """Layer which stochastically drops input dimensions in its output."""
-
- def __init__(self, rng=None, incl_prob=0.5, share_across_batch=True):
- """Construct a new dropout layer.
-
- Args:
- rng (RandomState): Seeded random number generator.
- incl_prob: Scalar value in (0, 1] specifying the probability of
- each input dimension being included in the output.
- share_across_batch: Whether to use same dropout mask across
- all inputs in a batch or use per input masks.
- """
- super(DropoutLayer, self).__init__(rng)
- assert incl_prob > 0. and incl_prob <= 1.
- self.incl_prob = incl_prob
- self.share_across_batch = share_across_batch
- self.rng = rng
-
- def fprop(self, inputs, stochastic=True):
- """Forward propagates activations through the layer transformation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- stochastic: Flag allowing different deterministic
- forward-propagation mode in addition to default stochastic
- forward-propagation e.g. for use at test time. If False
- a deterministic forward-propagation transformation
- corresponding to the expected output of the stochastic
- forward-propagation is applied.
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- if stochastic:
- mask_shape = (1,) + inputs.shape[1:] if self.share_across_batch else inputs.shape
- self._mask = (self.rng.uniform(size=mask_shape) < self.incl_prob)
- return inputs * self._mask
- else:
- return inputs * self.incl_prob
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs. This should correspond to
- default stochastic forward-propagation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- return grads_wrt_outputs * self._mask
-
- def __repr__(self):
- return 'DropoutLayer(incl_prob={0:.1f})'.format(self.incl_prob)
-
-class ReshapeLayer(Layer):
- """Layer which reshapes dimensions of inputs."""
-
- def __init__(self, output_shape=None):
- """Create a new reshape layer object.
-
- Args:
- output_shape: Tuple specifying shape each input in batch should
- be reshaped to in outputs. This **excludes** the batch size
- so the shape of the final output array will be
- (batch_size, ) + output_shape
- Similarly to numpy.reshape, one shape dimension can be -1. In
- this case, the value is inferred from the size of the input
- array and remaining dimensions. The shape specified must be
- compatible with the input array shape - i.e. the total number
- of values in the array cannot be changed. If set to `None` the
- output shape will be set to
- (batch_size, -1)
- which will flatten all the inputs to vectors.
- """
- self.output_shape = (-1,) if output_shape is None else output_shape
-
- def fprop(self, inputs):
- """Forward propagates activations through the layer transformation.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
-
- Returns:
- outputs: Array of layer outputs of shape (batch_size, output_dim).
- """
- return inputs.reshape((inputs.shape[0],) + self.output_shape)
-
- def bprop(self, inputs, outputs, grads_wrt_outputs):
- """Back propagates gradients through a layer.
-
- Given gradients with respect to the outputs of the layer calculates the
- gradients with respect to the layer inputs.
-
- Args:
- inputs: Array of layer inputs of shape (batch_size, input_dim).
- outputs: Array of layer outputs calculated in forward pass of
- shape (batch_size, output_dim).
- grads_wrt_outputs: Array of gradients with respect to the layer
- outputs of shape (batch_size, output_dim).
-
- Returns:
- Array of gradients with respect to the layer inputs of shape
- (batch_size, input_dim).
- """
- return grads_wrt_outputs.reshape(inputs.shape)
-
- def __repr__(self):
- return 'ReshapeLayer(output_shape={0})'.format(self.output_shape)
diff --git a/mlp/learning_rules.py b/mlp/learning_rules.py
deleted file mode 100644
index 22f2bcb..0000000
--- a/mlp/learning_rules.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Learning rules.
-
-This module contains classes implementing gradient based learning rules.
-"""
-
-import numpy as np
-
-
-class GradientDescentLearningRule(object):
- """Simple (stochastic) gradient descent learning rule.
-
- For a scalar error function `E(p[0], p_[1] ... )` of some set of
- potentially multidimensional parameters this attempts to find a local
- minimum of the loss function by applying updates to each parameter of the
- form
-
- p[i] := p[i] - learning_rate * dE/dp[i]
-
- With `learning_rate` a positive scaling parameter.
-
- The error function used in successive applications of these updates may be
- a stochastic estimator of the true error function (e.g. when the error with
- respect to only a subset of data-points is calculated) in which case this
- will correspond to a stochastic gradient descent learning rule.
- """
-
- def __init__(self, learning_rate=1e-3):
- """Creates a new learning rule object.
-
- Args:
- learning_rate: A postive scalar to scale gradient updates to the
- parameters by. This needs to be carefully set - if too large
- the learning dynamic will be unstable and may diverge, while
- if set too small learning will proceed very slowly.
-
- """
- assert learning_rate > 0., 'learning_rate should be positive.'
- self.learning_rate = learning_rate
-
- def initialise(self, params):
- """Initialises the state of the learning rule for a set or parameters.
-
- This must be called before `update_params` is first called.
-
- Args:
- params: A list of the parameters to be optimised. Note these will
- be updated *in-place* to avoid reallocating arrays on each
- update.
- """
- self.params = params
-
- def reset(self):
- """Resets any additional state variables to their intial values.
-
- For this learning rule there are no additional state variables so we
- do nothing here.
- """
- pass
-
- def update_params(self, grads_wrt_params):
- """Applies a single gradient descent update to all parameters.
-
- All parameter updates are performed using in-place operations and so
- nothing is returned.
-
- Args:
- grads_wrt_params: A list of gradients of the scalar loss function
- with respect to each of the parameters passed to `initialise`
- previously, with this list expected to be in the same order.
- """
- for param, grad in zip(self.params, grads_wrt_params):
- param -= self.learning_rate * grad
-
-
-class MomentumLearningRule(GradientDescentLearningRule):
- """Gradient descent with momentum learning rule.
-
- This extends the basic gradient learning rule by introducing extra
- momentum state variables for each parameter. These can help the learning
- dynamic help overcome shallow local minima and speed convergence when
- making multiple successive steps in a similar direction in parameter space.
-
- For parameter p[i] and corresponding momentum m[i] the updates for a
- scalar loss function `L` are of the form
-
- m[i] := mom_coeff * m[i] - learning_rate * dL/dp[i]
- p[i] := p[i] + m[i]
-
- with `learning_rate` a positive scaling parameter for the gradient updates
- and `mom_coeff` a value in [0, 1] that determines how much 'friction' there
- is the system and so how quickly previous momentum contributions decay.
- """
-
- def __init__(self, learning_rate=1e-3, mom_coeff=0.9):
- """Creates a new learning rule object.
-
- Args:
- learning_rate: A postive scalar to scale gradient updates to the
- parameters by. This needs to be carefully set - if too large
- the learning dynamic will be unstable and may diverge, while
- if set too small learning will proceed very slowly.
- mom_coeff: A scalar in the range [0, 1] inclusive. This determines
- the contribution of the previous momentum value to the value
- after each update. If equal to 0 the momentum is set to exactly
- the negative scaled gradient each update and so this rule
- collapses to standard gradient descent. If equal to 1 the
- momentum will just be decremented by the scaled gradient at
- each update. This is equivalent to simulating the dynamic in
- a frictionless system. Due to energy conservation the loss
- of 'potential energy' as the dynamics moves down the loss
- function surface will lead to an increasingly large 'kinetic
- energy' and so speed, meaning the updates will become
- increasingly large, potentially unstably so. Typically a value
- less than but close to 1 will avoid these issues and cause the
- dynamic to converge to a local minima where the gradients are
- by definition zero.
- """
- super(MomentumLearningRule, self).__init__(learning_rate)
- assert mom_coeff >= 0. and mom_coeff <= 1., (
- 'mom_coeff should be in the range [0, 1].'
- )
- self.mom_coeff = mom_coeff
-
- def initialise(self, params):
- """Initialises the state of the learning rule for a set or parameters.
-
- This must be called before `update_params` is first called.
-
- Args:
- params: A list of the parameters to be optimised. Note these will
- be updated *in-place* to avoid reallocating arrays on each
- update.
- """
- super(MomentumLearningRule, self).initialise(params)
- self.moms = []
- for param in self.params:
- self.moms.append(np.zeros_like(param))
-
- def reset(self):
- """Resets any additional state variables to their intial values.
-
- For this learning rule this corresponds to zeroing all the momenta.
- """
- for mom in zip(self.moms):
- mom *= 0.
-
- def update_params(self, grads_wrt_params):
- """Applies a single update to all parameters.
-
- All parameter updates are performed using in-place operations and so
- nothing is returned.
-
- Args:
- grads_wrt_params: A list of gradients of the scalar loss function
- with respect to each of the parameters passed to `initialise`
- previously, with this list expected to be in the same order.
- """
- for param, mom, grad in zip(self.params, self.moms, grads_wrt_params):
- mom *= self.mom_coeff
- mom -= self.learning_rate * grad
- param += mom
diff --git a/mlp/models.py b/mlp/models.py
deleted file mode 100644
index 7f1273e..0000000
--- a/mlp/models.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Model definitions.
-
-This module implements objects encapsulating learnable models of input-output
-relationships. The model objects implement methods for forward propagating
-the inputs through the transformation(s) defined by the model to produce
-outputs (and intermediate states) and for calculating gradients of scalar
-functions of the outputs with respect to the model parameters.
-"""
-
-from mlp.layers import LayerWithParameters, StochasticLayer, StochasticLayerWithParameters
-
-
-class SingleLayerModel(object):
- """A model consisting of a single transformation layer."""
-
- def __init__(self, layer):
- """Create a new single layer model instance.
-
- Args:
- layer: The layer object defining the model architecture.
- """
- self.layer = layer
-
- @property
- def params(self):
- """A list of all of the parameters of the model."""
- return self.layer.params
-
- def fprop(self, inputs):
- """Calculate the model outputs corresponding to a batch of inputs.
-
- Args:
- inputs: Batch of inputs to the model.
-
- Returns:
- List which is a concatenation of the model inputs and model
- outputs, this being done for consistency of the interface with
- multi-layer models for which `fprop` returns a list of
- activations through all immediate layers of the model and including
- the inputs and outputs.
- """
- activations = [inputs, self.layer.fprop(inputs)]
- return activations
-
- def grads_wrt_params(self, activations, grads_wrt_outputs):
- """Calculates gradients with respect to the model parameters.
-
- Args:
- activations: List of all activations from forward pass through
- model using `fprop`.
- grads_wrt_outputs: Gradient with respect to the model outputs of
- the scalar function parameter gradients are being calculated
- for.
-
- Returns:
- List of gradients of the scalar function with respect to all model
- parameters.
- """
- return self.layer.grads_wrt_params(activations[0], grads_wrt_outputs)
-
- def __repr__(self):
- return 'SingleLayerModel(' + str(self.layer) + ')'
-
-
-class MultipleLayerModel(object):
- """A model consisting of multiple layers applied sequentially."""
-
- def __init__(self, layers):
- """Create a new multiple layer model instance.
-
- Args:
- layers: List of the the layer objecst defining the model in the
- order they should be applied from inputs to outputs.
- """
- self.layers = layers
-
- @property
- def params(self):
- """A list of all of the parameters of the model."""
- params = []
- for layer in self.layers:
- if isinstance(layer, LayerWithParameters) or isinstance(layer, StochasticLayerWithParameters):
- params += layer.params
- return params
-
- def fprop(self, inputs, evaluation=False):
- """Forward propagates a batch of inputs through the model.
-
- Args:
- inputs: Batch of inputs to the model.
-
- Returns:
- List of the activations at the output of all layers of the model
- plus the inputs (to the first layer) as the first element. The
- last element of the list corresponds to the model outputs.
- """
- activations = [inputs]
- for i, layer in enumerate(self.layers):
- if evaluation:
- if issubclass(type(self.layers[i]), StochasticLayer) or issubclass(type(self.layers[i]),
- StochasticLayerWithParameters):
- current_activations = self.layers[i].fprop(activations[i], stochastic=False)
- else:
- current_activations = self.layers[i].fprop(activations[i])
- else:
- if issubclass(type(self.layers[i]), StochasticLayer) or issubclass(type(self.layers[i]),
- StochasticLayerWithParameters):
- current_activations = self.layers[i].fprop(activations[i], stochastic=True)
- else:
- current_activations = self.layers[i].fprop(activations[i])
- activations.append(current_activations)
- return activations
-
- def grads_wrt_params(self, activations, grads_wrt_outputs):
- """Calculates gradients with respect to the model parameters.
-
- Args:
- activations: List of all activations from forward pass through
- model using `fprop`.
- grads_wrt_outputs: Gradient with respect to the model outputs of
- the scalar function parameter gradients are being calculated
- for.
-
- Returns:
- List of gradients of the scalar function with respect to all model
- parameters.
- """
- grads_wrt_params = []
- for i, layer in enumerate(self.layers[::-1]):
- inputs = activations[-i - 2]
- outputs = activations[-i - 1]
- grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs)
- if isinstance(layer, LayerWithParameters) or isinstance(layer, StochasticLayerWithParameters):
- grads_wrt_params += layer.grads_wrt_params(
- inputs, grads_wrt_outputs)[::-1]
- grads_wrt_outputs = grads_wrt_inputs
- return grads_wrt_params[::-1]
-
- def __repr__(self):
- return (
- 'MultiLayerModel(\n ' +
- '\n '.join([str(layer) for layer in self.layers]) +
- '\n)'
- )
diff --git a/mlp/optimisers.py b/mlp/optimisers.py
deleted file mode 100644
index 8ab313a..0000000
--- a/mlp/optimisers.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Model optimisers.
-
-This module contains objects implementing (batched) stochastic gradient descent
-based optimisation of models.
-"""
-
-import time
-import logging
-from collections import OrderedDict
-import numpy as np
-import tqdm
-
-logger = logging.getLogger(__name__)
-
-
-class Optimiser(object):
- """Basic model optimiser."""
-
- def __init__(self, model, error, learning_rule, train_dataset,
- valid_dataset=None, data_monitors=None, notebook=False):
- """Create a new optimiser instance.
-
- Args:
- model: The model to optimise.
- error: The scalar error function to minimise.
- learning_rule: Gradient based learning rule to use to minimise
- error.
- train_dataset: Data provider for training set data batches.
- valid_dataset: Data provider for validation set data batches.
- data_monitors: Dictionary of functions evaluated on targets and
- model outputs (averaged across both full training and
- validation data sets) to monitor during training in addition
- to the error. Keys should correspond to a string label for
- the statistic being evaluated.
- """
- self.model = model
- self.error = error
- self.learning_rule = learning_rule
- self.learning_rule.initialise(self.model.params)
- self.train_dataset = train_dataset
- self.valid_dataset = valid_dataset
- self.data_monitors = OrderedDict([('error', error)])
- if data_monitors is not None:
- self.data_monitors.update(data_monitors)
- self.notebook = notebook
- if notebook:
- self.tqdm_progress = tqdm.tqdm_notebook
- else:
- self.tqdm_progress = tqdm.tqdm
-
- def do_training_epoch(self):
- """Do a single training epoch.
-
- This iterates through all batches in training dataset, for each
- calculating the gradient of the estimated error given the batch with
- respect to all the model parameters and then updates the model
- parameters according to the learning rule.
- """
- with self.tqdm_progress(total=self.train_dataset.num_batches) as train_progress_bar:
- train_progress_bar.set_description("Epoch Progress")
- for inputs_batch, targets_batch in self.train_dataset:
- activations = self.model.fprop(inputs_batch)
- grads_wrt_outputs = self.error.grad(activations[-1], targets_batch)
- grads_wrt_params = self.model.grads_wrt_params(
- activations, grads_wrt_outputs)
- self.learning_rule.update_params(grads_wrt_params)
- train_progress_bar.update(1)
-
- def eval_monitors(self, dataset, label):
- """Evaluates the monitors for the given dataset.
-
- Args:
- dataset: Dataset to perform evaluation with.
- label: Tag to add to end of monitor keys to identify dataset.
-
- Returns:
- OrderedDict of monitor values evaluated on dataset.
- """
- data_mon_vals = OrderedDict([(key + label, 0.) for key
- in self.data_monitors.keys()])
- for inputs_batch, targets_batch in dataset:
- activations = self.model.fprop(inputs_batch, evaluation=True)
- for key, data_monitor in self.data_monitors.items():
- data_mon_vals[key + label] += data_monitor(
- activations[-1], targets_batch)
- for key, data_monitor in self.data_monitors.items():
- data_mon_vals[key + label] /= dataset.num_batches
- return data_mon_vals
-
- def get_epoch_stats(self):
- """Computes training statistics for an epoch.
-
- Returns:
- An OrderedDict with keys corresponding to the statistic labels and
- values corresponding to the value of the statistic.
- """
- epoch_stats = OrderedDict()
- epoch_stats.update(self.eval_monitors(self.train_dataset, '(train)'))
- if self.valid_dataset is not None:
- epoch_stats.update(self.eval_monitors(
- self.valid_dataset, '(valid)'))
- return epoch_stats
-
- def log_stats(self, epoch, epoch_time, stats):
- """Outputs stats for a training epoch to a logger.
-
- Args:
- epoch (int): Epoch counter.
- epoch_time: Time taken in seconds for the epoch to complete.
- stats: Monitored stats for the epoch.
- """
- logger.info('Epoch {0}: {1:.1f}s to complete\n {2}'.format(
- epoch, epoch_time,
- ', '.join(['{0}={1:.2e}'.format(k, v) for (k, v) in stats.items()])
- ))
-
- def train(self, num_epochs, stats_interval=5):
- """Trains a model for a set number of epochs.
-
- Args:
- num_epochs: Number of epochs (complete passes through trainin
- dataset) to train for.
- stats_interval: Training statistics will be recorded and logged
- every `stats_interval` epochs.
-
- Returns:
- Tuple with first value being an array of training run statistics
- and the second being a dict mapping the labels for the statistics
- recorded to their column index in the array.
- """
- start_train_time = time.time()
- run_stats = [list(self.get_epoch_stats().values())]
- with self.tqdm_progress(total=num_epochs) as progress_bar:
- progress_bar.set_description("Experiment Progress")
- for epoch in range(1, num_epochs + 1):
- start_time = time.time()
- self.do_training_epoch()
- epoch_time = time.time()- start_time
- if epoch % stats_interval == 0:
- stats = self.get_epoch_stats()
- self.log_stats(epoch, epoch_time, stats)
- run_stats.append(list(stats.values()))
- progress_bar.update(1)
- finish_train_time = time.time()
- total_train_time = finish_train_time - start_train_time
- return np.array(run_stats), {k: i for i, k in enumerate(stats.keys())}, total_train_time
-
diff --git a/mlp/schedulers.py b/mlp/schedulers.py
deleted file mode 100644
index 4f53e7e..0000000
--- a/mlp/schedulers.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Training schedulers.
-
-This module contains classes implementing schedulers which control the
-evolution of learning rule hyperparameters (such as learning rate) over a
-training run.
-"""
-
-import numpy as np
-
-
-class ConstantLearningRateScheduler(object):
- """Example of scheduler interface which sets a constant learning rate."""
-
- def __init__(self, learning_rate):
- """Construct a new constant learning rate scheduler object.
-
- Args:
- learning_rate: Learning rate to use in learning rule.
- """
- self.learning_rate = learning_rate
-
- def update_learning_rule(self, learning_rule, epoch_number):
- """Update the hyperparameters of the learning rule.
-
- Run at the beginning of each epoch.
-
- Args:
- learning_rule: Learning rule object being used in training run,
- any scheduled hyperparameters to be altered should be
- attributes of this object.
- epoch_number: Integer index of training epoch about to be run.
- """
- learning_rule.learning_rate = self.learning_rate
diff --git a/msd10_network_trainer.py b/msd10_network_trainer.py
new file mode 100644
index 0000000..3e43939
--- /dev/null
+++ b/msd10_network_trainer.py
@@ -0,0 +1,181 @@
+import argparse
+import numpy as np
+import tensorflow as tf
+import tqdm
+from data_providers import MSD10GenreDataProvider
+from network_builder import ClassifierNetworkGraph
+from utils.parser_utils import ParserClass
+from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
+
+tf.reset_default_graph() # resets any previous graphs to clear memory
+parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
+parser_extractor = ParserClass(parser=parser) # creates a parser class to process the parsed input
+
+batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, batch_norm, \
+strided_dim_reduction, experiment_prefix, dropout_rate_value = parser_extractor.get_argument_variables()
+# returns a list of objects that contain
+# our parsed input
+
+experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_prefix,
+ batch_size, batch_norm,
+ strided_dim_reduction)
+# generate experiment name
+
+rng = np.random.RandomState(seed=seed) # set seed
+
+train_data = MSD10GenreDataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
+val_data = MSD10GenreDataProvider(which_set="valid", batch_size=batch_size, rng=rng)
+test_data = MSD10GenreDataProvider(which_set="test", batch_size=batch_size, rng=rng)
+# setup our data providers
+
+print("Running {}".format(experiment_name))
+print("Starting from epoch {}".format(continue_from_epoch))
+
+saved_models_filepath, logs_filepath = build_experiment_folder(experiment_name, logs_path) # generate experiment dir
+
+# Placeholder setup
+data_inputs = tf.placeholder(tf.float32, [batch_size, train_data.inputs.shape[1]], 'data-inputs')
+data_targets = tf.placeholder(tf.int32, [batch_size], 'data-targets')
+
+training_phase = tf.placeholder(tf.bool, name='training-flag')
+rotate_data = tf.placeholder(tf.bool, name='rotate-flag')
+dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
+
+classifier_network = ClassifierNetworkGraph(network_name='FCCClassifier',
+ input_x=data_inputs, target_placeholder=data_targets,
+ dropout_rate=dropout_rate, batch_size=batch_size,
+ n_classes=train_data.num_classes,
+ is_training=training_phase, augment_rotate_flag=rotate_data,
+ strided_dim_reduction=strided_dim_reduction,
+ use_batch_normalization=batch_norm) # initialize our computational graph
+
+if continue_from_epoch == -1: # if this is a new experiment and not continuation of a previous one then generate a new
+ # statistics file
+ save_statistics(logs_filepath, "result_summary_statistics", ["epoch", "train_c_loss", "train_c_accuracy",
+ "val_c_loss", "val_c_accuracy",
+ "test_c_loss", "test_c_accuracy"], create=True)
+
+start_epoch = continue_from_epoch if continue_from_epoch != -1 else 0 # if new experiment start from 0 otherwise
+# continue where left off
+
+summary_op, losses_ops, c_error_opt_op = classifier_network.init_train() # get graph operations (ops)
+
+total_train_batches = train_data.num_batches
+total_val_batches = val_data.num_batches
+total_test_batches = test_data.num_batches
+
+if tensorboard_enable:
+ print("saved tensorboard file at", logs_filepath)
+ writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
+
+init = tf.global_variables_initializer() # initialization op for the graph
+
+with tf.Session() as sess:
+ sess.run(init) # actually running the initialization op
+ train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
+ val_saver = tf.train.Saver()
+ best_val_accuracy = 0.
+ best_epoch = 0
+ # training or inference
+
+ if continue_from_epoch != -1:
+ train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
+ continue_from_epoch)) # restore previous graph to continue operations
+ best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
+ print(best_val_accuracy, best_epoch)
+
+ with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
+ for e in range(start_epoch, epochs):
+ total_c_loss = 0.
+ total_accuracy = 0.
+ with tqdm.tqdm(total=total_train_batches) as pbar_train:
+ for batch_idx, (x_batch, y_batch) in enumerate(train_data):
+ iter_id = e * total_train_batches + batch_idx
+ _, c_loss_value, acc = sess.run(
+ [c_error_opt_op, losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ # Here we execute the c_error_opt_op which trains the network and also the ops that compute the
+ # loss and accuracy, we save those in _, c_loss_value and acc respectively.
+ total_c_loss += c_loss_value # add loss of current iter to sum
+ total_accuracy += acc # add acc of current iter to sum
+
+ iter_out = "iter_num: {}, train_loss: {}, train_accuracy: {}".format(iter_id,
+ total_c_loss / (batch_idx + 1),
+ total_accuracy / (
+ batch_idx + 1)) # show
+ # iter statistics using running averages of previous iter within this epoch
+ pbar_train.set_description(iter_out)
+ pbar_train.update(1)
+ if tensorboard_enable and batch_idx % 25 == 0: # save tensorboard summary every 25 iterations
+ _summary = sess.run(
+ summary_op,
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ writer.add_summary(_summary, global_step=iter_id)
+
+ total_c_loss /= total_train_batches # compute mean of los
+ total_accuracy /= total_train_batches # compute mean of accuracy
+
+ save_path = train_saver.save(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ # save graph and weights
+ print("Saved current model at", save_path)
+
+ total_val_c_loss = 0.
+ total_val_accuracy = 0. # run validation stage, note how training_phase placeholder is set to False
+ # and that we do not run the c_error_opt_op which runs gradient descent, but instead only call the loss ops
+ # to collect losses on the validation set
+ with tqdm.tqdm(total=total_val_batches) as pbar_val:
+ for batch_idx, (x_batch, y_batch) in enumerate(val_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_val_c_loss += c_loss_value
+ total_val_accuracy += acc
+ iter_out = "val_loss: {}, val_accuracy: {}".format(total_val_c_loss / (batch_idx + 1),
+ total_val_accuracy / (batch_idx + 1))
+ pbar_val.set_description(iter_out)
+ pbar_val.update(1)
+
+ total_val_c_loss /= total_val_batches
+ total_val_accuracy /= total_val_batches
+
+ if best_val_accuracy < total_val_accuracy: # check if val acc better than the previous best and if
+ # so save current as best and save the model as the best validation model to be used on the test set
+ # after the final epoch
+ best_val_accuracy = total_val_accuracy
+ best_epoch = e
+ save_path = val_saver.save(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ print("Saved best validation score model at", save_path)
+
+ epoch_pbar.update(1)
+ # save statistics of this epoch, train and val without test set performance
+ save_statistics(logs_filepath, "result_summary_statistics",
+ [e, total_c_loss, total_accuracy, total_val_c_loss, total_val_accuracy,
+ -1, -1])
+
+ val_saver.restore(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, best_epoch))
+ # restore model with best performance on validation set
+ total_test_c_loss = 0.
+ total_test_accuracy = 0.
+ # computer test loss and accuracy and save
+ with tqdm.tqdm(total=total_test_batches) as pbar_test:
+ for batch_idx, (x_batch, y_batch) in enumerate(test_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_test_c_loss += c_loss_value
+ total_test_accuracy += acc
+ iter_out = "test_loss: {}, test_accuracy: {}".format(total_test_c_loss / (batch_idx + 1),
+ total_test_accuracy / (batch_idx + 1))
+ pbar_test.set_description(iter_out)
+ pbar_test.update(1)
+
+ total_test_c_loss /= total_test_batches
+ total_test_accuracy /= total_test_batches
+
+ save_statistics(logs_filepath, "result_summary_statistics",
+ ["test set performance", -1, -1, -1, -1,
+ total_test_c_loss, total_test_accuracy])
diff --git a/msd25_network_trainer.py b/msd25_network_trainer.py
new file mode 100644
index 0000000..3e43939
--- /dev/null
+++ b/msd25_network_trainer.py
@@ -0,0 +1,181 @@
+import argparse
+import numpy as np
+import tensorflow as tf
+import tqdm
+from data_providers import MSD10GenreDataProvider
+from network_builder import ClassifierNetworkGraph
+from utils.parser_utils import ParserClass
+from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
+
+tf.reset_default_graph() # resets any previous graphs to clear memory
+parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
+parser_extractor = ParserClass(parser=parser) # creates a parser class to process the parsed input
+
+batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, batch_norm, \
+strided_dim_reduction, experiment_prefix, dropout_rate_value = parser_extractor.get_argument_variables()
+# returns a list of objects that contain
+# our parsed input
+
+experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_prefix,
+ batch_size, batch_norm,
+ strided_dim_reduction)
+# generate experiment name
+
+rng = np.random.RandomState(seed=seed) # set seed
+
+train_data = MSD10GenreDataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
+val_data = MSD10GenreDataProvider(which_set="valid", batch_size=batch_size, rng=rng)
+test_data = MSD10GenreDataProvider(which_set="test", batch_size=batch_size, rng=rng)
+# setup our data providers
+
+print("Running {}".format(experiment_name))
+print("Starting from epoch {}".format(continue_from_epoch))
+
+saved_models_filepath, logs_filepath = build_experiment_folder(experiment_name, logs_path) # generate experiment dir
+
+# Placeholder setup
+data_inputs = tf.placeholder(tf.float32, [batch_size, train_data.inputs.shape[1]], 'data-inputs')
+data_targets = tf.placeholder(tf.int32, [batch_size], 'data-targets')
+
+training_phase = tf.placeholder(tf.bool, name='training-flag')
+rotate_data = tf.placeholder(tf.bool, name='rotate-flag')
+dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
+
+classifier_network = ClassifierNetworkGraph(network_name='FCCClassifier',
+ input_x=data_inputs, target_placeholder=data_targets,
+ dropout_rate=dropout_rate, batch_size=batch_size,
+ n_classes=train_data.num_classes,
+ is_training=training_phase, augment_rotate_flag=rotate_data,
+ strided_dim_reduction=strided_dim_reduction,
+ use_batch_normalization=batch_norm) # initialize our computational graph
+
+if continue_from_epoch == -1: # if this is a new experiment and not continuation of a previous one then generate a new
+ # statistics file
+ save_statistics(logs_filepath, "result_summary_statistics", ["epoch", "train_c_loss", "train_c_accuracy",
+ "val_c_loss", "val_c_accuracy",
+ "test_c_loss", "test_c_accuracy"], create=True)
+
+start_epoch = continue_from_epoch if continue_from_epoch != -1 else 0 # if new experiment start from 0 otherwise
+# continue where left off
+
+summary_op, losses_ops, c_error_opt_op = classifier_network.init_train() # get graph operations (ops)
+
+total_train_batches = train_data.num_batches
+total_val_batches = val_data.num_batches
+total_test_batches = test_data.num_batches
+
+if tensorboard_enable:
+ print("saved tensorboard file at", logs_filepath)
+ writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
+
+init = tf.global_variables_initializer() # initialization op for the graph
+
+with tf.Session() as sess:
+ sess.run(init) # actually running the initialization op
+ train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
+ val_saver = tf.train.Saver()
+ best_val_accuracy = 0.
+ best_epoch = 0
+ # training or inference
+
+ if continue_from_epoch != -1:
+ train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
+ continue_from_epoch)) # restore previous graph to continue operations
+ best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
+ print(best_val_accuracy, best_epoch)
+
+ with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
+ for e in range(start_epoch, epochs):
+ total_c_loss = 0.
+ total_accuracy = 0.
+ with tqdm.tqdm(total=total_train_batches) as pbar_train:
+ for batch_idx, (x_batch, y_batch) in enumerate(train_data):
+ iter_id = e * total_train_batches + batch_idx
+ _, c_loss_value, acc = sess.run(
+ [c_error_opt_op, losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ # Here we execute the c_error_opt_op which trains the network and also the ops that compute the
+ # loss and accuracy, we save those in _, c_loss_value and acc respectively.
+ total_c_loss += c_loss_value # add loss of current iter to sum
+ total_accuracy += acc # add acc of current iter to sum
+
+ iter_out = "iter_num: {}, train_loss: {}, train_accuracy: {}".format(iter_id,
+ total_c_loss / (batch_idx + 1),
+ total_accuracy / (
+ batch_idx + 1)) # show
+ # iter statistics using running averages of previous iter within this epoch
+ pbar_train.set_description(iter_out)
+ pbar_train.update(1)
+ if tensorboard_enable and batch_idx % 25 == 0: # save tensorboard summary every 25 iterations
+ _summary = sess.run(
+ summary_op,
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: True, rotate_data: False})
+ writer.add_summary(_summary, global_step=iter_id)
+
+ total_c_loss /= total_train_batches # compute mean of los
+ total_accuracy /= total_train_batches # compute mean of accuracy
+
+ save_path = train_saver.save(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ # save graph and weights
+ print("Saved current model at", save_path)
+
+ total_val_c_loss = 0.
+ total_val_accuracy = 0. # run validation stage, note how training_phase placeholder is set to False
+ # and that we do not run the c_error_opt_op which runs gradient descent, but instead only call the loss ops
+ # to collect losses on the validation set
+ with tqdm.tqdm(total=total_val_batches) as pbar_val:
+ for batch_idx, (x_batch, y_batch) in enumerate(val_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_val_c_loss += c_loss_value
+ total_val_accuracy += acc
+ iter_out = "val_loss: {}, val_accuracy: {}".format(total_val_c_loss / (batch_idx + 1),
+ total_val_accuracy / (batch_idx + 1))
+ pbar_val.set_description(iter_out)
+ pbar_val.update(1)
+
+ total_val_c_loss /= total_val_batches
+ total_val_accuracy /= total_val_batches
+
+ if best_val_accuracy < total_val_accuracy: # check if val acc better than the previous best and if
+ # so save current as best and save the model as the best validation model to be used on the test set
+ # after the final epoch
+ best_val_accuracy = total_val_accuracy
+ best_epoch = e
+ save_path = val_saver.save(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, e))
+ print("Saved best validation score model at", save_path)
+
+ epoch_pbar.update(1)
+ # save statistics of this epoch, train and val without test set performance
+ save_statistics(logs_filepath, "result_summary_statistics",
+ [e, total_c_loss, total_accuracy, total_val_c_loss, total_val_accuracy,
+ -1, -1])
+
+ val_saver.restore(sess, "{}/best_validation_{}_{}.ckpt".format(saved_models_filepath, experiment_name, best_epoch))
+ # restore model with best performance on validation set
+ total_test_c_loss = 0.
+ total_test_accuracy = 0.
+ # computer test loss and accuracy and save
+ with tqdm.tqdm(total=total_test_batches) as pbar_test:
+ for batch_idx, (x_batch, y_batch) in enumerate(test_data):
+ c_loss_value, acc = sess.run(
+ [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
+ feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
+ data_targets: y_batch, training_phase: False, rotate_data: False})
+ total_test_c_loss += c_loss_value
+ total_test_accuracy += acc
+ iter_out = "test_loss: {}, test_accuracy: {}".format(total_test_c_loss / (batch_idx + 1),
+ total_test_accuracy / (batch_idx + 1))
+ pbar_test.set_description(iter_out)
+ pbar_test.update(1)
+
+ total_test_c_loss /= total_test_batches
+ total_test_accuracy /= total_test_batches
+
+ save_statistics(logs_filepath, "result_summary_statistics",
+ ["test set performance", -1, -1, -1, -1,
+ total_test_c_loss, total_test_accuracy])
diff --git a/network_architectures.py b/network_architectures.py
new file mode 100644
index 0000000..aaa29dd
--- /dev/null
+++ b/network_architectures.py
@@ -0,0 +1,146 @@
+import tensorflow as tf
+from tensorflow.contrib.layers import batch_norm
+from tensorflow.python.ops.nn_ops import leaky_relu
+
+from utils.network_summary import count_parameters
+
+
+class VGGClassifier:
+ def __init__(self, batch_size, layer_stage_sizes, name, num_classes, batch_norm_use=False,
+ inner_layer_depth=2, strided_dim_reduction=True):
+
+ """
+ Initializes a VGG Classifier architecture
+ :param batch_size: The size of the data batch
+ :param layer_stage_sizes: A list containing the filters for each layer stage, where layer stage is a series of
+ convolutional layers with stride=1 and no max pooling followed by a dimensionality reducing stage which is
+ either a convolution with stride=1 followed by max pooling or a convolution with stride=2
+ (i.e. strided convolution). So if we pass a list [64, 128, 256] it means that if we have inner_layer_depth=2
+ then stage 0 will have 2 layers with stride=1 and filter size=64 and another dimensionality reducing convolution
+ with either stride=1 and max pooling or stride=2 to dimensionality reduce. Similarly for the other stages.
+ :param name: Name of the network
+ :param num_classes: Number of classes we will need to classify
+ :param num_channels: Number of channels of our image data.
+ :param batch_norm_use: Whether to use batch norm between layers or not.
+ :param inner_layer_depth: The amount of extra layers on top of the dimensionality reducing stage to have per
+ layer stage.
+ :param strided_dim_reduction: Whether to use strided convolutions instead of max pooling.
+ """
+ self.reuse = False
+ self.batch_size = batch_size
+ self.layer_stage_sizes = layer_stage_sizes
+ self.name = name
+ self.num_classes = num_classes
+ self.batch_norm_use = batch_norm_use
+ self.inner_layer_depth = inner_layer_depth
+ self.strided_dim_reduction = strided_dim_reduction
+ self.build_completed = False
+
+ def __call__(self, image_input, training=False, dropout_rate=0.0):
+ """
+ Runs the CNN producing the predictions and the gradients.
+ :param image_input: Image input to produce embeddings for. e.g. for EMNIST [batch_size, 28, 28, 1]
+ :param training: A flag indicating training or evaluation
+ :param dropout_rate: A tf placeholder of type tf.float32 indicating the amount of dropout applied
+ :return: Embeddings of size [batch_size, self.num_classes]
+ """
+
+ with tf.variable_scope(self.name, reuse=self.reuse):
+ layer_features = []
+ with tf.variable_scope('VGGNet'):
+ outputs = image_input
+ for i in range(len(self.layer_stage_sizes)):
+ with tf.variable_scope('conv_stage_{}'.format(i)):
+ for j in range(self.inner_layer_depth):
+ with tf.variable_scope('conv_{}_{}'.format(i, j)):
+ if (j == self.inner_layer_depth-1) and self.strided_dim_reduction:
+ stride = 2
+ else:
+ stride = 1
+ outputs = tf.layers.conv2d(outputs, self.layer_stage_sizes[i], [3, 3],
+ strides=(stride, stride),
+ padding='SAME', activation=None)
+ outputs = leaky_relu(outputs, name="leaky_relu{}".format(i))
+ layer_features.append(outputs)
+ if self.batch_norm_use:
+ outputs = batch_norm(outputs, decay=0.99, scale=True,
+ center=True, is_training=training, renorm=False)
+ if self.strided_dim_reduction==False:
+ outputs = tf.layers.max_pooling2d(outputs, pool_size=(2, 2), strides=2)
+
+ outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=training)
+ # apply dropout only at dimensionality
+ # reducing steps, i.e. the last layer in
+ # every group
+
+ c_conv_encoder = outputs
+ c_conv_encoder = tf.contrib.layers.flatten(c_conv_encoder)
+ c_conv_encoder = tf.layers.dense(c_conv_encoder, units=self.num_classes)
+
+ self.reuse = True
+ self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
+
+ if not self.build_completed:
+ self.build_completed = True
+ count_parameters(self.variables, "VGGNet")
+
+ return c_conv_encoder, layer_features
+
+
+class FCCLayerClassifier:
+ def __init__(self, batch_size, layer_stage_sizes, name, num_classes, batch_norm_use=False,
+ inner_layer_depth=2, strided_dim_reduction=True):
+
+ """
+ Initializes a FCC Classifier architecture
+ """
+ self.reuse = False
+ self.batch_size = batch_size
+ self.layer_stage_sizes = layer_stage_sizes
+ self.name = name
+ self.num_classes = num_classes
+ self.batch_norm_use = batch_norm_use
+ self.inner_layer_depth = inner_layer_depth
+ self.strided_dim_reduction = strided_dim_reduction
+ self.build_completed = False
+
+ def __call__(self, image_input, training=False, dropout_rate=0.0):
+ """
+ Runs the CNN producing the predictions and the gradients.
+ :param image_input: Image input to produce embeddings for. e.g. for EMNIST [batch_size, 28, 28, 1]
+ :param training: A flag indicating training or evaluation
+ :param dropout_rate: A tf placeholder of type tf.float32 indicating the amount of dropout applied
+ :return: Embeddings of size [batch_size, self.num_classes]
+ """
+
+ with tf.variable_scope(self.name, reuse=self.reuse):
+ layer_features = []
+ with tf.variable_scope('FCCLayerNet'):
+ outputs = image_input
+ for i in range(len(self.layer_stage_sizes)):
+ with tf.variable_scope('conv_stage_{}'.format(i)):
+ for j in range(self.inner_layer_depth):
+ with tf.variable_scope('conv_{}_{}'.format(i, j)):
+ outputs = tf.layers.dense(outputs, units=self.layer_stage_sizes[i])
+ outputs = leaky_relu(outputs, name="leaky_relu{}".format(i))
+ layer_features.append(outputs)
+ if self.batch_norm_use:
+ outputs = batch_norm(outputs, decay=0.99, scale=True,
+ center=True, is_training=training, renorm=False)
+ outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=training)
+ # apply dropout only at dimensionality
+ # reducing steps, i.e. the last layer in
+ # every group
+
+ c_conv_encoder = outputs
+ c_conv_encoder = tf.contrib.layers.flatten(c_conv_encoder)
+ c_conv_encoder = tf.layers.dense(c_conv_encoder, units=self.num_classes)
+
+ self.reuse = True
+ self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
+
+ if not self.build_completed:
+ self.build_completed = True
+ count_parameters(self.variables, "FCCLayerNet")
+
+ return c_conv_encoder, layer_features
diff --git a/network_builder.py b/network_builder.py
new file mode 100644
index 0000000..eed8da7
--- /dev/null
+++ b/network_builder.py
@@ -0,0 +1,177 @@
+import tensorflow as tf
+from network_architectures import VGGClassifier, FCCLayerClassifier
+
+
+class ClassifierNetworkGraph:
+ def __init__(self, input_x, target_placeholder, dropout_rate,
+ batch_size=100, n_classes=100, is_training=True, augment_rotate_flag=True,
+ tensorboard_use=False, use_batch_normalization=False, strided_dim_reduction=True,
+ network_name='VGG_classifier'):
+
+ """
+ Initializes a Classifier Network Graph that can build models, train, compute losses and save summary statistics
+ and images
+ :param input_x: A placeholder that will feed the input images, usually of size [batch_size, height, width,
+ channels]
+ :param target_placeholder: A target placeholder of size [batch_size,]. The classes should be in index form
+ i.e. not one hot encoding, that will be done automatically by tf
+ :param dropout_rate: A placeholder of size [None] that holds a single float that defines the amount of dropout
+ to apply to the network. i.e. for 0.1 drop 0.1 of neurons
+ :param batch_size: The batch size
+ :param num_channels: Number of channels
+ :param n_classes: Number of classes we will be classifying
+ :param is_training: A placeholder that will indicate whether we are training or not
+ :param augment_rotate_flag: A placeholder indicating whether to apply rotations augmentations to our input data
+ :param tensorboard_use: Whether to use tensorboard in this experiment
+ :param use_batch_normalization: Whether to use batch normalization between layers
+ :param strided_dim_reduction: Whether to use strided dim reduction instead of max pooling
+ """
+ self.batch_size = batch_size
+ if network_name == "VGG_classifier":
+ self.c = VGGClassifier(self.batch_size, name="classifier_neural_network",
+ batch_norm_use=use_batch_normalization, num_classes=n_classes,
+ layer_stage_sizes=[64, 128, 256], strided_dim_reduction=strided_dim_reduction)
+ elif network_name == "FCCClassifier":
+ self.c = FCCLayerClassifier(self.batch_size, name="classifier_neural_network",
+ batch_norm_use=use_batch_normalization, num_classes=n_classes,
+ layer_stage_sizes=[64, 128, 256], strided_dim_reduction=strided_dim_reduction)
+
+ self.input_x = input_x
+ self.dropout_rate = dropout_rate
+ self.targets = target_placeholder
+
+ self.training_phase = is_training
+ self.n_classes = n_classes
+ self.iterations_trained = 0
+
+ self.augment_rotate = augment_rotate_flag
+ self.is_tensorboard = tensorboard_use
+ self.strided_dim_reduction = strided_dim_reduction
+ self.use_batch_normalization = use_batch_normalization
+
+ def loss(self):
+ """build models, calculates losses, saves summary statistcs and images.
+ Returns:
+ dict of losses.
+ """
+ with tf.name_scope("losses"):
+ image_inputs = self.data_augment_batch(self.input_x) # conditionally apply augmentaions
+ true_outputs = self.targets
+ # produce predictions and get layer features to save for visual inspection
+ preds, layer_features = self.c(image_input=image_inputs, training=self.training_phase,
+ dropout_rate=self.dropout_rate)
+ # compute loss and accuracy
+ correct_prediction = tf.equal(tf.argmax(preds, 1), tf.cast(true_outputs, tf.int64))
+ accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+ crossentropy_loss = tf.reduce_mean(
+ tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_outputs, logits=preds))
+
+ # add loss and accuracy to collections
+ tf.add_to_collection('crossentropy_losses', crossentropy_loss)
+ tf.add_to_collection('accuracy', accuracy)
+
+ # save summaries for the losses, accuracy and image summaries for input images, augmented images
+ # and the layer features
+ if len(self.input_x.get_shape().as_list()) == 4:
+ self.save_features(name="VGG_features", features=layer_features)
+ tf.summary.image('image', [tf.concat(tf.unstack(self.input_x, axis=0), axis=0)])
+ tf.summary.image('augmented_image', [tf.concat(tf.unstack(image_inputs, axis=0), axis=0)])
+ tf.summary.scalar('crossentropy_losses', crossentropy_loss)
+ tf.summary.scalar('accuracy', accuracy)
+
+ return {"crossentropy_losses": tf.add_n(tf.get_collection('crossentropy_losses'),
+ name='total_classification_loss'),
+ "accuracy": tf.add_n(tf.get_collection('accuracy'), name='total_accuracy')}
+
+ def save_features(self, name, features, num_rows_in_grid=4):
+ """
+ Saves layer features in a grid to be used in tensorboard
+ :param name: Features name
+ :param features: A list of feature tensors
+ """
+ for i in range(len(features)):
+ shape_in = features[i].get_shape().as_list()
+ channels = shape_in[3]
+ y_channels = num_rows_in_grid
+ x_channels = int(channels / y_channels)
+
+ activations_features = tf.reshape(features[i], shape=(shape_in[0], shape_in[1], shape_in[2],
+ y_channels, x_channels))
+
+ activations_features = tf.unstack(activations_features, axis=4)
+ activations_features = tf.concat(activations_features, axis=2)
+ activations_features = tf.unstack(activations_features, axis=3)
+ activations_features = tf.concat(activations_features, axis=1)
+ activations_features = tf.expand_dims(activations_features, axis=3)
+ tf.summary.image('{}_{}'.format(name, i), activations_features)
+
+ def rotate_image(self, image):
+ """
+ Rotates a single image
+ :param image: An image to rotate
+ :return: A rotated or a non rotated image depending on the result of the flip
+ """
+ no_rotation_flip = tf.unstack(
+ tf.random_uniform([1], minval=1, maxval=100, dtype=tf.int32, seed=None,
+ name=None)) # get a random number between 1 and 100
+ flip_boolean = tf.less_equal(no_rotation_flip[0], 50)
+ # if that number is less than or equal to 50 then set to true
+ random_variable = tf.unstack(tf.random_uniform([1], minval=1, maxval=3, dtype=tf.int32, seed=None, name=None))
+ # get a random variable between 1 and 3 for how many degrees the rotation will be i.e. k=1 means 1*90,
+ # k=2 2*90 etc.
+ image = tf.cond(flip_boolean, lambda: tf.image.rot90(image, k=random_variable[0]),
+ lambda: image) # if flip_boolean is true the rotate if not then do not rotate
+ return image
+
+ def rotate_batch(self, batch_images):
+ """
+ Rotate a batch of images
+ :param batch_images: A batch of images
+ :return: A rotated batch of images (some images will not be rotated if their rotation flip ends up False)
+ """
+ shapes = map(int, list(batch_images.get_shape()))
+ if len(list(batch_images.get_shape())) < 4:
+ return batch_images
+ batch_size, x, y, c = shapes
+ with tf.name_scope('augment'):
+ batch_images_unpacked = tf.unstack(batch_images)
+ new_images = []
+ for image in batch_images_unpacked:
+ new_images.append(self.rotate_image(image))
+ new_images = tf.stack(new_images)
+ new_images = tf.reshape(new_images, (batch_size, x, y, c))
+ return new_images
+
+ def data_augment_batch(self, batch_images):
+ """
+ Augments data with a variety of augmentations, in the current state only does rotations.
+ :param batch_images: A batch of images to augment
+ :return: Augmented data
+ """
+ batch_images = tf.cond(self.augment_rotate, lambda: self.rotate_batch(batch_images), lambda: batch_images)
+ return batch_images
+
+ def train(self, losses, learning_rate=1e-3, beta1=0.9):
+ """
+ Args:
+ losses dict.
+ Returns:
+ train op.
+ """
+ c_opt = tf.train.AdamOptimizer(beta1=beta1, learning_rate=learning_rate)
+ update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) # Needed for correct batch norm usage
+ with tf.control_dependencies(update_ops):
+ c_error_opt_op = c_opt.minimize(losses["crossentropy_losses"], var_list=self.c.variables,
+ colocate_gradients_with_ops=True)
+
+ return c_error_opt_op
+
+ def init_train(self):
+ """
+ Builds graph ops and returns them
+ :return: Summary, losses and training ops
+ """
+ losses_ops = self.loss()
+ c_error_opt_op = self.train(losses_ops)
+ summary_op = tf.summary.merge_all()
+ return summary_op, losses_ops, c_error_opt_op
diff --git a/notebooks/.ipynb_checkpoints/Introduction_to_tensorflow-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Introduction_to_tensorflow-checkpoint.ipynb
new file mode 100644
index 0000000..8d25d8c
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Introduction_to_tensorflow-checkpoint.ipynb
@@ -0,0 +1,557 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Introduction to TensorFlow\n",
+ "\n",
+ "## Computation graphs\n",
+ "\n",
+ "In the first semester we used the NumPy-based `mlp` Python package to illustrate the concepts involved in automatically propagating gradients through multiple-layer neural network models. We also looked at how to use these calculated derivatives to do gradient-descent based training of models in supervised learning tasks such as classification and regression.\n",
+ "\n",
+ "A key theme in the first semester's work was the idea of defining models in a modular fashion. There we considered models composed of a sequence of *layer* modules, the output of each of which fed into the input of the next in the sequence and each applying a transformation to map inputs to outputs. By defining a standard interface to layer objects with each defining a `fprop` method to *forward propagate* inputs to outputs, and a `bprop` method to *back propagate* gradients with respect to the output of the layer to gradients with respect to the input of the layer, the layer modules could be composed together arbitarily and activations and gradients forward and back propagated through the whole stack respectively.\n",
+ "\n",
+ "
\n",
+ " \n",
+ " 'Pipeline' model composed of sequence of single input, single output layer modules\n",
+ "
\n",
+ "\n",
+ "By construction a layer was defined as an object with a single array input and single array output. This is a natural fit for the architectures of standard feedforward networks which can be thought of a single pipeline of transformations from user provided input data to predicted outputs as illustrated in the figure above. \n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Models which fit less well into pipeline structure: left, a sequence-to-sequence recurrent network; right, a feed forward network with skip connections.\n",
+ "
\n",
+ "\n",
+ "Towards the end of last semester however we encountered several models which do not fit so well in to this pipeline-like structure. For instance (unrolled) recurrent neural networks tend to have inputs feeding in to and outputs feeding out from multiple points along a deep feedforward model corresponding to the updates of the hidden recurrent state, as illustrated in the left panel in the figure above. It is not trivial to see how to map this structure to our layer based pipeline. Similarly models with skip connections between layers as illustrated in the right panel of the above figure also do not fit particularly well in to a pipeline structure.\n",
+ "\n",
+ "Ideally we would like to be able to compose modular components in more general structures than the pipeline structure we have being using so far. In particular it turns out to be useful to be able to deal with models which have structures defined by arbitrary [*directed acyclic graphs*](https://en.wikipedia.org/wiki/Directed_acyclic_graph) (DAGs), that is graphs connected by directed edges and without any directed cycles. Both the recurrent network and skip-connections examples can be naturally expressed as DAGs as well many other model structures.\n",
+ "\n",
+ "When working with these more general graphical structures, rather than considering a graph made up of layer modules, it often more useful to consider lower level mathematical operations or *ops* that make up the computation as the fundamental building block. A DAG composed of ops is often termed a *computation graph*. THis terminolgy was covered briefly in [lecture 6](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp06-enc.pdf), and also in the [MLPR course](http://www.inf.ed.ac.uk/teaching/courses/mlpr/2016/notes/w5a_backprop.html). The backpropagation rules we used to propagate gradients through a stack of layer modules can be naturally generalised to apply to computation graphs, with this method of applying the chain rule to automatically propagate gradients backwards through a general computation graph also sometimes termed [*reverse-mode automatic differentiation*](https://en.wikipedia.org/wiki/Automatic_differentiation#Reverse_accumulation).\n",
+ "\n",
+ "
\n",
+ " \n",
+ " Computation / data flow graph for an affine transformation $\\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}$\n",
+ "
\n",
+ "\n",
+ "The figure above shows a very simple computation graph corresponding to the mathematical expression $\\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}$, i.e. the affine transformation we encountered last semester. Here the nodes of the graph are operations and the edges the vector or matrix values passed between operations. The opposite convention with nodes as values and edges as operations is also sometimes used. Note that just like there was ambiguity about what to define as a layer (as discussed previously at beginning of the [third lab notebook](03_Multiple_layer_models.ipynb), there are a range of choices for the level of abstraction to use in the op nodes in a computational graph. For instance, we could also have chosen to express the above computational graph with a single `AffineTransform` op node with three inputs (one matrix, two vector) and one vector output. Equally we might choose to express the `MatMul` op in terms of the underlying individual scalar addition and multiplication operations. What to consider an operation is therefore somewhat a matter of choice and what is convenient in a particular setting.\n",
+ "\n",
+ "## TensorFlow\n",
+ "\n",
+ "To allow us to work with models defined by more general computation graphs and to avoid the need to write `fprop` and `bprop` methods for each new model component we want to try out, this semester we will be using the open-source computation graph framework [TensorFlow](https://www.tensorflow.org/), originally developed by the Google Brain team:\n",
+ "\n",
+ "> TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs \n",
+ "in a desktop, server, or mobile device with a single API.\n",
+ "\n",
+ "TensorFlow allows complex computation graphs (also known as data flow graphs in TensorFlow parlance) to be defined via a Python interface, with efficient C++ implementations for running the corresponding operations on different devices. TensorFlow also includes tools for automatic gradient computation and a large and growing suite of pre-define operations useful for gradient-based training of machine learning models.\n",
+ "\n",
+ "In this notebook we will introduce some of the basic elements of constructing, training and evaluating models with TensorFlow. This will use similar material to some of the [official TensorFlow tutorials](https://www.tensorflow.org/tutorials/) but with an additional emphasis of making links to the material covered in this course last semester. For those who have not used a computational graph framework such as TensorFlow or Theano before you may find the [basic usage tutorial](https://www.tensorflow.org/get_started/basic_usage) useful to go through.\n",
+ "\n",
+ "### Installing TensorFlow\n",
+ "\n",
+ "To install TensorFlow, open a terminal, activate your Conda `mlp` environment using\n",
+ "\n",
+ "```\n",
+ "source activate mlp\n",
+ "```\n",
+ "\n",
+ "and then run\n",
+ "\n",
+ "```\n",
+ "pip install tensorflow # for CPU users\n",
+ "```\n",
+ "\n",
+ "```\n",
+ "pip install tensorflow_gpu # for GPU users\n",
+ "```\n",
+ "\n",
+ "This should locally install the stable release version of TensorFlow (currently 1.4.1) in your Conda environment. After installing TensorFlow you may need to restart the kernel in the notebook to allow it to be imported."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 1: EMNIST softmax regression\n",
+ "\n",
+ "As a first example we will train a simple softmax regression model to classify handwritten digit images from the EMNIST data set encountered last semester (for those fed up of working with EMNIST - don't worry you will soon be moving on to other datasets!). This is equivalent to the model implemented in the first exercise of the third lab notebook. We will walk through constructing an equivalent model in TensorFlow and explain new TensorFlow model concepts as we use them. You should run each cell as you progress through the exercise.\n",
+ "\n",
+ "Similarly to the common convention of importing NumPy under the shortform alias `np` it is common to import the Python TensorFlow top-level module under the alias `tf`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/antreas/anaconda2/envs/mlp/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+ " return f(*args, **kwds)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "import sys\n",
+ "sys.path.append(\"..\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We begin by defining [*placeholder*](https://www.tensorflow.org/api_docs/python/io_ops/placeholders) objects for the data inputs and targets arrays. These are nodes in the computation graph to which we will later *feed* in external data, such as batches of training set inputs and targets. This abstraction allows us to reuse the same computation graph for different data inputs - we can think of placeholders as acting equivalently to the arguments of a function. It is actually possible to feed data into any node in a TensorFlow graph however the advantage of using a placeholder is that is *must* always have a value fed into it (an exception will be raised if a value isn't provided) and no arbitrary alternative values needs to be entered.\n",
+ "\n",
+ "The `tf.placeholder` function has three arguments:\n",
+ "\n",
+ " * `dtype` : The [TensorFlow datatype](https://www.tensorflow.org/api_docs/python/framework/tensor_types) for the tensor e.g. `tf.float32` for single-precision floating point values.\n",
+ " * `shape` (optional) : An iterable defining the shape (size of each dimension) of the tensor e.g. `shape=(5, 2)` would indicate a 2D tensor (matrix) with first dimension of size 5 and second dimension of size 2. An entry of `None` in the shape definition corresponds to the corresponding dimension size being left unspecified, so for example `shape=(None, 28, 28)` would allow any 3D inputs with final two dimensions of size 28 to be inputted.\n",
+ " * `name` (optional): String argument defining a name for the tensor which can be useful when visualising a computation graph and for debugging purposes.\n",
+ " \n",
+ "As we will generally be working with batches of datapoints, both the `inputs` and `targets` will be 2D tensors with the first dimension corresponding to the batch size (set as `None` here to allow it to specified later) and the second dimension corresponding to the size of each input or output vector. As in the previous semester's work we will use a 1-of-K encoding for the class targets so for EMNIST each output corresponds to a vector of length 47 (number of digit/letter classes)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "inputs = tf.placeholder(tf.float32, [None, 784], 'inputs')\n",
+ "targets = tf.placeholder(tf.float32, [None, 47], 'targets')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now define [*variable*](https://www.tensorflow.org/api_docs/python/state_ops/variables) objects for the model parameters. Variables are stateful tensors in the computation graph - they have to be explicitly initialised and their internal values can be updated as part of the operations in a graph e.g. gradient updates to model parameter during training. They can also be saved to disk and pre-saved values restored in to a graph at a later time.\n",
+ "\n",
+ "The `tf.Variable` constructor takes an `initial_value` as its first argument; this should be a TensorFlow tensor which specifies the initial value to assign to the variable, often a constant tensor such as all zeros, or random samples from a distribution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "weights = tf.Variable(tf.zeros([784, 47]))\n",
+ "biases = tf.Variable(tf.zeros([47]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now build the computation graph corresponding to producing the predicted outputs of the model (log unnormalised class probabilities) given the data inputs and model parameters. We use the TensorFlow [`matmul`](https://www.tensorflow.org/api_docs/python/math_ops/matrix_math_functions#matmul) op to compute the matrix-matrix product between the 2D array of input vectors and the weight matrix parameter variable. TensorFlow [overloads all of the common arithmetic operators](http://stackoverflow.com/a/35095052) for tensor objects so `x + y` where at least one of `x` or `y` is a tensor instance (both `tf.placeholder` and `tf.Variable` return (sub-classes) of `tf.Tensor`) corresponds to the TensorFlow elementwise addition op `tf.add`. Further elementwise binary arithmetic operators like addition follow NumPy style [broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html), so in the expression below the `+ biases` sub-expression will correspond to creating an operation in the computation graph which adds the bias vector to each of the rows of the 2D tensor output of the `matmul` op."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outputs = tf.matmul(inputs, weights) + biases"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "While we could have defined `outputs` as the softmax of the expression above to produce normalised class probabilities as the outputs of the model, as discussed last semester when using a softmax output combined with a cross-entropy error function it usually desirable from a numerical stability and efficiency perspective to wrap the softmax computation in to the error computation (as done in the `CrossEntropySoftmaxError` class in our `mlp` framework). \n",
+ "\n",
+ "In TensorFlow this can be achieved with the `softmax_cross_entropy_with_logits` op which is part of the `tf.nn` submodule which contains a number of ops specifically for neural network type models. This op takes as its first input log unnormalised class probabilities (sometimes termed logits) and as second input the class label targets which should be of the same dimension as the first input. By default the last dimension of the input tensors is assumed to correspond to the class dimension - this can be altered via an optional `dim` argument.\n",
+ "\n",
+ "The output of the `softmax_cross_entropy_with_logits` op here is a 1D tensor with a cross-entropy error value for each data point in the batch. We wish to minimise the mean cross-entropy error across the full dataset and will use the mean of the error on the batch as a stochastic estimator of this value. In TensorFlow ops which *reduce* a tensor along a dimension(s), for example by taking a sum, mean, or product, are prefixed with `reduce`, with the default behaviour being to perform the reduction across all dimensions of the input tensor and return a scalar output. Therefore the second line below will take the per data point cross-entropy errors and produce a single mean value across the whole batch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "per_datapoint_errors = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\n",
+ "error = tf.reduce_mean(per_datapoint_errors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although for the purposes of training we will use the cross-entropy error as this is differentiable, for evaluation we will also be interested in the classification accuracy i.e. what proportion of all of the predicted classes correspond to the true target label. We can calculate this in TensorFlow similarly to how we used NumPy to do this previously - we use the TensorFlow `tf.argmax` op to find the index of along the class dimension corresponding to the maximum predicted class probability and check if this is equal to the index along the class dimension of the 1-of-$k$ encoded target labels. Analagously to the error computation above, this computes per-datapoint values which we then need to average across with a `reduce_mean` op to produce the classification accuracy for a batch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "per_datapoint_pred_is_correct = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\n",
+ "accuracy = tf.reduce_mean(tf.cast(per_datapoint_pred_is_correct, tf.float32))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As mentioned previously TensorFlow is able to automatically calculate gradients of scalar computation graph outputs with respect to tensors in the computation graph. We can explicitly construct a new sub-graph corresponding to the gradient of a scalar with respect to one or more tensors in the graph using the [`tf.gradients`](https://www.tensorflow.org/api_docs/python/train/gradient_computation) function. \n",
+ "\n",
+ "TensorFlow also however includes a number of higher-level `Optimizer` classes in the `tf.train` module that internally deal with constructing graphs corresponding to the gradients of some scalar loss with respect to one or more `Variable` tensors in the graph (usually corresponding to model parameters) and then using these gradients to update the variables (roughly equivalent to the `LearningRule` classes in the `mlp` framework). The most basic `Optimizer` instance is the `GradientDescentOptimizer` which simply adds operations corresponding to basic (stochastic) gradient descent to the graph (i.e. no momentum, adaptive learning rates etc.). The `__init__` constructor method for this class takes one argument `learning_rate` corresponding to the gradient descent learning rate / step size encountered previously.\n",
+ "\n",
+ "Usually we are not interested in the `Optimizer` object other than in adding operations in the graph corresponding to the optimisation steps. This can be achieved using the `minimize` method of the object which takes as first argument the tensor object corresponding to the scalar loss / error to be minimized. A further optional keyword argument `var_list` can be used to specify a list of variables to compute the gradients of the loss with respect to and update; by default this is set to `None` which indicates to use all trainable variables in the current graph. The `minimize` method returns an operation corresponding to applying the gradient updates to the variables - we need to store a reference to this to allow us to run these operations later. Note we do not need to store a reference to the optimizer as we have no further need of this object hence commonly the steps of constructing the `Optimizer` and calling `minimize` are commonly all applied in a single line as below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_step = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(error)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have now constructed a computation graph which can compute predicted outputs, use these to calculate an error value (and accuracy) and use the gradients of the error with respect to the model parameter variables to update their values with a gradient descent step.\n",
+ "\n",
+ "Although we have defined our computation graph, we have not yet initialised any tensor data in memory - all of the tensor variables defined above are just symbolic representations of parts of the computation graph. We can think of the computation graph as a whole as being similar to a function - it defines a sequence of operations but does not directly run those operations on data itself.\n",
+ "\n",
+ "To run the operations in (part of) a TensorFlow graph we need to create a [`Session`](https://www.tensorflow.org/api_docs/python/client/session_management) object:\n",
+ "\n",
+ "> A `Session` object encapsulates the environment in which `Operation` objects are executed, and `Tensor` objects are evaluated.\n",
+ "\n",
+ "A session object can be constructed using either `tf.Session()` or `tf.InteractiveSession()`. The only difference in the latter is that it installs itself as the default session on construction. This can be useful in interactive contexts such as shells or the notebook interface in which an alternative to running a graph operation using the session `run` method (see below) is to call the `eval` method of an operation e.g. `op.eval()`; generally a session in which the op runs needs to be passed to `eval`; however if an interactive session is used, then this is set as a default to use in `eval` calls."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sess = tf.InteractiveSession()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The key property of a session object is its `run` method. This takes an operation (or list of operations) in a defined graph as an argument and runs the parts of the computation graph necessary to evaluate the output(s) (if any) of the operation(s), and additionally performs any updates to variables states defined by the graph (e.g. gradient updates of parameters). The output values if any of the operation(s) are returned by the `run` call.\n",
+ "\n",
+ "A standard operation which needs to be called before any other operations on a graph which includes variable nodes is a variable *initializer* operation. This, as the name suggests, initialises the values of the variables in the session to the values defined by the `initial_value` argument when adding the variables to the graph. For instance for the graph we have defined here this will initialise the `weights` variable value in the session to a 2D array of zeros of shape `(784, 10)` and the `biases` variable to a 1D array of shape `(10,)`.\n",
+ "\n",
+ "We can access initializer ops for each variable individually using the `initializer` property of the variables in question and then individually run these, however a common pattern is to use the `tf.global_variables_initializer()` function to create a single initializer op which will initialise all globally defined variables in the default graph and then run this as done below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "init_op = tf.global_variables_initializer()\n",
+ "sess.run(init_op)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are now almost ready to begin training our defined model, however as a final step we need to create objects for accessing batches of EMNIST input and target data. In the tutorial code provided in `tf.examples.tutorials.mnist` there is an `input_data` sub-module which provides a `read_data_sets` function for downloading the MNIST data and constructing an object for iterating over MNIST data. However in the `mlp` package we already have the MNIST and EMNIST data provider classes that we used extensively last semester, and corresponding local copies of the MNIST and EMNIST data, so we will use that here as it provides all the necessary functionality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import data_providers as data_providers\n",
+ "train_data = data_providers.EMNISTDataProvider('train', batch_size=50, flatten=True, one_hot=True)\n",
+ "valid_data = data_providers.EMNISTDataProvider('valid', batch_size=50, flatten=True, one_hot=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are now all set to train our model. As when training models last semester, the training procedure will involve two nested loops - an outer loop corresponding to multiple full-passes through the dataset or *epochs* and an inner loop iterating over individual batches in the training data.\n",
+ "\n",
+ "The `init_op` we ran with `sess.run` previously did not depend on the placeholders `inputs` and `target` in our graph, so we simply ran it with `sess.run(init_op)`. The `train_step` operation corresponding to the gradient based updates of the `weights` and `biases` parameter variables does however depend on the `inputs` and `targets` placeholders and so we need to specify values to *feed* into these placeholders; as we wish the gradient updates to be calculated using the gradients with respect to a batch of inputs and targets, the values that we feed in are the input and target batches. This is specified using the keyword `feed_dict` argument to the session `run` method. As the name suggests this should be a Python dictionary (`dict`) with keys corresponding to references to the tensors in the graph to feed values in to and values the corresponding array values to feed in (typically NumPy `ndarray` instances) - here we have `feed_dict = {inputs: input_batch, targets: target_batch}`.\n",
+ "\n",
+ "Another difference in our use of the session `run` method below is that we call it with a list of two operations - `[train_step, error]` rather than just a single operation. This allows the output (and variable updates) of multiple operations in a graph to be evaluated together - here we both run the `train_step` operation to update the parameter values and evaluate the `error` operation to return the mean error on the batch. Although we could split this into two separate session `run` calls, as the operations calculating the batch error will need to be evaluated when running the `train_step` operation (as this is the value gradients are calculated with respect to) this would involve redoing some of the computation and so be less efficient than combining them in a single `run` call.\n",
+ "\n",
+ "As we are running two different operations, the `run` method returns two values here. The `train_step` operation has no outputs and so the first return value is `None` - in the code below we assign this to `_`, this being a common convention in Python code for assigning return values we are not interested in using. The second return value is the average error across the batch which we assign to `batch_error` and use to keep a running average of the dataset error across the epochs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End of epoch 1: running error average = 1.40\n",
+ "End of epoch 2: running error average = 1.25\n",
+ "End of epoch 3: running error average = 1.22\n",
+ "End of epoch 4: running error average = 1.20\n",
+ "End of epoch 5: running error average = 1.19\n",
+ "End of epoch 6: running error average = 1.18\n",
+ "End of epoch 7: running error average = 1.18\n",
+ "End of epoch 8: running error average = 1.17\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_epoch = 20\n",
+ "for e in range(num_epoch):\n",
+ " running_error = 0.\n",
+ " for input_batch, target_batch in train_data:\n",
+ " _, batch_error = sess.run(\n",
+ " [train_step, error], \n",
+ " feed_dict={inputs: input_batch, targets: target_batch})\n",
+ " running_error += batch_error\n",
+ " running_error /= train_data.num_batches\n",
+ " print('End of epoch {0}: running error average = {1:.2f}'.format(e + 1, running_error))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To check your understanding of using sessions objects to evaluate parts of a graph and feeding values in to a graph, complete the definition of the function in the cell below. This should iterate across all batches in a provided data provider and calculate the error and classification accuracy for each, accumulating the average error and accuracy values across the whole dataset and returning these as a tuple."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_error_and_accuracy(data):\n",
+ " \"\"\"Calculate average error and classification accuracy across a dataset.\n",
+ " \n",
+ " Args:\n",
+ " data: Data provider which iterates over input-target batches in dataset.\n",
+ " \n",
+ " Returns:\n",
+ " Tuple with first element scalar value corresponding to average error\n",
+ " across all batches in dataset and second value corresponding to\n",
+ " average classification accuracy across all batches in dataset.\n",
+ " \"\"\"\n",
+ " err = 0\n",
+ " acc = 0\n",
+ " for input_batch, target_batch in data:\n",
+ " err += sess.run(error, feed_dict={inputs: input_batch, targets: target_batch})\n",
+ " acc += sess.run(accuracy, feed_dict={inputs: input_batch, targets: target_batch})\n",
+ " err /= data.num_batches\n",
+ " acc /= data.num_batches\n",
+ " return err, acc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Test your implementation by running the cell below - this should print the error and accuracy of the trained model on the validation and training datasets if implemented correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train data: Error=1.14 Accuracy=0.69\n",
+ "Valid data: Error=1.29 Accuracy=0.66\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Train data: Error={0:.2f} Accuracy={1:.2f}'\n",
+ " .format(*get_error_and_accuracy(train_data)))\n",
+ "print('Valid data: Error={0:.2f} Accuracy={1:.2f}'\n",
+ " .format(*get_error_and_accuracy(valid_data)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 2: Explicit graphs, name scopes, summaries and TensorBoard\n",
+ "\n",
+ "In the exercise above we introduced most of the basic concepts needed for constructing graphs in TensorFlow and running graph operations. In an attempt to avoid introducing too many new terms and syntax at once however we skipped over some of the non-essential elements of creating and running models in TensorFlow, in particular some of the provided functionality for organising and structuring the computation graphs created and for monitoring the progress of training runs.\n",
+ "\n",
+ "Now that you are hopefully more familiar with the basics of TensorFlow we will introduce some of these features as they are likely to provide useful when you are building and training more complex models in the rest of this semester.\n",
+ "\n",
+ "Although we started off by motivating TensorFlow as a framework which builds computation graphs, in the code above we never explicitly referenced a graph object. This is because TensorFlow always registers a default graph at start up and all operations are added to this graph by default. The default graph can be accessed using `tf.get_default_graph()`. For example running the code in the cell below will assign a reference to the default graph to `default_graph` and print the total number of operations in the current graph definition."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of operations in graph: 198\n"
+ ]
+ }
+ ],
+ "source": [
+ "default_graph = tf.get_default_graph()\n",
+ "print('Number of operations in graph: {0}'\n",
+ " .format(len(default_graph.get_operations())))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also explicitly create a new graph object using `tf.Graph()`. This may be useful if we wish to build up several independent computation graphs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "graph = tf.Graph()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To add operations to a constructed graph object, we use the `graph.as_default()` [context manager](http://book.pythontips.com/en/latest/context_managers.html). Context managers are used with the `with` statement in Python - `with context_manager:` opens a block in Python in which a special `__enter__` method of the `context_manager` object is called before the code in the block is run and a further special `__exit__` method is run after the block code has finished execution. This can be used to for example manage allocation of resources (e.g. file handles) but also to locally change some 'context' in the code - in the example here, `graph.as_default()` is a context manager which changes the default graph within the following block to be `graph` before returning to the previous default graph once the block code is finished running. Context managers are used extensively in TensorFlow so it is worth being familiar with how they work.\n",
+ "\n",
+ "Another common context manager usage in TensorFlow is to define *name scopes*. As we encountered earlier, individual operations in a TensorFlow graph can be assigned names. As we will see later this is useful for making graphs interpretable when we use the tools provided in TensorFlow for visualising them. As computation graphs can become very big (even the quite simple graph we created in the first exercise has around 100 operations in it) even with interpretable names attached to the graph operations it can still be difficult to understand and debug what is happening in a graph. Therefore rather than simply allowing a single-level naming scheme to be applied to the individual operations in the graph, TensorFlow supports hierachical naming of sub-graphs. This allows sets of related operations to be grouped together under a common name, and thus allows both higher and lower level structure in a graph to be easily identified.\n",
+ "\n",
+ "This hierarchical naming is performed by using the name scope context manager `tf.name_scope('name')`. Starting a block `with tf.name_scope('name'):`, will cause all the of the operations added to a graph within that block to be grouped under the name specified in the `tf.name_scope` call. Name scope blocks can be nested to allow finer-grained sub-groupings of operations. Name scopes can be used to group operations at various levels e.g. operations corresponding to inference/prediction versus training, grouping operations which correspond to the classical definition of a neural network layer etc.\n",
+ "\n",
+ "The code in the cell below uses both a `graph.as_default()` context manager and name scopes to create a second copy of the computation graph corresponding to softmax regression that we constructed in the previous exercise."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with graph.as_default():\n",
+ " with tf.name_scope('data'):\n",
+ " inputs = tf.placeholder(tf.float32, [None, 784], name='inputs')\n",
+ " targets = tf.placeholder(tf.float32, [None, 47], name='targets')\n",
+ " with tf.name_scope('parameters'):\n",
+ " weights = tf.Variable(tf.zeros([784, 47]), name='weights')\n",
+ " biases = tf.Variable(tf.zeros([47]), name='biases')\n",
+ " with tf.name_scope('model'):\n",
+ " outputs = tf.matmul(inputs, weights) + biases\n",
+ " with tf.name_scope('error'):\n",
+ " error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets))\n",
+ " with tf.name_scope('train'):\n",
+ " train_step = tf.train.GradientDescentOptimizer(0.5).minimize(error)\n",
+ " with tf.name_scope('accuracy'):\n",
+ " accuracy = tf.reduce_mean(tf.cast(\n",
+ " tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1)), tf.float32))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As hinted earlier TensorFlow comes with tools for visualising computation graphs. In particular [TensorBoard](https://www.tensorflow.org/how_tos/summaries_and_tensorboard/) is an interactive web application for amongst other things visualising TensorFlow computation graphs (we will explore some of its other functionality in the latter part of the exercise). Typically TensorBoard in launched from a terminal and a browser used to connect to the resulting locally running TensorBoard server instance. However for the purposes of graph visualisation it is also possible to embed a remotely-served TensorBoard graph visualisation interface in a Jupyter notebook using the helper function below (a slight variant of the recipe in [this notebook](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb)).\n",
+ "\n",
+ "Note: The code below seems to not work for some people when accessing the notebook in Firefox. You can either try loading the notebook in an alternative browser, or just skip this section for now and explore the graph visualisation tool when launching TensorBoard below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import display, HTML\n",
+ "import datetime\n",
+ "\n",
+ "def show_graph(graph_def, frame_size=(900, 600)):\n",
+ " \"\"\"Visualize TensorFlow graph.\"\"\"\n",
+ " if hasattr(graph_def, 'as_graph_def'):\n",
+ " graph_def = graph_def.as_graph_def()\n",
+ " timestamp = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n",
+ " code = \"\"\"\n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " \"\"\".format(height=frame_size[1], data=repr(str(graph_def)), id='graph'+timestamp)\n",
+ " iframe = \"\"\"\n",
+ " \n",
+ " \"\"\".format(width=frame_size[0], height=frame_size[1] + 20, src=code.replace('\"', '"'))\n",
+ " display(HTML(iframe))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the cell below to display a visualisation of the graph we just defined. Notice that by default all operations within a particular defined name scope are grouped under a single node; this allows the top-level structure of the graph and how data flows between the various components to be easily visualised. We can also expand these nodes however to interrogate the operations within them - simply double-click on one of the nodes to do this (double-clicking on the expanded node will cause it to collapse again). If you expand the `model` node you should see a graph closely mirroring the affine transform example given as a motivation above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_graph(graph)"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/01_Introduction.ipynb b/notebooks/01_Introduction.ipynb
deleted file mode 100644
index a25d342..0000000
--- a/notebooks/01_Introduction.ipynb
+++ /dev/null
@@ -1,669 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "nbpresent": {
- "id": "b167e6e2-05e0-4a4b-a6cc-47cab1c728b4"
- }
- },
- "source": [
- "# Introduction\n",
- "\n",
- "## Getting started with Jupyter notebooks\n",
- "\n",
- "The majority of your work in this course will be done using Jupyter notebooks so we will here introduce some of the basics of the notebook system. If you are already comfortable using notebooks or just would rather get on with some coding feel free to [skip straight to the exercises below](#Exercises).\n",
- "\n",
- "*Note: Jupyter notebooks are also known as IPython notebooks. The Jupyter system now supports languages other than Python [hence the name was changed to make it more language agnostic](https://ipython.org/#jupyter-and-the-future-of-ipython) however IPython notebook is still commonly used.*\n",
- "\n",
- "### Jupyter basics: the server, dashboard and kernels\n",
- "\n",
- "In launching this notebook you will have already come across two of the other key components of the Jupyter system - the notebook *server* and *dashboard* interface.\n",
- "\n",
- "We began by starting a notebook server instance in the terminal by running\n",
- "\n",
- "```\n",
- "jupyter notebook\n",
- "```\n",
- "\n",
- "This will have begun printing a series of log messages to terminal output similar to\n",
- "\n",
- "```\n",
- "$ jupyter notebook\n",
- "[I 08:58:24.417 NotebookApp] Serving notebooks from local directory: ~/mlpractical\n",
- "[I 08:58:24.417 NotebookApp] 0 active kernels\n",
- "[I 08:58:24.417 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/\n",
- "```\n",
- "\n",
- "The last message included here indicates the URL the application is being served at. The default behaviour of the `jupyter notebook` command is to open a tab in a web browser pointing to this address after the server has started up. The server can be launched without opening a browser window by running `jupyter notebook --no-browser`. This can be useful for example when running a notebook server on a remote machine over SSH. Descriptions of various other command options can be found by displaying the command help page using\n",
- "\n",
- "```\n",
- "juptyer notebook --help\n",
- "```\n",
- "\n",
- "While the notebook server is running it will continue printing log messages to terminal it was started from. Unless you detach the process from the terminal session you will need to keep the session open to keep the notebook server alive. If you want to close down a running server instance from the terminal you can use `Ctrl+C` - this will bring up a confirmation message asking you to confirm you wish to shut the server down. You can either enter `y` or skip the confirmation by hitting `Ctrl+C` again.\n",
- "\n",
- "When the notebook application first opens in your browser you are taken to the notebook *dashboard*. This will appear something like this\n",
- "\n",
- "\n",
- "\n",
- "The dashboard above is showing the `Files` tab, a list of files in the directory the notebook server was launched from. We can navigate in to a sub-directory by clicking on a directory name and back up to the parent directory by clicking the `..` link. An important point to note is that the top-most level that you will be able to navigate to is the directory you run the server from. This is a security feature and generally you should try to limit the access the server has by launching it in the highest level directory which gives you access to all the files you need to work with.\n",
- "\n",
- "As well as allowing you to launch existing notebooks, the `Files` tab of the dashboard also allows new notebooks to be created using the `New` drop-down on the right. It can also perform basic file-management tasks such as renaming and deleting files (select a file by checking the box alongside it to bring up a context menu toolbar).\n",
- "\n",
- "In addition to opening notebook files, we can also edit text files such as `.py` source files, directly in the browser by opening them from the dashboard. The in-built text-editor is less-featured than a full IDE but is useful for quick edits of source files and previewing data files.\n",
- "\n",
- "The `Running` tab of the dashboard gives a list of the currently running notebook instances. This can be useful to keep track of which notebooks are still running and to shutdown (or reopen) old notebook processes when the corresponding tab has been closed.\n",
- "\n",
- "### The notebook interface\n",
- "\n",
- "The top of your notebook window should appear something like this:\n",
- "\n",
- "\n",
- "\n",
- "The name of the current notebook is displayed at the top of the page and can be edited by clicking on the text of the name. Displayed alongside this is an indication of the last manual *checkpoint* of the notebook file. On-going changes are auto-saved at regular intervals; the check-point mechanism is mainly meant as a way to recover an earlier version of a notebook after making unwanted changes. Note the default system only currently supports storing a single previous checkpoint despite the `Revert to checkpoint` dropdown under the `File` menu perhaps suggesting otherwise.\n",
- "\n",
- "As well as having options to save and revert to checkpoints, the `File` menu also allows new notebooks to be created in same directory as the current notebook, a copy of the current notebook to be made and the ability to export the current notebook to various formats.\n",
- "\n",
- "The `Edit` menu contains standard clipboard functions as well as options for reorganising notebook *cells*. Cells are the basic units of notebooks, and can contain formatted text like the one you are reading at the moment or runnable code as we will see below. The `Edit` and `Insert` drop down menus offer various options for moving cells around the notebook, merging and splitting cells and inserting new ones, while the `Cell` menu allow running of code cells and changing cell types.\n",
- "\n",
- "The `Kernel` menu offers some useful commands for managing the Python process (kernel) running in the notebook. In particular it provides options for interrupting a busy kernel (useful for example if you realise you have set a slow code cell running with incorrect parameters) and to restart the current kernel. This will cause all variables currently defined in the workspace to be lost but may be necessary to get the kernel back to a consistent state after polluting the namespace with lots of global variables or when trying to run code from an updated module and `reload` is failing to work. \n",
- "\n",
- "To the far right of the menu toolbar is a kernel status indicator. When a dark filled circle is shown this means the kernel is currently busy and any further code cell run commands will be queued to happen after the currently running cell has completed. An open status circle indicates the kernel is currently idle.\n",
- "\n",
- "The final row of the top notebook interface is the notebook toolbar which contains shortcut buttons to some common commands such as clipboard actions and cell / kernel management. If you are interested in learning more about the notebook user interface you may wish to run through the `User Interface Tour` under the `Help` menu drop down.\n",
- "\n",
- "### Markdown cells: easy text formatting\n",
- "\n",
- "This entire introduction has been written in what is termed a *Markdown* cell of a notebook. [Markdown](https://en.wikipedia.org/wiki/Markdown) is a lightweight markup language intended to be readable in plain-text. As you may wish to use Markdown cells to keep your own formatted notes in notebooks, a small sampling of the formatting syntax available is below (escaped mark-up on top and corresponding rendered output below that); there are many much more extensive syntax guides - for example [this cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet).\n",
- "\n",
- "---\n",
- "\n",
- "```\n",
- "## Level 2 heading\n",
- "### Level 3 heading\n",
- "\n",
- "*Italicised* and **bold** text.\n",
- "\n",
- " * bulleted\n",
- " * lists\n",
- " \n",
- "and\n",
- "\n",
- " 1. enumerated\n",
- " 2. lists\n",
- "\n",
- "Inline maths $y = mx + c$ using [MathJax](https://www.mathjax.org/) as well as display style\n",
- "\n",
- "$$ ax^2 + bx + c = 0 \\qquad \\Rightarrow \\qquad x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a} $$\n",
- "```\n",
- "---\n",
- "\n",
- "## Level 2 heading\n",
- "### Level 3 heading\n",
- "\n",
- "*Italicised* and **bold** text.\n",
- "\n",
- " * bulleted\n",
- " * lists\n",
- " \n",
- "and\n",
- "\n",
- " 1. enumerated\n",
- " 2. lists\n",
- "\n",
- "Inline maths $y = mx + c$ using [MathJax]() as well as display maths\n",
- "\n",
- "$$ ax^2 + bx + c = 0 \\qquad \\Rightarrow \\qquad x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a} $$\n",
- "\n",
- "---\n",
- "\n",
- "We can also directly use HTML tags in Markdown cells to embed rich content such as images and videos.\n",
- "\n",
- "---\n",
- "```\n",
- "\n",
- "```\n",
- "---\n",
- "\n",
- "\n",
- "\n",
- "---\n",
- "\n",
- " \n",
- "### Code cells: in browser code execution\n",
- "\n",
- "Up to now we have not seen any runnable code. An example of a executable code cell is below. To run it first click on the cell so that it is highlighted, then either click the button on the notebook toolbar, go to `Cell > Run Cells` or use the keyboard shortcut `Ctrl+Enter`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello world!\n",
- "Hello again!\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Alarming hello!\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'And again!'"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from __future__ import print_function\n",
- "import sys\n",
- "\n",
- "print('Hello world!')\n",
- "print('Alarming hello!', file=sys.stderr)\n",
- "print('Hello again!')\n",
- "'And again!'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This example shows the three main components of a code cell.\n",
- "\n",
- "The most obvious is the input area. This (unsuprisingly) is used to enter the code to be run which will be automatically syntax highlighted.\n",
- "\n",
- "To the immediate left of the input area is the execution indicator / counter. Before a code cell is first run this will display `In [ ]:`. After the cell is run this is updated to `In [n]:` where `n` is a number corresponding to the current execution counter which is incremented whenever any code cell in the notebook is run. This can therefore be used to keep track of the relative order in which cells were last run. There is no fundamental requirement to run cells in the order they are organised in the notebook, though things will usually be more readable if you keep things in roughly in order!\n",
- "\n",
- "Immediately below the input area is the output area. This shows any output produced by the code in the cell. This is dealt with a little bit confusingly in the current Jupyter version. At the top any output to [`stdout`](https://en.wikipedia.org/wiki/Standard_streams#Standard_output_.28stdout.29) is displayed. Immediately below that output to [`stderr`](https://en.wikipedia.org/wiki/Standard_streams#Standard_error_.28stderr.29) is displayed. All of the output to `stdout` is displayed together even if there has been output to `stderr` between as shown by the suprising ordering in the output here. \n",
- "\n",
- "The final part of the output area is the *display* area. By default this will just display the returned output of the last Python statement as would usually be the case in a (I)Python interpreter run in a terminal. What is displayed for a particular object is by default determined by its special `__repr__` method e.g. for a string it is just the quote enclosed value of the string itself.\n",
- "\n",
- "### Useful keyboard shortcuts\n",
- "\n",
- "There are a wealth of keyboard shortcuts available in the notebook interface. For an exhaustive list see the `Keyboard Shortcuts` option under the `Help` menu. We will cover a few of those we find most useful below.\n",
- "\n",
- "Shortcuts come in two flavours: those applicable in *command mode*, active when no cell is currently being edited and indicated by a blue highlight around the current cell; those applicable in *edit mode* when the content of a cell is being edited, indicated by a green current cell highlight.\n",
- "\n",
- "In edit mode of a code cell, two of the more generically useful keyboard shortcuts are offered by the `Tab` key.\n",
- "\n",
- " * Pressing `Tab` a single time while editing code will bring up suggested completions of what you have typed so far. This is done in a scope aware manner so for example typing `a` + `[Tab]` in a code cell will come up with a list of objects beginning with `a` in the current global namespace, while typing `np.a` + `[Tab]` (assuming `import numpy as np` has been run already) will bring up a list of objects in the root NumPy namespace beginning with `a`.\n",
- " * Pressing `Shift+Tab` once immediately after opening parenthesis of a function or method will cause a tool-tip to appear with the function signature (including argument names and defaults) and its docstring. Pressing `Shift+Tab` twice in succession will cause an expanded version of the same tooltip to appear, useful for longer docstrings. Pressing `Shift+Tab` four times in succession will cause the information to be instead displayed in a pager docked to bottom of the notebook interface which stays attached even when making further edits to the code cell and so can be useful for keeping documentation visible when editing e.g. to help remember the name of arguments to a function and their purposes.\n",
- "\n",
- "A series of useful shortcuts available in both command and edit mode are `[modifier]+Enter` where `[modifier]` is one of `Ctrl` (run selected cell), `Shift` (run selected cell and select next) or `Alt` (run selected cell and insert a new cell after).\n",
- "\n",
- "A useful command mode shortcut to know about is the ability to toggle line numbers on and off for a cell by pressing `L` which can be useful when trying to diagnose stack traces printed when an exception is raised or when referring someone else to a section of code.\n",
- " \n",
- "### Magics\n",
- "\n",
- "There are a range of *magic* commands in IPython notebooks, than provide helpful tools outside of the usual Python syntax. A full list of the inbuilt magic commands is given [here](http://ipython.readthedocs.io/en/stable/interactive/magics.html), however three that are particularly useful for this course:\n",
- "\n",
- " * [`%%timeit`](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib#magic-timeit) Put at the beginning of a cell to time its execution and print the resulting timing statistics.\n",
- " * [`%precision`](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib#magic-precision) Set the precision for pretty printing of floating point values and NumPy arrays.\n",
- " * [`%debug`](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib#magic-debug) Activates the interactive debugger in a cell. Run after an exception has been occured to help diagnose the issue.\n",
- " \n",
- "### Plotting with `matplotlib`\n",
- "\n",
- "When setting up your environment one of the dependencies we asked you to install was `matplotlib`. This is an extensive plotting and data visualisation library which is tightly integrated with NumPy and Jupyter notebooks.\n",
- "\n",
- "When using `matplotlib` in a notebook you should first run the [magic command](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib)\n",
- "\n",
- "```\n",
- "%matplotlib inline\n",
- "```\n",
- "\n",
- "This will cause all plots to be automatically displayed as images in the output area of the cell they are created in. Below we give a toy example of plotting two sinusoids using `matplotlib` to show case some of the basic plot options. To see the output produced select the cell and then run it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "nbpresent": {
- "id": "2bced39d-ae3a-4603-ac94-fbb6a6283a96"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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Q1hyObYHv7peOjB4ktn6tvzoyfr+D3/emneMIIUy25DFIWQPBDWHIVLB6m52o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\nNxcbg9rX5f42QXy5+wRzNx9j34lLTFufyPQNiUQ2DKR/qzp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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# use the matplotlib magic to specify to display plots inline in the notebook\n",
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "\n",
- "# generate a pair of sinusoids\n",
- "x = np.linspace(0., 2. * np.pi, 100)\n",
- "y1 = np.sin(x)\n",
- "y2 = np.cos(x)\n",
- "\n",
- "# produce a new figure object with a defined (width, height) in inches\n",
- "fig = plt.figure(figsize=(8, 4))\n",
- "# add a single axis to the figure\n",
- "ax = fig.add_subplot(111)\n",
- "# plot the two sinusoidal traces on the axis, adjusting the line width\n",
- "# and adding LaTeX legend labels\n",
- "ax.plot(x, y1, linewidth=2, label=r'$\\sin(x)$')\n",
- "ax.plot(x, y2, linewidth=2, label=r'$\\cos(x)$')\n",
- "# set the axis labels\n",
- "ax.set_xlabel('$x$', fontsize=16)\n",
- "ax.set_ylabel('$y$', fontsize=16)\n",
- "# force the legend to be displayed\n",
- "ax.legend()\n",
- "# adjust the limits of the horizontal axis\n",
- "ax.set_xlim(0., 2. * np.pi)\n",
- "# make a grid be displayed in the axis background\n",
- "ax.grid('on')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "nbpresent": {
- "id": "533c10f0-95ba-4684-a72d-fd52cef0d007"
- }
- },
- "source": [
- "# Exercises\n",
- "\n",
- "Today's exercises are meant to allow you to get some initial familiarisation with the `mlp` package and how data is provided to the learning functions. Next week onwards, we will follow with the material covered in lectures. \n",
- "\n",
- "If you are new to Python and/or NumPy and are struggling to complete the exercises, you may find going through [this Stanford University tutorial](http://cs231n.github.io/python-numpy-tutorial/) by [Justin Johnson](http://cs.stanford.edu/people/jcjohns/) first helps. There is also a derived Jupyter notebook by [Volodymyr Kuleshov](http://web.stanford.edu/~kuleshov/) and [Isaac Caswell](https://symsys.stanford.edu/viewing/symsysaffiliate/21335) which you can download [from here](https://github.com/kuleshov/cs228-material/raw/master/tutorials/python/cs228-python-tutorial.ipynb) - if you save this in to your `mlpractical/notebooks` directory you should be able to open the notebook from the dashboard to run the examples.\n",
- "\n",
- "## Data providers\n",
- "\n",
- "Open (in the browser) the [`mlp.data_providers`](../../edit/mlp/data_providers.py) module. Have a look through the code and comments, then follow to the exercises.\n",
- "\n",
- "### Exercise 1 \n",
- "\n",
- "The `MNISTDataProvider` iterates over input images and target classes (digit IDs) from the [MNIST database of handwritten digit images](http://yann.lecun.com/exdb/mnist/), a common supervised learning benchmark task. Using the data provider and `matplotlib` we can for example iterate over the first couple of images in the dataset and display them using the following code:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "nbpresent": {
- "id": "978c1095-a9ce-4626-a113-e0be5fe51ecb"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJIAAACPCAYAAAARM4LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAABJhJREFUeJzt3b8vc28cxvH28UwMJonVYCAmET8WImYxCZGYkEgkgj/A\nImH1IxKiasJGYrAYxCQ2LAaxECo2BoPFd/mm6eeTtNXH1Zue835NvXLk9ESu3L17nx9Nfn5+JoDv\n+vPTB4BooEiQoEiQoEiQoEiQoEiQoEiQoEiQoEiQ+Bv4/VhGrzzJr/wRIxIkKBIkKBIkKBIkKBIk\nKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIkKBIk\nKBIkKBIkQt+yHQsfHx8mn5+fm3xycmJyOp02+fn52eTW1laT9/f3TW5sbPyn41RiRIIERYIERYJE\nMvAD2yP5WJtUKmXyxsaGyZeXlyb7/3kymSxp+9DQkMm7u7tfP9jS8VgbhEORIEGRIME6Uh7F1oJm\nZ2ezr6+ursy26upqk7u7u01eWloyuaOjw+TV1dW875VIJBKZTCbfYf8YRiRIUCRIUCRIsI6Uh5+n\nzM3NmZz7f/PnwtbX101ub28v6b2rqqpM9utIfg52d3dncl1dXUnvVwTrSAiHIkGCIkEitutIfp1o\namrK5O3tbZP9PGV4eDj7emtry2zzcxjv4eHBZL+OVGze2tbWZnJNTU3Bvw+BEQkSFAkSFAkSsV1H\nmpiYMHlnZ8fk/v5+k0dGRkweHBzMu+/393eTV1ZWTF5bWzP55eXF5GLXI93c3Jhc5mu2WUdCOBQJ\nEhQJErGZI83MzJjsz6XV19eb/PT0VHB/uetQ/lql3t5ek0u9JruhocHkvb09k0s9d/dNzJEQDkWC\nRGxOkVxfX5vsP07GxsZMPj4+Lri/+fn57Gt/u5Hft8+e3+6Ptdgpl9+AEQkSFAkSFAkSkZ0j+ctE\n3t7eTPZfwRcXFwtuL/QV3i8d+MfSeD09PSZvbm6aXAlzIo8RCRIUCRIUCRKRPUXy+vpqcnNzs8l+\nHlPsNIa/5ejg4CD7enp62mw7OjoquO/Al4F8F6dIEA5FggRFgkRk15Fqa2tNfnx8lO4/dw52cXFh\ntvn51fLyssm/fE70TxiRIEGRIEGRIBHZOVK59fX1ZV/724nGx8dNnpycDHJMP4kRCRIUCRIUCRKR\nPdf2Xf5cXVNTk8m55+oGBgbMtsPDw/IdWHica0M4FAkSFAkSrCPl4e9V82tFudcYLSwsBDmm34wR\nCRIUCRIUCRLMkf53e3trcrFH0+Tei9bS0lK+A6sQjEiQoEiQoEiQiO0cyV9nPTo6arKfE52enprs\nf1407hiRIEGRIEGRIBHbOVIqlTLZ/5ynf0Sx/2krWIxIkKBIkIjNR5v/KEun0yb7r/v+afuV+Di+\nkBiRIEGRIEGRIBHZ25Hu7+9N7uzsNDmTyZh8dnZmMqdAsrgdCeFQJEhQJEhEdh3J33Ltbyfyv7Ld\n1dVV9mOKMkYkSFAkSFAkSER2HQkyrCMhHIoECYoEidDrSF/6vEXlYUSCBEWCBEWCBEWCBEWCBEWC\nBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCBEWCxH/yKxa+\nn2pIxAAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Image target: [[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n"
- ]
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJIAAACPCAYAAAARM4LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Image target: [[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]\n"
- ]
- }
- ],
- "source": [
- "%matplotlib inline\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import mlp.data_providers as data_providers\n",
- "\n",
- "def show_single_image(img, fig_size=(2, 2)):\n",
- " fig = plt.figure(figsize=fig_size)\n",
- " ax = fig.add_subplot(111)\n",
- " ax.imshow(img, cmap='Greys')\n",
- " ax.axis('off')\n",
- " plt.show()\n",
- " return fig, ax\n",
- "\n",
- "# An example for a single MNIST image\n",
- "mnist_dp = data_providers.MNISTDataProvider(\n",
- " which_set='valid', batch_size=1, max_num_batches=2, shuffle_order=True)\n",
- "\n",
- "for inputs, target in mnist_dp:\n",
- " show_single_image(inputs.reshape((28, 28)))\n",
- " print('Image target: {0}'.format(target))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generally we will want to deal with batches of multiple images i.e. `batch_size > 1`. As a first task:\n",
- "\n",
- " * Using MNISTDataProvider, write code that iterates over the first 5 minibatches of size 100 data-points. \n",
- " * Display each batch of MNIST digits in a $10\\times10$ grid of images. \n",
- " \n",
- "**Notes**:\n",
- "\n",
- " * Images are returned from the provider as tuples of numpy arrays `(inputs, targets)`. The `inputs` matrix has shape `(batch_size, input_dim)` while the `targets` array is of shape `(batch_size,)`, where `batch_size` is the number of data points in a single batch and `input_dim` is dimensionality of the input features. \n",
- " * Each input data-point (image) is stored as a 784 dimensional vector of pixel intensities normalised to $[0, 1]$ from inital integer values in $[0, 255]$. However, the original spatial domain is two dimensional, so before plotting you will need to reshape the one dimensional input arrays in to two dimensional arrays 2D (MNIST images have the same height and width dimensions)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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ZF41tDuvr69G7d2+89dZb1oq0bFEBjUOnQ4cOoaKiQnVPtWDBAhPjU5GLbg4h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\nDj4+Pk26oFRWVsLFxcVE8OY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- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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ys7MBtF54MfOFJcPNFStWoKCgQBLR9u3bpXUauRfeGHv37uVamzlx4gRmzJhh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\nPQsG3e2tt96CVqtFbm6u1f4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- "text/plain": [
- ""
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- "output_type": "display_data"
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- {
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gQHFxMWpqarBixQr4+vraNAoWoaKiQv7ugIAAtGvXDu3atcOyZcsceUG0HlHN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\nphBvb2/h469cuUIIIWTq1Knk+++/J/n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- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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ZsiXc3d2tJtBriLi4OMTFxYlsLTSP1b99+/bB19cXvr6+6o0/duyYrS9vJiRu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\nwz/GmE3D2qioKHTr1s1aV47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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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02bls2TLF9dhLVlYW2rdvb7CaKGcpe+7cuSYbv61BYGCgyaoob2zFxsZGJCcn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\nxNOvXz+kp6ejtLQUBQUFiI6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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "def show_batch_of_images(img_batch, fig_size=(3, 3)):\n",
- " fig = plt.figure(figsize=fig_size)\n",
- " batch_size, im_height, im_width = img_batch.shape\n",
- " # calculate no. columns per grid row to give square grid\n",
- " grid_size = int(batch_size**0.5)\n",
- " # intialise empty array to tile image grid into\n",
- " tiled = np.empty((im_height * grid_size, \n",
- " im_width * batch_size // grid_size))\n",
- " # iterate over images in batch + indexes within batch\n",
- " for i, img in enumerate(img_batch):\n",
- " # calculate grid row and column indices\n",
- " r, c = i % grid_size, i // grid_size\n",
- " tiled[r * im_height:(r + 1) * im_height, \n",
- " c * im_height:(c + 1) * im_height] = img\n",
- " ax = fig.add_subplot(111)\n",
- " ax.imshow(tiled, cmap='Greys')\n",
- " ax.axis('off')\n",
- " fig.tight_layout()\n",
- " plt.show()\n",
- " return fig, ax\n",
- "\n",
- "batch_size = 100\n",
- "num_batches = 5\n",
- "\n",
- "mnist_dp = data_providers.MNISTDataProvider(\n",
- " which_set='valid', batch_size=batch_size, \n",
- " max_num_batches=num_batches, shuffle_order=True)\n",
- "\n",
- "for inputs, target in mnist_dp:\n",
- " # reshape inputs from batch of vectors to batch of 2D arrays (images)\n",
- " _ = show_batch_of_images(inputs.reshape((batch_size, 28, 28)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "nbpresent": {
- "id": "d2d525de-5d5b-41d5-b2fb-a83874dba986"
- }
- },
- "source": [
- "### Exercise 2\n",
- "\n",
- "`MNISTDataProvider` as `targets` currently returns a vector of integers, each element in this vector represents an the integer ID of the class the corresponding data-point represents. \n",
- "\n",
- "For training of neural networks a 1-of-K representation of multi-class targets is more useful. Instead of representing class identity by an integer ID, for each data point a vector of length equal to the number of classes is created, will all elements zero except for the element corresponding to the class ID. \n",
- "\n",
- "For instance, given a batch of 5 integer targets `[2, 2, 0, 1, 0]` and assuming there are 3 different classes \n",
- "the corresponding 1-of-K encoded targets would be\n",
- "```\n",
- "[[0, 0, 1],\n",
- " [0, 0, 1],\n",
- " [1, 0, 0],\n",
- " [0, 1, 0],\n",
- " [1, 0, 0]]\n",
- "```\n",
- "\n",
- " * Implement the `to_one_of_k` method of `MNISTDataProvider` class. \n",
- " * Uncomment the overloaded `next` method, so the raw targets are converted to 1-of-K coding. \n",
- " * Test your code by running the the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
- " [ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
- " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
- " [ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n",
- "[[ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
- " [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n",
- "[[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
- " [ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
- " [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n",
- "[[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
- " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
- " [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
- " [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]\n",
- "[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
- " [ 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
- " [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
- " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
- " [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
- ]
- }
- ],
- "source": [
- "mnist_dp = data_providers.MNISTDataProvider(\n",
- " which_set='valid', batch_size=5, max_num_batches=5, shuffle_order=False)\n",
- "\n",
- "for inputs, targets in mnist_dp:\n",
- " assert np.all(targets.sum(-1) == 1.)\n",
- " assert np.all(targets >= 0.)\n",
- " assert np.all(targets <= 1.)\n",
- " print(targets)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true,
- "nbpresent": {
- "id": "471093b7-4b94-4295-823a-5285c79d3119"
- }
- },
- "source": [
- "### Exercise 3\n",
- "\n",
- "Here you will write your own data provider `MetOfficeDataProvider` that wraps [weather data for south Scotland](http://www.metoffice.gov.uk/hadobs/hadukp/data/daily/HadSSP_daily_qc.txt). A previous version of this data has been stored in `data` directory for your convenience and skeleton code for the class provided in `mlp/data_providers.py`.\n",
- "\n",
- "The data is organised in the text file as a table, with the first two columns indexing the year and month of the readings and the following 31 columns giving daily precipitation values for the corresponding month. As not all months have 31 days some of entries correspond to non-existing days. These values are indicated by a non-physical value of `-99.9`.\n",
- "\n",
- " * You should read all of the data from the file ([`np.loadtxt`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html) may be useful for this) and then filter out the `-99.9` values and collapse the table to a one-dimensional array corresponding to a sequence of daily measurements for the whole period data is available for. [NumPy's boolean indexing feature](http://docs.scipy.org/doc/numpy/user/basics.indexing.html#boolean-or-mask-index-arrays) could be helpful here.\n",
- " * A common initial preprocessing step in machine learning tasks is to normalise data so that it has zero mean and a standard deviation of one. Normalise the data sequence so that its overall mean is zero and standard deviation one.\n",
- " * Each data point in the data provider should correspond to a window of length specified in the `__init__` method as `window_size` of this contiguous data sequence, with the model inputs being the first `window_size - 1` elements of the window and the target output being the last element of the window. For example if the original data sequence was `[1, 2, 3, 4, 5, 6]` and `window_size=3` then `input, target` pairs iterated over by the data provider should be\n",
- " ```\n",
- " [1, 2], 3\n",
- " [4, 5], 6\n",
- " ```\n",
- " * **Extension**: Have the data provider instead overlapping windows of the sequence so that more training data instances are produced. For example for the sequence `[1, 2, 3, 4, 5, 6]` the corresponding `input, target` pairs would be\n",
- "\n",
- "```\n",
- "[1, 2], 3\n",
- "[2, 3], 4\n",
- "[3, 4], 5\n",
- "[4, 5], 6\n",
- "```\n",
- " * Test your code by running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "nbpresent": {
- "id": "c8553a56-9f25-4198-8a1a-d7e9572b4382"
- }
- },
- "outputs": [
- {
- "data": {
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u5zrycvM4c+CZ0Q7NmHYL5+ypXwIHgSU4X8KnAcNU9d/DEWi4hWVMY3SGJQwT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\nYOMCXt31KvESz21jbmPWxFk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JyOxyvrbptsQmc2cRPaI6NryyR9T49uPJycvho50fmRPU2UQ/BW7esKI8fTCq\nj5HdGhFSw4Mpv+ynqi2BoJnHloZwbxH5h4jMUkplASEiMqQ8B1VKnVdK9VFKNbNexkqyPr9ZKTWq\niP0/VUo9Xp5jVoajiak8+sUWbp+xntikdF69rTVLJnUvdtW8ML8wbo+4ne8Pfs+Ji3r6h1L5BkPX\n8bDvJzjleKP+HY2XuwsT+zZjS2wyy/cVdQVY066dLZenPgGyMNoYAE4D/7FbIieUeCmLf/ywm36F\n5oiypUfUo20fxc3Fjfe3Of3YxcpxwzjwDoJlL4H+67lUd0U1oFGQD28s2U9evn6/tPKzpWg0UUq9\ngdEAjlIqHaPrbbVXsEfUVxtPMPz6MFY/24sJfZrZ3CMqyCuI+1vczy/Hf2Hv+b12TlwFeNSAHs/C\n8bVweLnZaRyem4uFp/pHcDA+lQXbdM8zrfxsKRrZIuKFtVusiDTBOPOoti73iOr51pU9oso6R9RD\n1z1ETY+aTNs6zQ5pq6COD0HNhsbZRn6+2Wkc3qDr6tC6nj/vLD1IZk5e6d+gaSWwpWi8iDEiu4GI\nzMGY9uNZu6ZyUEopYvbEMcDaIyq81pU9osqqhnsNRrUexfoz69lwdkMFJq6iXN2h998hfpexNKxW\nIotFmDwwktMXMvjyj1iz42hOrsSp0cVova0PpANdMC5L/aGUOlc58a6dvaZG33oimdd+3sem48k0\nCfZh8sDIYhu4yyIrL4shC4YQ5BnE3MFzK+x1q6z8fPgoGrJTYdwmo5BoJbp/9gb2nElh9bO98PPU\nU81rV6qQqdGto7V/tvZ2WqyUWuTIBcMejiam8tiXWxj2wXqOn/+rR1T/VrUr9IPdw8WDsW3Hsvv8\nbpbGLq2w162yLBbo8yIkH4etn5mdxilMHhhJcnoOs9YcNTuK5sRsuTy1VUQ62T2JgynYI2rNwWvr\nEVVWtzS5hSb+TXhv23vk5ufa5RhVSrN+0LAbrH4dslLNTuPwWtf3Z3CbOsxee4zES9W6WVIrB1s+\n/a4HfheRIyKyU0R2ichOewczw5bYZKYuPcjk73b+2SPqvs5hrHrm2npElZWLxYUJHSZw/OJxFhxe\nYNdjVQki0PdlSEuEPz4wO41TeLp/c7Lz8nlvxSGzo2hOypZPwQF2T+EANh47z/DZG8jJM9p4ujQO\n5NXbWpergbssejXoRbvgdny4/UOGNB6Cl6tXpR7f6TToBJFD4Ld3IWok+ASZncihNQry4e5ODZi7\n4QQP39iF+2TDAAAgAElEQVSIhrWq8SJfWpnYMo1IbFFbZYSrTMv2JvxZMCwC0c2CK71ggLFs56SO\nk0jISGDuvrmVfnyn1OefkJMGa982O4lTmNinGa4uwtsxeg4v7drpBRysBlxXGw9XCy4C7q4WujSu\nZVqWjqEd6V6/Ox/v/piUrBTTcjiN4ObQ7j7YNBsu6OlYShPq58nIbo1YuOMMu0/r3y/t2uiiYdWx\nYQBzR3fhyf7NmTOqy1WTC1a2Ce0nkJqdyse7PzY1h9Po+TwgsPI1s5M4hUd6NMHfy403luip07Vr\no4tGAR0bBjCuV1PTCwZA88DmDG48mLn75hKXFmd2HMfnXx86j4YdX0G8no6lNP5ebozr1YQ1BxNZ\nf6Ra9aLXyqnYoiEil0TkYnFbZYasrsa1G0eeyuPDHR+aHcU5RD9lzE21XE+dbosRN4RTx9+T1389\noKdO12xWbNFQStVQSvkB04DngHoYo8MnA1MrJ171Vr9Gfe5ufjcLDi/gaIoekFUq70DoNhEO/gKx\nv5udxuF5urnwRN8Idpy8wJI9+mxWs40tl6duUUp9oJS6pJS6qJSaAQwt9bu0CjG69Wg8XTz11Om2\n6vIY+IbqqdNtNKxDPZqG+PLGkgPk5unJH7XS2VI00kRkuIi4iIhFRIYDafYOphlqedXiwVYPsjR2\nKbsSd5kdx/G5+0CPyXDyDzj4q9lpHJ6ri4Wn+zfnaGIa3205ZXYczQnYUjTuA+4C4q3bndbntEoy\notUIAj0Dmbp1qr72bIsOIyCwidG2ka+nAi/NgFahtA+rydRlh/TU6VqpbBncd1wpNVQpFaSUClZK\n3aqUOl4J2TQrHzcfxrQZw8a4jaw/s97sOI7Pxc2YOj1hL+z8xuw0Dk/EmDo97mImn64/bnYczcHZ\nskZ4hIgsF5Hd1sdtROTv9o+mFXRnxJ3U863H1K1TyVf62nOpWt4KddrBylchV0/OV5oujWvRs3kw\nH6w8TEp6jtlxNAdmy+WpWcDz/LXc607gHnuG0q7m7uLOuHbj2J+0n1+P6Wv1pbJYoO+LkHICNukB\nkrZ4dkAkl7JymbH6iNlRNAdmS9HwVkptLPScnrfbBIMbDyYiIIL3tr1HTp7+a7BUTXpDox6w9i3I\n1EOLStOyrh9D29blk9+OEZeSaXYczUHZUjTOWdcFv7xG+B3AWbum0opkEQsTO0zkVOopvj/0vdlx\nnEPflyD9PKx/z+wkTuGp/s3JV4ppy/XU6VrRbCka44CPgEgROQ1MAh4rz0FFJFBElorIIettkfN2\niEieiGy3bgvLc8yqIrpeNB1DO/Lhjg9Jz0k3O47jq9fBaN/4fTqkJpidxuE1CPRm+PUN+WbzSY4k\n6oWttKvZ0nvqqFKqLxAMRCqlbqyA3lPPAcuVUs2A5dbHRclQSrWzbreU85hVgojwRMcnOJ95ni/2\nfmF2HOfQ+x+Qmwlr3jQ7iVMY16spHq4W3o7RkxlqV7Ol99REEfED0oF3RGSriPQv53GHApcXdv4M\nuLWcr1ettA1uS+8GvflkzyckZyabHcfxBTU1xm5s/gSSjpmdxuEF1/BgVHRjft4Vx46TF8yOozkY\nWy5PjVRKXQT6A7WAvwFTynncUKXU5XaROCC0mP08RWSziPwhIrqwFDChwwQycjOYtWuW2VGcQ4/J\nYHGFla+YncQpjI5uRKCPO6//ul8PKNWuYEvREOvtIOBzpdSeAs8V/00iy0RkdxHbFfNWKeM3srjf\nyoZKqSiMEehTrQ3yRR1rjLW4bE5MTLThR3J+TWo24ZYmtzBv/zzOpJ4xO47j86sDXR6FXd/C2Sq5\nxH2FquHpxuO9mrL+yHnWHtJTp2t/saVobBGRGIyisUREagClji5TSvVVSl1XxPYjEC8idQCst0W2\nUCqlTltvjwKrgPbF7DdTKRWllIoKDg624UeqGsa1G4cgTN8+3ewozqHbJPCsCctfNjuJUxjeJYz6\nAV68sWQ/+fn6bEMz2FI0HsZoqO6klEoH3IGHynnchcAD1vsPAD8W3kFEAkTEw3o/COgG6NV1Cqjt\nU5t7I+/lpyM/cShZd5EslVdNiH4SDi+DY2vNTuPwPFxdeLJfBLtPX2TxLt3LXjOUtAhTpPVuO+tt\nYxHpADQEXMt53ClAPxE5BPS1PkZEokRktnWfFsBmEdkBrASmKKV00ShkVOtR+Lj58O62d82O4hw6\njwG/erDsRT11ug2GtqtHZO0avB1zgBw9dbpGyWcaT1lv3y5ie6s8B1VKnVdK9VFKNbNexkqyPr9Z\nKTXKen+9Uqq1Uqqt9VbPBVGEmp41GXndSFadXMW2hG1mx3F8bl7Q8zk4vQX2LzI7jcNzsQjPDGjO\n8fPpzNt00uw4mgMoaeW+0dbbXkVsvSsvolaa4S2GE+QVxNQteup0m7S9D4IijKnT8/SMOKXpHRlC\np/AA3l1+iPRs/X5VdyVdnhpW0laZIbWSebt582ibR9masJU1p9aYHcfxubhCn3/CuYOwY67ZaRye\niPDcTZEkXsrif+v0OJfqrqTLUzeXsA2xfzTtWgyLGEZYjTCmbp1Knl54qHSRQ6BeFKx8DXIyzE7j\n8Do2DKRvi1A+Wn2U5LRss+NoJirp8tRDJWwjKzOkVjo3ixvj24/n8IXDLD622Ow4jk/EmMzw0hnY\nONPsNE7h2YHNScvOZfrKw2ZH0UxkS5dbRGSwiDwrIv+8vNk7mHbt+of3p0VgC6Zvm052nv5rsFSN\noqFpX1j7X8jQ02WUJiK0BsM61OfzP2I5fUGfnTmSOXPmEB4ejsViITw8nDlz5tjtWLbMPfUhcDcw\nHmMk+J0Y3W41B2MRC5M6TuJM2hm+OaCXObVJnxch8wL8NtXsJE7hiX4RAExdetDkJNplc+bMYcyY\nMcTGxqKUIjY2ljFjxtitcNhyptFVKTUCSFZKvQzcAETYJY1Wbl3rduX6Otczc+dMUrP11NalqtMG\nWt8Jf3wIF/UAttLUq+nFiC4N+X7rKQ7FXzI7jga88MILpKdfuUxCeno6L7zwgl2OZ0vRuHwemi4i\ndTGWfa1jlzRahXiiwxMkZyXz2d7PSt9Zg14vQH4urH7d7CROYWyvpvi4u/LGEj11uiM4ceLENT1f\nXrYUjUUiUhN4E9gKHAe+sksarUK0CmpF/4b9+WzPZ5zL0JPNlSqwEUQ9BFs/h3O6kbc0gT7ujOne\nmKV749kSm2R2nGpLKcWcfXNwC3Qr8uthYWF2Oa4tizD9Wyl1QSn1PUZbRqRS6h92SaNVmPHtx5Od\nl83MnbpnkE26PwOunrDi32YncQoPRzciyNeD1385oAeUmiAhPYHHlj3GlI1TiB4djZeX1xVf9/b2\n5pVX7LMMgC0N4S4icouITMBY+vVhEXnSLmm0ChPuH85tzW7j24PfcvKSnv6hVL4hcMM42PsDnN5q\ndhqH5+3uysQ+Tdl4PImVB/QyupVpWewyhi0cxpb4Lfyjyz9Y+upSZs2aRcOGDRERGjZsyMyZMxk+\nfLhdji+l/ZUgIj8DmcAuCkyJbm0UdzhRUVFq8+bNZsdwCAnpCQyeP5jeYb15vbu+Xl+qzIvwbjsI\nvQ4e0EvSlyYnL5++/12Nl5s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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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KgMOUBhjbb9NL00kvSbe/U8Zy8O0JUaMdI4SnHwyZDfsXQ7X9prJVaQUk9Q6g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\nCzqXAAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "batch_size = 3\n",
- "for window_size in [2, 5, 10]:\n",
- " met_dp = data_providers.MetOfficeDataProvider(\n",
- " window_size=window_size, batch_size=batch_size,\n",
- " max_num_batches=1, shuffle_order=False)\n",
- " fig = plt.figure(figsize=(6, 3))\n",
- " ax = fig.add_subplot(111)\n",
- " ax.set_title('Window size {0}'.format(window_size))\n",
- " ax.set_xlabel('Day in window')\n",
- " ax.set_ylabel('Normalised reading')\n",
- " # iterate over data provider batches checking size and plotting\n",
- " for inputs, targets in met_dp:\n",
- " assert inputs.shape == (batch_size, window_size - 1)\n",
- " assert targets.shape == (batch_size, )\n",
- " ax.plot(np.c_[inputs, targets].T, '.-')\n",
- " ax.plot([window_size - 1] * batch_size, targets, 'ko')"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/02_Single_layer_models.ipynb b/notebooks/02_Single_layer_models.ipynb
deleted file mode 100644
index 53d6f88..0000000
--- a/notebooks/02_Single_layer_models.ipynb
+++ /dev/null
@@ -1,3305 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Single layer models\n",
- "\n",
- "In this lab we will implement a single-layer network model consisting of solely of an affine transformation of the inputs. The relevant material for this was covered in [the slides of the first lecture](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp01-intro.pdf). \n",
- "\n",
- "We will first implement the forward propagation of inputs to the network to produce predicted outputs. We will then move on to considering how to use gradients of an error function evaluated on the outputs to compute the gradients with respect to the model parameters to allow us to perform an iterative gradient-descent training procedure. In the final exercise you will use an interactive visualisation to explore the role of some of the different hyperparameters of gradient-descent based training methods.\n",
- "\n",
- "#### A note on random number generators\n",
- "\n",
- "It is generally a good practice (for machine learning applications **not** for cryptography!) to seed a pseudo-random number generator once at the beginning of each experiment. This makes it easier to reproduce results as the same random draws will produced each time the experiment is run (e.g. the same random initialisations used for parameters). Therefore generally when we need to generate random values during this course, we will create a seeded random number generator object as we do in the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "seed = 27092016 \n",
- "rng = np.random.RandomState(seed)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 1: linear and affine transforms\n",
- "\n",
- "Any *linear transform* (also called a linear map) of a finite-dimensional vector space can be parametrised by a matrix. So for example if we consider $\\boldsymbol{x} \\in \\mathbb{R}^{D}$ as the input space of a model with $D$ dimensional real-valued inputs, then a matrix $\\mathbf{W} \\in \\mathbb{R}^{K\\times D}$ can be used to define a prediction model consisting solely of a linear transform of the inputs\n",
- "\n",
- "\\begin{equation}\n",
- " \\boldsymbol{y} = \\mathbf{W} \\boldsymbol{x}\n",
- " \\qquad\n",
- " \\Leftrightarrow\n",
- " \\qquad\n",
- " y_k = \\sum_{d=1}^D \\left( W_{kd} x_d \\right) \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "with here $\\boldsymbol{y} \\in \\mathbb{R}^K$ the $K$-dimensional real-valued output of the model. Geometrically we can think of a linear transform doing some combination of rotation, scaling, reflection and shearing of the input.\n",
- "\n",
- "An *affine transform* consists of a linear transform plus an additional translation parameterised by a vector $\\boldsymbol{b} \\in \\mathbb{R}^K$. A model consisting of an affine transformation of the inputs can then be defined as\n",
- "\n",
- "\\begin{equation}\n",
- " \\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}\n",
- " \\qquad\n",
- " \\Leftrightarrow\n",
- " \\qquad\n",
- " y_k = \\sum_{d=1}^D \\left( W_{kd} x_d \\right) + b_k \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "In machine learning we will usually refer to the matrix $\\mathbf{W}$ as a *weight matrix* and the vector $\\boldsymbol{b}$ as a *bias vector*.\n",
- "\n",
- "Generally rather than working with a single data vector $\\boldsymbol{x}$ we will work with batches of datapoints $\\left\\lbrace \\boldsymbol{x}^{(b)}\\right\\rbrace_{b=1}^B$. We could calculate the outputs for each input in the batch sequentially\n",
- "\n",
- "\\begin{align}\n",
- " \\boldsymbol{y}^{(1)} &= \\mathbf{W}\\boldsymbol{x}^{(1)} + \\boldsymbol{b}\\\\\n",
- " \\boldsymbol{y}^{(2)} &= \\mathbf{W}\\boldsymbol{x}^{(2)} + \\boldsymbol{b}\\\\\n",
- " \\dots &\\\\\n",
- " \\boldsymbol{y}^{(B)} &= \\mathbf{W}\\boldsymbol{x}^{(B)} + \\boldsymbol{b}\\\\\n",
- "\\end{align}\n",
- "\n",
- "by looping over each input in the batch and calculating the output. However in general loops in Python are slow (particularly compared to compiled and typed languages such as C). This is due at least in part to the large overhead in dynamically inferring variable types. In general therefore wherever possible we want to avoid having loops in which such overhead will become the dominant computational cost.\n",
- "\n",
- "For array based numerical operations, one way of overcoming this bottleneck is to *vectorise* operations. NumPy `ndarrays` are typed arrays for which operations such as basic elementwise arithmetic and linear algebra operations such as computing matrix-matrix or matrix-vector products are implemented by calls to highly-optimised compiled libraries. Therefore if you can implement code directly using NumPy operations on arrays rather than by looping over array elements it is often possible to make very substantial performance gains.\n",
- "\n",
- "As a simple example we can consider adding up two arrays `a` and `b` and writing the result to a third array `c`. First lets initialise `a` and `b` with arbitrary values by running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "size = 1000\n",
- "a = np.arange(size)\n",
- "b = np.ones(size)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's time how long it takes to add up each pair of values in the two array and write the results to a third array using a loop-based implementation. We will use the `%%timeit` magic briefly mentioned in the previous lab notebook specifying the number of times to loop the code as 100 and to give the best of 3 repeats. Run the cell below to get a print out of the average time taken."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2.05 ms ± 148 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n"
- ]
- }
- ],
- "source": [
- "%%timeit -n 100 -r 3\n",
- "c = np.empty(size)\n",
- "for i in range(size):\n",
- " c[i] = a[i] + b[i]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And now we will perform the corresponding summation with the overloaded addition operator of NumPy arrays. Again run the cell below to get a print out of the average time taken."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3.01 µs ± 1.53 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n"
- ]
- }
- ],
- "source": [
- "%%timeit -n 100 -r 3\n",
- "c = a + b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The first loop-based implementation should have taken on the order of milliseconds ($10^{-3}$s) while the vectorised implementation should have taken on the order of microseconds ($10^{-6}$s), i.e. a $\\sim1000\\times$ speedup. Hopefully this simple example should make it clear why we want to vectorise operations whenever possible!\n",
- "\n",
- "Getting back to our affine model, ideally rather than individually computing the output corresponding to each input we should compute the outputs for all inputs in a batch using a vectorised implementation. As you saw last week, data providers return batches of inputs as arrays of shape `(batch_size, input_dim)`. In the mathematical notation used earlier we can consider this as a matrix $\\mathbf{X}$ of dimensionality $B \\times D$, and in particular\n",
- "\n",
- "\\begin{equation}\n",
- " \\mathbf{X} = \\left[ \\boldsymbol{x}^{(1)} ~ \\boldsymbol{x}^{(2)} ~ \\dots ~ \\boldsymbol{x}^{(B)} \\right]^\\mathrm{T}\n",
- "\\end{equation}\n",
- "\n",
- "i.e. the $b^{\\textrm{th}}$ input vector $\\boldsymbol{x}^{(b)}$ corresponds to the $b^{\\textrm{th}}$ row of $\\mathbf{X}$. If we define the $B \\times K$ matrix of outputs $\\mathbf{Y}$ similarly as\n",
- "\n",
- "\\begin{equation}\n",
- " \\mathbf{Y} = \\left[ \\boldsymbol{y}^{(1)} ~ \\boldsymbol{y}^{(2)} ~ \\dots ~ \\boldsymbol{y}^{(B)} \\right]^\\mathrm{T}\n",
- "\\end{equation}\n",
- "\n",
- "then we can express the relationship between $\\mathbf{X}$ and $\\mathbf{Y}$ using [matrix multiplication](https://en.wikipedia.org/wiki/Matrix_multiplication) and addition as\n",
- "\n",
- "\\begin{equation}\n",
- " \\mathbf{Y} = \\mathbf{X} \\mathbf{W}^\\mathrm{T} + \\mathbf{B}\n",
- "\\end{equation}\n",
- "\n",
- "where $\\mathbf{B} = \\left[ \\boldsymbol{b} ~ \\boldsymbol{b} ~ \\dots ~ \\boldsymbol{b} \\right]^\\mathrm{T}$ i.e. a $B \\times K$ matrix with each row corresponding to the bias vector. The weight matrix needs to be transposed here as the inner dimensions of a matrix multiplication must match i.e. for $\\mathbf{C} = \\mathbf{A} \\mathbf{B}$ then if $\\mathbf{A}$ is of dimensionality $K \\times L$ and $\\mathbf{B}$ is of dimensionality $M \\times N$ then it must be the case that $L = M$ and $\\mathbf{C}$ will be of dimensionality $K \\times N$.\n",
- "\n",
- "The first exercise for this lab is to implement *forward propagation* for a single-layer model consisting of an affine transformation of the inputs in the `fprop` function given as skeleton code in the cell below. This should work for a batch of inputs of shape `(batch_size, input_dim)` producing a batch of outputs of shape `(batch_size, output_dim)`.\n",
- " \n",
- "You will probably want to use the NumPy `dot` function and [broadcasting features](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) to implement this efficiently. If you are not familiar with either / both of these you may wish to read the [hints](#Hints:-Using-the-dot-function-and-broadcasting) section below which gives some details on these before attempting the exercise."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "def fprop(inputs, weights, biases):\n",
- " \"\"\"Forward propagates activations through the layer transformation.\n",
- "\n",
- " For inputs `x`, outputs `y`, weights `W` and biases `b` the layer\n",
- " corresponds to `y = W x + b`.\n",
- "\n",
- " Args:\n",
- " inputs: Array of layer inputs of shape (batch_size, input_dim).\n",
- " weights: Array of weight parameters of shape \n",
- " (output_dim, input_dim).\n",
- " biases: Array of bias parameters of shape (output_dim, ).\n",
- "\n",
- " Returns:\n",
- " outputs: Array of layer outputs of shape (batch_size, output_dim).\n",
- " \"\"\"\n",
- " return inputs.dot(weights.T) + biases"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once you have implemented `fprop` in the cell above you can test your implementation by running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "All outputs correct!\n"
- ]
- }
- ],
- "source": [
- "inputs = np.array([[0., -1., 2.], [-6., 3., 1.]])\n",
- "weights = np.array([[2., -3., -1.], [-5., 7., 2.]])\n",
- "biases = np.array([5., -3.])\n",
- "true_outputs = np.array([[6., -6.], [-17., 50.]])\n",
- "\n",
- "if not np.allclose(fprop(inputs, weights, biases), true_outputs):\n",
- " print('Wrong outputs computed.')\n",
- "else:\n",
- " print('All outputs correct!')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Hints: Using the `dot` function and broadcasting\n",
- "\n",
- "For those new to NumPy below are some details on the `dot` function and broadcasting feature of NumPy that you may want to use for implementing the first exercise. If you are already familiar with these and have already completed the first exercise you can move on straight to [second exercise](#Exercise-2:-visualising-random-models).\n",
- "\n",
- "#### `numpy.dot` function\n",
- "\n",
- "Matrix-matrix, matrix-vector and vector-vector (dot) products can all be computed in NumPy using the [`dot`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) function. For example if `A` and `B` are both two dimensional arrays, then `C = np.dot(A, B)` or equivalently `C = A.dot(B)` will both compute the matrix product of `A` and `B` assuming `A` and `B` have compatible dimensions. Similarly if `a` and `b` are one dimensional arrays then `c = np.dot(a, b)` (which is equivalent to `c = a.dot(b)`) will compute the [scalar / dot product](https://en.wikipedia.org/wiki/Dot_product) of the two arrays. If `A` is a two-dimensional array and `b` a one-dimensional array `np.dot(A, b)` (which is equivalent to `A.dot(b)`) will compute the matrix-vector product of `A` and `b`. Examples of all three of these product types are shown in the cell below:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 6. 6. 6.]\n",
- " [ 24. 24. 24.]\n",
- " [ 42. 42. 42.]]\n",
- "[[ 18. 24. 30.]\n",
- " [ 18. 24. 30.]\n",
- " [ 18. 24. 30.]]\n",
- "[ 0.8 2.6 4.4]\n",
- "[ 2.4 3. 3.6]\n",
- "0.2\n"
- ]
- }
- ],
- "source": [
- "# Initiliase arrays with arbitrary values\n",
- "A = np.arange(9).reshape((3, 3))\n",
- "B = np.ones((3, 3)) * 2\n",
- "a = np.array([-1., 0., 1.])\n",
- "b = np.array([0.1, 0.2, 0.3])\n",
- "print(A.dot(B)) # Matrix-matrix product\n",
- "print(B.dot(A)) # Reversed product of above A.dot(B) != B.dot(A) in general\n",
- "print(A.dot(b)) # Matrix-vector product\n",
- "print(b.dot(A)) # Again A.dot(b) != b.dot(A) unless A is symmetric i.e. A == A.T\n",
- "print(a.dot(b)) # Vector-vector scalar product"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Broadcasting\n",
- "\n",
- "Another NumPy feature it will be helpful to get familiar with is [broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). Broadcasting allows you to apply operations to arrays of different shapes, for example to add a one-dimensional array to a two-dimensional array or multiply a multidimensional array by a scalar. The complete set of rules for broadcasting as explained in the official documentation page just linked to can sound a bit complex: you might find the [visual explanation on this page](http://www.scipy-lectures.org/intro/numpy/operations.html#broadcasting) more intuitive. The cell below gives a few examples:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 0.1 1.2]\n",
- " [ 2.1 3.2]\n",
- " [ 4.1 5.2]]\n",
- "[[-1. 0.]\n",
- " [ 2. 3.]\n",
- " [ 5. 6.]]\n",
- "[[ 0. 0.2]\n",
- " [ 0.2 0.6]\n",
- " [ 0.4 1. ]]\n"
- ]
- }
- ],
- "source": [
- "# Initiliase arrays with arbitrary values\n",
- "A = np.arange(6).reshape((3, 2))\n",
- "b = np.array([0.1, 0.2])\n",
- "c = np.array([-1., 0., 1.])\n",
- "print(A + b) # Add b elementwise to all rows of A\n",
- "print((A.T + c).T) # Add b elementwise to all columns of A\n",
- "print(A * b) # Multiply each row of A elementise by b "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 2: visualising random models"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this exercise you will use your `fprop` implementation to visualise the outputs of a single-layer affine transform model with two-dimensional inputs and a one-dimensional output. In this simple case we can visualise the joint input-output space on a 3D axis.\n",
- "\n",
- "For this task and the learning experiments later in the notebook we will use a regression dataset from the [UCI machine learning repository](http://archive.ics.uci.edu/ml/index.html). In particular we will use a version of the [Combined Cycle Power Plant dataset](http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant), where the task is to predict the energy output of a power plant given observations of the local ambient conditions (e.g. temperature, pressure and humidity).\n",
- "\n",
- "The original dataset has four input dimensions and a single target output dimension. We have preprocessed the dataset by [whitening](https://en.wikipedia.org/wiki/Whitening_transformation) it, a common preprocessing step. We will only use the first two dimensions of the whitened inputs (corresponding to the first two principal components of the inputs) so we can easily visualise the joint input-output space.\n",
- "\n",
- "The dataset has been wrapped in the `CCPPDataProvider` class in the `mlp.data_providers` module and the data included as a compressed file in the data directory as `ccpp_data.npz`. Running the cell below will initialise an instance of this class, get a single batch of inputs and outputs and import the necessary `matplotlib` objects."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "from mpl_toolkits.mplot3d import Axes3D\n",
- "from mlp.data_providers import CCPPDataProvider\n",
- "%matplotlib notebook\n",
- "\n",
- "data_provider = CCPPDataProvider(\n",
- " which_set='train',\n",
- " input_dims=[0, 1],\n",
- " batch_size=5000, \n",
- " max_num_batches=1, \n",
- " shuffle_order=False\n",
- ")\n",
- "\n",
- "input_dim, output_dim = 2, 1\n",
- "\n",
- "inputs, targets = data_provider.next()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here we used the `%matplotlib notebook` magic command rather than the `%matplotlib inline` we used in the previous lab as this allows us to produce interactive 3D plots which you can rotate and zoom in/out by dragging with the mouse and scrolling the mouse-wheel respectively. Once you have finished interacting with a plot you can close it to produce a static inline plot using the button in the top-right corner.\n",
- "\n",
- "Now run the cell below to plot the predicted outputs of a randomly initialised model across the two dimensional input space as well as the true target outputs. This sort of visualisation can be a useful method (in low dimensions) to assess how well the model is likely to be able to fit the data and to judge appropriate initialisation scales for the parameters. Each time you re-run the cell a new set of random parameters will be sampled\n",
- "\n",
- "Some questions to consider:\n",
- "\n",
- " * How do the weights and bias initialisation scale affect the sort of predicted input-output relationships?\n",
- " * Does the linear form of the model seem appropriate for the data here?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " if (mpl.ratio != 1) {\n",
- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
- "\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
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- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
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- "source": [
- "weights_init_range = 0.5\n",
- "biases_init_range = 0.1\n",
- "\n",
- "# Randomly initialise weights matrix\n",
- "weights = rng.uniform(\n",
- " low=-weights_init_range, \n",
- " high=weights_init_range, \n",
- " size=(output_dim, input_dim)\n",
- ")\n",
- "\n",
- "# Randomly initialise biases vector\n",
- "biases = rng.uniform(\n",
- " low=-biases_init_range, \n",
- " high=biases_init_range, \n",
- " size=output_dim\n",
- ")\n",
- "# Calculate predicted model outputs\n",
- "outputs = fprop(inputs, weights, biases)\n",
- "\n",
- "# Plot target and predicted outputs against inputs on same axis\n",
- "fig = plt.figure(figsize=(8, 8))\n",
- "ax = fig.add_subplot(111, projection='3d')\n",
- "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n",
- "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n",
- "ax.set_xlabel('Input dim 1')\n",
- "ax.set_ylabel('Input dim 2')\n",
- "ax.set_zlabel('Output')\n",
- "ax.legend(['Targets', 'Predictions'], frameon=False)\n",
- "fig.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 3: computing the error function and its gradient\n",
- "\n",
- "Here we will consider the task of regression as covered in the first lecture slides. The aim in a regression problem is given inputs $\\left\\lbrace \\boldsymbol{x}^{(n)}\\right\\rbrace_{n=1}^N$ to produce outputs $\\left\\lbrace \\boldsymbol{y}^{(n)}\\right\\rbrace_{n=1}^N$ that are as 'close' as possible to a set of target outputs $\\left\\lbrace \\boldsymbol{t}^{(n)}\\right\\rbrace_{n=1}^N$. The measure of 'closeness' or distance between target and predicted outputs is a design choice. \n",
- "\n",
- "A very common choice is the squared Euclidean distance between the predicted and target outputs. This can be computed as the sum of the squared differences between each element in the target and predicted outputs. A common convention is to multiply this value by $\\frac{1}{2}$ as this gives a slightly nicer expression for the error gradient. The error for the $n^{\\textrm{th}}$ training example is then\n",
- "\n",
- "\\begin{equation}\n",
- " E^{(n)} = \\frac{1}{2} \\sum_{k=1}^K \\left\\lbrace \\left( y^{(n)}_k - t^{(n)}_k \\right)^2 \\right\\rbrace.\n",
- "\\end{equation}\n",
- "\n",
- "The overall error is then the *average* of this value across all training examples\n",
- "\n",
- "\\begin{equation}\n",
- " \\bar{E} = \\frac{1}{N} \\sum_{n=1}^N \\left\\lbrace E^{(n)} \\right\\rbrace. \n",
- "\\end{equation}\n",
- "\n",
- "*Note here we are using a slightly different convention from the lectures. There the overall error was considered to be the sum of the individual error terms rather than the mean. To differentiate between the two we will use $\\bar{E}$ to represent the average error here as opposed to sum of errors $E$ as used in the slides with $\\bar{E} = \\frac{E}{N}$. Normalising by the number of training examples is helpful to do in practice as this means we can more easily compare errors across data sets / batches of different sizes, and more importantly it means the size of our gradient updates will be independent of the number of training examples summed over.*\n",
- "\n",
- "The regression problem is then to find parameters of the model which minimise $\\bar{E}$. For our simple single-layer affine model here that corresponds to finding weights $\\mathbf{W}$ and biases $\\boldsymbol{b}$ which minimise $\\bar{E}$. \n",
- "\n",
- "As mentioned in the lecture, for this simple case there is actually a closed form solution for the optimal weights and bias parameters. This is the linear least-squares solution those doing MLPR will have come across.\n",
- "\n",
- "However in general we will be interested in models where closed form solutions do not exist. We will therefore generally use iterative, gradient descent based training methods to find parameters which (locally) minimise the error function. A basic requirement of being able to do gradient-descent based training is (unsuprisingly) the ability to evaluate gradients of the error function.\n",
- "\n",
- "In the next exercise we will consider how to calculate gradients of the error function with respect to the model parameters $\\mathbf{W}$ and $\\boldsymbol{b}$, but as a first step here we will consider the gradient of the error function with respect to the model outputs $\\left\\lbrace \\boldsymbol{y}^{(n)}\\right\\rbrace_{n=1}^N$. This can be written\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial \\bar{E}}{\\partial \\boldsymbol{y}^{(n)}} = \\frac{1}{N} \\left( \\boldsymbol{y}^{(n)} - \\boldsymbol{t}^{(n)} \\right)\n",
- " \\qquad \\Leftrightarrow \\qquad\n",
- " \\frac{\\partial \\bar{E}}{\\partial y^{(n)}_k} = \\frac{1}{N} \\left( y^{(n)}_k - t^{(n)}_k \\right) \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "i.e. the gradient of the error function with respect to the $n^{\\textrm{th}}$ model output is just the difference between the $n^{\\textrm{th}}$ model and target outputs, corresponding to the $\\boldsymbol{\\delta}^{(n)}$ terms mentioned in the lecture slides.\n",
- "\n",
- "The third exercise is, using the equations given above, to implement functions computing the mean sum of squared differences error and its gradient with respect to the model outputs. You should implement the functions using the provided skeleton definitions in the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "def error(outputs, targets):\n",
- " \"\"\"Calculates error function given a batch of outputs and targets.\n",
- "\n",
- " Args:\n",
- " outputs: Array of model outputs of shape (batch_size, output_dim).\n",
- " targets: Array of target outputs of shape (batch_size, output_dim).\n",
- "\n",
- " Returns:\n",
- " Scalar error function value.\n",
- " \"\"\"\n",
- " return 0.5 * ((outputs - targets)**2).sum() / outputs.shape[0]\n",
- " \n",
- "def error_grad(outputs, targets):\n",
- " \"\"\"Calculates gradient of error function with respect to model outputs.\n",
- "\n",
- " Args:\n",
- " outputs: Array of model outputs of shape (batch_size, output_dim).\n",
- " targets: Array of target outputs of shape (batch_size, output_dim).\n",
- "\n",
- " Returns:\n",
- " Gradient of error function with respect to outputs.\n",
- " This will be an array of shape (batch_size, output_dim).\n",
- " \"\"\"\n",
- " return (outputs - targets) / outputs.shape[0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check your implementation by running the test cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Error function and gradient computed correctly!\n"
- ]
- }
- ],
- "source": [
- "outputs = np.array([[1., 2.], [-1., 0.], [6., -5.], [-1., 1.]])\n",
- "targets = np.array([[0., 1.], [3., -2.], [7., -3.], [1., -2.]])\n",
- "true_error = 5.\n",
- "true_error_grad = np.array([[0.25, 0.25], [-1., 0.5], [-0.25, -0.5], [-0.5, 0.75]])\n",
- "\n",
- "if not error(outputs, targets) == true_error:\n",
- " print('Error calculated incorrectly.')\n",
- "elif not np.allclose(error_grad(outputs, targets), true_error_grad):\n",
- " print('Error gradient calculated incorrectly.')\n",
- "else:\n",
- " print('Error function and gradient computed correctly!')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 4: computing gradients with respect to the parameters\n",
- "\n",
- "In the previous exercise you implemented a function computing the gradient of the error function with respect to the model outputs. For gradient-descent based training, we need to be able to evaluate the gradient of the error function with respect to the model parameters.\n",
- "\n",
- "Using the [chain rule for derivatives](https://en.wikipedia.org/wiki/Chain_rule#Higher_dimensions) we can write the partial deriviative of the error function with respect to single elements of the weight matrix and bias vector as\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial E}{\\partial W_{kj}} = \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\frac{\\partial y^{(n)}_k}{\\partial W_{kj}} \\right\\rbrace\n",
- " \\quad \\textrm{and} \\quad\n",
- " \\frac{\\partial E}{\\partial b_k} = \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\frac{\\partial y^{(n)}_k}{\\partial b_k} \\right\\rbrace.\n",
- "\\end{equation}\n",
- "\n",
- "From the definition of our model at the beginning we have \n",
- "\n",
- "\\begin{equation}\n",
- " y^{(n)}_k = \\sum_{d=1}^D \\left\\lbrace W_{kd} x^{(n)}_d \\right\\rbrace + b_k\n",
- " \\quad \\Rightarrow \\quad\n",
- " \\frac{\\partial y^{(n)}_k}{\\partial W_{kj}} = x^{(n)}_j\n",
- " \\quad \\textrm{and} \\quad\n",
- " \\frac{\\partial y^{(n)}_k}{\\partial b_k} = 1.\n",
- "\\end{equation}\n",
- "\n",
- "Putting this together we get that\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial E}{\\partial W_{kj}} = \n",
- " \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} x^{(n)}_j \\right\\rbrace\n",
- " \\quad \\textrm{and} \\quad\n",
- " \\frac{\\partial E}{\\partial b_{k}} = \n",
- " \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\right\\rbrace.\n",
- "\\end{equation}\n",
- "\n",
- "Although this may seem a bit of a roundabout way to get to these results, this method of decomposing the error gradient with respect to the parameters in terms of the gradient of the error function with respect to the model outputs and the derivatives of the model outputs with respect to the model parameters, will be key when calculating the parameter gradients of more complex models later in the course.\n",
- "\n",
- "Your task in this exercise is to implement a function calculating the gradient of the error function with respect to the weight and bias parameters of the model given the already computed gradient of the error function with respect to the model outputs. You should implement this in the `grads_wrt_params` function in the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "def grads_wrt_params(inputs, grads_wrt_outputs):\n",
- " \"\"\"Calculates gradients with respect to model parameters.\n",
- "\n",
- " Args:\n",
- " inputs: array of inputs to model of shape (batch_size, input_dim)\n",
- " grads_wrt_to_outputs: array of gradients of with respect to the model\n",
- " outputs of shape (batch_size, output_dim).\n",
- "\n",
- " Returns:\n",
- " list of arrays of gradients with respect to the model parameters\n",
- " `[grads_wrt_weights, grads_wrt_biases]`.\n",
- " \"\"\"\n",
- " grads_wrt_weights = grads_wrt_outputs.T.dot(inputs)\n",
- " grads_wrt_biases = grads_wrt_outputs.sum(0)\n",
- " return [grads_wrt_weights, grads_wrt_biases]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check your implementation by running the test cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "All parameter gradients calculated correctly!\n"
- ]
- }
- ],
- "source": [
- "inputs = np.array([[1., 2., 3.], [-1., 4., -9.]])\n",
- "grads_wrt_outputs = np.array([[-1., 1.], [2., -3.]])\n",
- "true_grads_wrt_weights = np.array([[-3., 6., -21.], [4., -10., 30.]])\n",
- "true_grads_wrt_biases = np.array([1., -2.])\n",
- "\n",
- "grads_wrt_weights, grads_wrt_biases = grads_wrt_params(\n",
- " inputs, grads_wrt_outputs)\n",
- "\n",
- "if not np.allclose(true_grads_wrt_weights, grads_wrt_weights):\n",
- " print('Gradients with respect to weights incorrect.')\n",
- "elif not np.allclose(true_grads_wrt_biases, grads_wrt_biases):\n",
- " print('Gradients with respect to biases incorrect.')\n",
- "else:\n",
- " print('All parameter gradients calculated correctly!')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 5: wrapping the functions into reusable components\n",
- "\n",
- "In exercises 1, 3 and 4 you implemented methods to compute the predicted outputs of our model, evaluate the error function and its gradient on the outputs and finally to calculate the gradients of the error with respect to the model parameters. Together they constitute all the basic ingredients we need to implement a gradient-descent based iterative learning procedure for the model.\n",
- "\n",
- "Although you could implement training code which directly uses the functions you defined, this would only be usable for this particular model architecture. In subsequent labs we will want to use the affine transform functions as the basis for more interesting multi-layer models. We will therefore wrap the implementations you just wrote in to reusable components that we can build more complex models with later in the course.\n",
- "\n",
- " * In the [`mlp.layers`](/edit/mlp/layers.py) module, use your implementations of `fprop` and `grad_wrt_params` above to implement the corresponding methods in the skeleton `AffineLayer` class provided.\n",
- " * In the [`mlp.errors`](/edit/mlp/errors.py) module use your implementation of `error` and `error_grad` to implement the `__call__` and `grad` methods respectively of the skeleton `SumOfSquaredDiffsError` class provided. Note `__call__` is a special Python method that allows an object to be used with a function call syntax.\n",
- "\n",
- "Run the cell below to use your completed `AffineLayer` and `SumOfSquaredDiffsError` implementations to train a single-layer model using batch gradient descent on the CCPP dataset."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 1: 0.0s to complete\n",
- " error(train)=1.67e-01\n",
- "Epoch 2: 0.0s to complete\n",
- " error(train)=9.30e-02\n",
- "Epoch 3: 0.0s to complete\n",
- " error(train)=7.95e-02\n",
- "Epoch 4: 0.0s to complete\n",
- " error(train)=7.71e-02\n",
- "Epoch 5: 0.0s to complete\n",
- " error(train)=7.66e-02\n",
- "Epoch 6: 0.0s to complete\n",
- " error(train)=7.65e-02\n",
- "Epoch 7: 0.0s to complete\n",
- " error(train)=7.65e-02\n",
- "Epoch 8: 0.0s to complete\n",
- " error(train)=7.65e-02\n",
- "Epoch 9: 0.0s to complete\n",
- " error(train)=7.63e-02\n",
- "Epoch 10: 0.0s to complete\n",
- " error(train)=7.64e-02\n"
- ]
- },
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- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from mlp.layers import AffineLayer\n",
- "from mlp.errors import SumOfSquaredDiffsError\n",
- "from mlp.models import SingleLayerModel\n",
- "from mlp.initialisers import UniformInit, ConstantInit\n",
- "from mlp.learning_rules import GradientDescentLearningRule\n",
- "from mlp.optimisers import Optimiser\n",
- "import logging\n",
- "\n",
- "# Seed a random number generator\n",
- "seed = 27092016 \n",
- "rng = np.random.RandomState(seed)\n",
- "\n",
- "# Set up a logger object to print info about the training run to stdout\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
- "logger.handlers = [logging.StreamHandler()]\n",
- "\n",
- "# Create data provider objects for the CCPP training set\n",
- "train_data = CCPPDataProvider('train', [0, 1], batch_size=100, rng=rng)\n",
- "input_dim, output_dim = 2, 1\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-0.1, 0.1]\n",
- "param_init = UniformInit(-0.1, 0.1, rng=rng)\n",
- "\n",
- "# Create our single layer model\n",
- "layer = AffineLayer(input_dim, output_dim, param_init, param_init)\n",
- "model = SingleLayerModel(layer)\n",
- "\n",
- "# Initialise the error object\n",
- "error = SumOfSquaredDiffsError()\n",
- "\n",
- "# Use a basic gradient descent learning rule with a small learning rate\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=1e-2)\n",
- "\n",
- "# Use the created objects to initialise a new Optimiser instance.\n",
- "optimiser = Optimiser(model, error, learning_rule, train_data)\n",
- "\n",
- "# Run the optimiser for 5 epochs (full passes through the training set)\n",
- "# printing statistics every epoch.\n",
- "stats, keys, _ = optimiser.train(num_epochs=10, stats_interval=1)\n",
- "\n",
- "# Plot the change in the error over training.\n",
- "fig = plt.figure(figsize=(8, 4))\n",
- "ax = fig.add_subplot(111)\n",
- "ax.plot(np.arange(1, stats.shape[0] + 1), stats[:, keys['error(train)']])\n",
- "ax.set_xlabel('Epoch number')\n",
- "ax.set_ylabel('Error')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Using similar code to previously we can now visualise the joint input-output space for the trained model. If you implemented the required methods correctly you should now see a much improved fit between predicted and target outputs when running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " if (mpl.ratio != 1) {\n",
- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
- "\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "data_provider = CCPPDataProvider(\n",
- " which_set='train',\n",
- " input_dims=[0, 1],\n",
- " batch_size=5000, \n",
- " max_num_batches=1, \n",
- " shuffle_order=False\n",
- ")\n",
- "\n",
- "inputs, targets = data_provider.next()\n",
- "\n",
- "# Calculate predicted model outputs\n",
- "outputs = model.fprop(inputs)[-1]\n",
- "\n",
- "# Plot target and predicted outputs against inputs on same axis\n",
- "fig = plt.figure(figsize=(8, 8))\n",
- "ax = fig.add_subplot(111, projection='3d')\n",
- "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n",
- "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n",
- "ax.set_xlabel('Input dim 1')\n",
- "ax.set_ylabel('Input dim 2')\n",
- "ax.set_zlabel('Output')\n",
- "ax.legend(['Targets', 'Predictions'], frameon=False)\n",
- "fig.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 6: visualising training trajectories in parameter space"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Running the cell below will display an interactive widget which plots the trajectories of gradient-based training of the single-layer affine model on the CCPP dataset in the three dimensional parameter space (two weights plus bias) from random initialisations. Also shown on the right is a plot of the evolution of the error function (evaluated on the current batch) over training. By moving the sliders you can alter the training hyperparameters to investigate the effect they have on how training procedes.\n",
- "\n",
- "Some questions to explore:\n",
- "\n",
- " * Are there multiple local minima in parameter space here? Why?\n",
- " * What happens to learning for very small learning rates? And very large learning rates?\n",
- " * How does the batch size affect learning?\n",
- " \n",
- "**Note:** You don't need to understand how the code below works. The idea of this exercise is to help you understand the role of the various hyperparameters involved in gradient-descent based training methods."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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NMeuMMTfVe8m/6/7cZYypNMYcUfeaq40xS40xFcaYJcaY+uPixxljFhpjdhtj/mmMyaUF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\nuI2O+4w6OjqLgiugyIgii7KJCBQQyr7vlL0tpbR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xCRMmMGHChFrPZ7PZcDgc1Yo6KSVG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YLZ5iXvkdNoNMayBXq93pj+V7W0gVarrZUW2NSNUkRJg7uXpl6nruaeOjA4YGZlZd1yn8XFxRQW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\nSUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from ipywidgets import interact\n",
- "%matplotlib inline\n",
- "\n",
- "def setup_figure():\n",
- " # create figure and axes\n",
- " fig = plt.figure(figsize=(12, 6))\n",
- " ax1 = fig.add_axes([0., 0., 0.5, 1.], projection='3d')\n",
- " ax2 = fig.add_axes([0.6, 0.1, 0.4, 0.8])\n",
- " # set axes properties\n",
- " ax2.spines['right'].set_visible(False)\n",
- " ax2.spines['top'].set_visible(False)\n",
- " ax2.yaxis.set_ticks_position('left')\n",
- " ax2.xaxis.set_ticks_position('bottom')\n",
- " ax2.set_yscale('log')\n",
- " ax1.set_xlim((-2, 2))\n",
- " ax1.set_ylim((-2, 2))\n",
- " ax1.set_zlim((-2, 2))\n",
- " #set axes labels and title\n",
- " ax1.set_title('Parameter trajectories over training')\n",
- " ax1.set_xlabel('Weight 1')\n",
- " ax1.set_ylabel('Weight 2')\n",
- " ax1.set_zlabel('Bias')\n",
- " ax2.set_title('Batch errors over training')\n",
- " ax2.set_xlabel('Batch update number')\n",
- " ax2.set_ylabel('Batch error')\n",
- " return fig, ax1, ax2\n",
- "\n",
- "def visualise_training(n_epochs=1, batch_size=200, log_lr=-1., n_inits=5,\n",
- " w_scale=1., b_scale=1., elev=30., azim=0.):\n",
- " fig, ax1, ax2 = setup_figure()\n",
- " # create seeded random number generator\n",
- " rng = np.random.RandomState(1234)\n",
- " # create data provider\n",
- " data_provider = CCPPDataProvider(\n",
- " input_dims=[0, 1],\n",
- " batch_size=batch_size, \n",
- " shuffle_order=False,\n",
- " )\n",
- " learning_rate = 10 ** log_lr\n",
- " n_batches = data_provider.num_batches\n",
- " weights_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1, 2))\n",
- " biases_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1))\n",
- " errors_traj = np.empty((n_inits, n_epochs * n_batches))\n",
- " # randomly initialise parameters\n",
- " weights = rng.uniform(-w_scale, w_scale, (n_inits, 1, 2))\n",
- " biases = rng.uniform(-b_scale, b_scale, (n_inits, 1))\n",
- " # store initial parameters\n",
- " weights_traj[:, 0] = weights\n",
- " biases_traj[:, 0] = biases\n",
- " # iterate across different initialisations\n",
- " for i in range(n_inits):\n",
- " # iterate across epochs\n",
- " for e in range(n_epochs):\n",
- " # iterate across batches\n",
- " for b, (inputs, targets) in enumerate(data_provider):\n",
- " outputs = fprop(inputs, weights[i], biases[i])\n",
- " errors_traj[i, e * n_batches + b] = error(outputs, targets)\n",
- " grad_wrt_outputs = error_grad(outputs, targets)\n",
- " weights_grad, biases_grad = grads_wrt_params(inputs, grad_wrt_outputs)\n",
- " weights[i] -= learning_rate * weights_grad\n",
- " biases[i] -= learning_rate * biases_grad\n",
- " weights_traj[i, e * n_batches + b + 1] = weights[i]\n",
- " biases_traj[i, e * n_batches + b + 1] = biases[i]\n",
- " # choose a different color for each trajectory\n",
- " colors = plt.cm.jet(np.linspace(0, 1, n_inits))\n",
- " # plot all trajectories\n",
- " for i in range(n_inits):\n",
- " lines_1 = ax1.plot(\n",
- " weights_traj[i, :, 0, 0], \n",
- " weights_traj[i, :, 0, 1], \n",
- " biases_traj[i, :, 0], \n",
- " '-', c=colors[i], lw=2)\n",
- " lines_2 = ax2.plot(\n",
- " np.arange(n_batches * n_epochs),\n",
- " errors_traj[i],\n",
- " c=colors[i]\n",
- " )\n",
- " ax1.view_init(elev, azim)\n",
- " plt.show()\n",
- "\n",
- "w = interact(\n",
- " visualise_training,\n",
- " elev=(-90, 90, 2),\n",
- " azim=(-180, 180, 2), \n",
- " n_epochs=(1, 5), \n",
- " batch_size=(100, 1000, 100),\n",
- " log_lr=(-3., 1.),\n",
- " w_scale=(0., 2.),\n",
- " b_scale=(0., 2.),\n",
- " n_inits=(1, 10)\n",
- ")\n",
- "\n",
- "for child in w.widget.children:\n",
- " child.layout.width = '100%'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/03_Multiple_layer_models.ipynb b/notebooks/03_Multiple_layer_models.ipynb
deleted file mode 100644
index 6f681b2..0000000
--- a/notebooks/03_Multiple_layer_models.ipynb
+++ /dev/null
@@ -1,2498 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$\\newcommand{\\vct}[1]{\\boldsymbol{#1}}\n",
- "\\newcommand{\\mtx}[1]{\\mathbf{#1}}\n",
- "\\newcommand{\\tr}{^\\mathrm{T}}\n",
- "\\newcommand{\\reals}{\\mathbb{R}}\n",
- "\\newcommand{\\lpa}{\\left(}\n",
- "\\newcommand{\\rpa}{\\right)}\n",
- "\\newcommand{\\lsb}{\\left[}\n",
- "\\newcommand{\\rsb}{\\right]}\n",
- "\\newcommand{\\lbr}{\\left\\lbrace}\n",
- "\\newcommand{\\rbr}{\\right\\rbrace}\n",
- "\\newcommand{\\fset}[1]{\\lbr #1 \\rbr}\n",
- "\\newcommand{\\pd}[2]{\\frac{\\partial #1}{\\partial #2}}$\n",
- "\n",
- "# Multiple layer models\n",
- "\n",
- "In this notebook we will explore network models with multiple layers of transformations. This will build upon the single-layer affine model we looked at in the previous notebook and use material covered in the [second](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp02-sln.pdf) and [third](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp03-mlp.pdf) lectures.\n",
- "\n",
- "You will need to use these models for the experiments you will be running in the first coursework so part of the aim of this lab will be to get you familiar with how to construct multiple layer models in our framework and how to train them.\n",
- "\n",
- "## What is a layer?\n",
- "\n",
- "Often when discussing (neural) network models, a network layer is taken to mean an input to output transformation of the form\n",
- "\n",
- "\\begin{equation}\n",
- " \\vct{y} = \\vct{f}(\\mtx{W} \\vct{x} + \\vct{b})\n",
- " \\qquad\n",
- " \\Leftrightarrow\n",
- " \\qquad\n",
- " y_k = f\\lpa\\sum_{d=1}^D \\lpa W_{kd} x_d \\rpa + b_k \\rpa\n",
- "\\end{equation}\n",
- "\n",
- "where $\\mtx{W}$ and $\\vct{b}$ parameterise an affine transformation as discussed in the previous notebook, and $f$ is a function applied elementwise to the result of the affine transformation. For example a common choice for $f$ is the logistic sigmoid function \n",
- "\\begin{equation}\n",
- " f(u) = \\frac{1}{1 + \\exp(-u)}.\n",
- "\\end{equation}\n",
- "\n",
- "In the second lecture slides you were shown how to train a model consisting of an affine transformation followed by the elementwise logistic sigmoid using gradient descent. This was referred to as a 'sigmoid single-layer network'.\n",
- "\n",
- "In the previous notebook we also referred to single-layer models, where in that case the layer was an affine transformation, with you implementing the various necessary methods for the `AffineLayer` class before using an instance of that class within a `SingleLayerModel` on a regression problem. We could in that case consider the function $f$ to simply be the identity function $f(u) = u$. In the code for the labs we will however use a slightly different convention. Here we will consider the affine transformations and subsequent elementwise function $f$ to each be a separate transformation layer. \n",
- "\n",
- "This will mean we can combine our already implemented `AffineLayer` class with any non-linear function applied to the outputs just by implementing a layer object for the relevant non-linearity and then stacking the two layers together. An alternative would be to have our new layer objects inherit from `AffineLayer` and then call the relevant parent class methods in the child class however this would mean we need to include a lot of the same boilerplate code in every new class.\n",
- "\n",
- "To give a concrete example, in the `mlp.layers` module there is a definition for a `SigmoidLayer` equivalent to the following (documentation strings have been removed here for brevity)\n",
- "\n",
- "```python\n",
- "class SigmoidLayer(Layer):\n",
- "\n",
- " def fprop(self, inputs):\n",
- " return 1. / (1. + np.exp(-inputs))\n",
- "\n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " return grads_wrt_outputs * outputs * (1. - outputs)\n",
- "```\n",
- "\n",
- "As you can see this `SigmoidLayer` class has a very lightweight definition, defining just two key methods:\n",
- "\n",
- " * `fprop` which takes a batch of activations at the input to the layer and forward propagates them to produce activates at the outputs (directly equivalently to the `fprop` method you implemented for then `AffineLayer` in the previous notebook),\n",
- " * `brop` which takes a batch of gradients with respect to the outputs of the layer and back-propagates them to calculate gradients with respect to the inputs of the layer (explained in more detail below).\n",
- " \n",
- "This `SigmoidLayer` class only implements the logistic sigmoid non-linearity transformation and so does not have any parameters. Therefore unlike `AffineLayer` it is derived directly from the base `Layer` class rather than `LayerWithParameters` and does not need to implement `grads_wrt_params` or `params` methods. \n",
- "\n",
- "To create a model consisting of an affine transformation followed by applying an elementwise logistic sigmoid transformation we first create a list of the two layer objects (in the order they are applied from inputs to outputs) and then use this to instantiate a new `MultipleLayerModel` object:\n",
- "\n",
- "```python\n",
- "from mlp.layers import AffineLayer, SigmoidLayer\n",
- "from mlp.models import MultipleLayerModel\n",
- "\n",
- "layers = [AffineLayer(input_dim, output_dim), SigmoidLayer()]\n",
- "model = MultipleLayerModel(layers)\n",
- "```\n",
- "\n",
- "Because of the modular way in which the layers are defined we can also stack an arbitrarily long sequence of layers together to produce deeper models. For instance the following would define a model consisting of three pairs of affine and logistic sigmoid transformations.\n",
- "\n",
- "```python\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim), SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim), SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, output_dim), SigmoidLayer(),\n",
- "])\n",
- "```\n",
- "\n",
- "## Back-propagation of gradients\n",
- " \n",
- "To allow training models consisting of a stack of multiple layers, all layers need to implement a `bprop` method in addition to the `fprop` we encountered in the previous week. \n",
- "\n",
- "The `bprop` method takes gradients of an error function with respect to the *outputs* of a layer and uses these gradients to calculate gradients of the error function with respect to the *inputs* of a layer. As the inputs to a non-input layer in a multiple-layer model consist of the outputs of the previous layer, this means we can calculate the gradients of the error function with respect to the outputs of every layer in the model by iteratively propagating the gradients backwards through the layers of the model (i.e. from the last to first layer), hence the term 'back-propagation' or 'bprop' for short. A block diagram illustrating this is shown for a three layer model below.\n",
- "\n",
- "\n",
- "\n",
- "For a layer with parameters, the gradients with respect to the layer outputs are required to calculate gradients with respect to the layer parameters. Therefore by combining back-propagation of gradients through the model with computing the gradients with respect to parameters in the relevant layers we can calculate gradients of the error function with respect to all of the parameters of a multiple-layer model in a very efficient manner (in fact the computational cost of computing gradients with respect to all of the parameters of the model using this method will only be a constant factor times the cost of calculating the model outputs in the forward pass).\n",
- "\n",
- "We so far have abstractly talked about calculating gradients with respect to the inputs of a layer using gradients with respect to the layer outputs. More concretely we will be using the chain rule for derivatives to do this, similarly to how we used the chain rule in exercise 4 of the previous notebook to calculate gradients with respect to the parameters of an affine layer given gradients with respect to the outputs of the layer.\n",
- "\n",
- "In particular if our layer has a batch of $B$ vector inputs each of dimension $D$, $\\fset{\\vct{x}^{(b)}}_{b=1}^B$, and produces a batch of $B$ vector outputs each of dimension $K$, $\\fset{\\vct{y}^{(b)}}_{b=1}^B$, then we can calculate the gradient with respect to the $d^\\textrm{th}$ dimension of the $b^{\\textrm{th}}$ input given the gradients with respect to the $b^{\\textrm{th}}$ output using\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} \\pd{y^{(b)}_k}{x^{(b)}_d} \\rpa.\n",
- "\\end{equation}\n",
- "\n",
- "Mathematically therefore the `bprop` method takes an array of gradients with respect to the outputs $\\pd{E}{y^{(b)}_k}$ and applies a sum-product operation with the partial derivatives of each output with respect to each input $\\pd{y^{(b)}_k}{x^{(b)}_d}$ to produce gradients with respect to the inputs of the layer $\\pd{E}{x^{(b)}_d}$.\n",
- "\n",
- "For the affine transformation used in the `AffineLayer` implemented last week, i.e a forward propagation corresponding to \n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_k = \\sum_{d=1}^D \\lpa W_{kd} x^{(b)}_d \\rpa + b_k\n",
- "\\end{equation}\n",
- "\n",
- "then the corresponding partial derivatives of layer outputs with respect to inputs are\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{y^{(b)}_k}{x^{(b)}_d} = W_{kd}\n",
- "\\end{equation}\n",
- "\n",
- "and so the back-propagation method for the `AffineLayer` takes the following form\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} W_{kd} \\rpa.\n",
- "\\end{equation}\n",
- "\n",
- "This can be efficiently implemented in NumPy using the `dot` function\n",
- "\n",
- "```python\n",
- "class AffineLayer(LayerWithParameters):\n",
- "\n",
- " # ... [implementation of remaining methods from previous week] ...\n",
- " \n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " return grads_wrt_outputs.dot(self.weights)\n",
- "```\n",
- "\n",
- "An important special case applies when the outputs of a layer are an elementwise function of the inputs such that $y^{(b)}_k$ only depends on $x^{(b)}_d$ when $d = k$. In this case the partial derivatives $\\pd{y^{(b)}_k}{x^{(b)}_d}$ will be zero for $k \\neq d$ and so the above summation collapses to a single term, giving\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E}{x^{(b)}_d} = \\pd{E}{y^{(b)}_d} \\pd{y^{(b)}_d}{x^{(b)}_d}\n",
- "\\end{equation}\n",
- "\n",
- "i.e. to calculate the gradient with respect to the $b^{\\textrm{th}}$ input vector we just perform an elementwise multiplication of the gradient with respect to the $b^{\\textrm{th}}$ output vector with the vector of derivatives of the outputs with respect to the inputs. This case applies to the `SigmoidLayer` and to all other layers applying an elementwise function to their inputs.\n",
- "\n",
- "For the logistic sigmoid layer we have that\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_d = \\frac{1}{1 + \\exp(-x^{(b)}_d)}\n",
- " \\qquad\n",
- " \\Rightarrow\n",
- " \\qquad\n",
- " \\pd{y^{(b)}_d}{x^{(b)}_d} = \n",
- " \\frac{\\exp(-x^{(b)}_d)}{\\lsb 1 + \\exp(-x^{(b)}_d) \\rsb^2} =\n",
- " y^{(b)}_d \\lsb 1 - y^{(b)}_d \\rsb\n",
- "\\end{equation}\n",
- "\n",
- "which you should now be able relate to the implementation of `SigmoidLayer.bprop` given earlier:\n",
- "\n",
- "```python\n",
- "class SigmoidLayer(Layer):\n",
- "\n",
- " def fprop(self, inputs):\n",
- " return 1. / (1. + np.exp(-inputs))\n",
- "\n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " return grads_wrt_outputs * outputs * (1. - outputs)\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 1: training a softmax model on MNIST\n",
- "\n",
- "For this first exercise we will train a model consisting of an affine transformation plus softmax on a multiclass classification task: classifying the digit labels for handwritten digit images from the MNIST data set introduced in the first notebook.\n",
- "\n",
- "First run the cell below to import the necessary modules and classes and to load the MNIST data provider objects. As it takes a little while to load the MNIST data from disk into memory it is worth loading the data providers just once in a separate cell like this rather than recreating the objects for every training run.\n",
- "\n",
- "We are loading two data provider objects here - one corresponding to the training data set and a second to use as a *validation* data set. This is data we do not train the model on but measure the performance of the trained model on to assess its ability to *generalise* to unseen data. \n",
- "\n",
- "If you are in the Monday or Tuesday lab sessions you will not yet have had the lecture introducing the concepts of generalisation and validation data sets (though those doing MLPR alongside this course should already be familiar with these ideas). As you will need to report both training and validation set performances in your experiments for the first coursework assignment we are providing code here to give an example of how to do this."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import logging\n",
- "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer\n",
- "from mlp.errors import CrossEntropyError, CrossEntropySoftmaxError\n",
- "from mlp.models import SingleLayerModel, MultipleLayerModel\n",
- "from mlp.initialisers import UniformInit\n",
- "from mlp.learning_rules import GradientDescentLearningRule\n",
- "from mlp.data_providers import MNISTDataProvider\n",
- "from mlp.optimisers import Optimiser\n",
- "%matplotlib inline\n",
- "plt.style.use('ggplot')\n",
- "\n",
- "# Seed a random number generator\n",
- "seed = 6102016 \n",
- "rng = np.random.RandomState(seed)\n",
- "\n",
- "# Set up a logger object to print info about the training run to stdout\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
- "logger.handlers = [logging.StreamHandler()]\n",
- "\n",
- "# Create data provider objects for the MNIST data set\n",
- "train_data = MNISTDataProvider('train', rng=rng)\n",
- "valid_data = MNISTDataProvider('valid', rng=rng)\n",
- "input_dim, output_dim = 784, 10"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To minimise replication of code and allow you to run experiments more quickly a helper function is provided below which trains a model and plots the evolution of the error and classification accuracy of the model (on both training and validation sets) over training."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "def train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval):\n",
- "\n",
- " # As well as monitoring the error over training also monitor classification\n",
- " # accuracy i.e. proportion of most-probable predicted classes being equal to targets\n",
- " data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
- "\n",
- " # Use the created objects to initialise a new Optimiser instance.\n",
- " optimiser = Optimiser(\n",
- " model, error, learning_rule, train_data, valid_data, data_monitors)\n",
- "\n",
- " # Run the optimiser for 5 epochs (full passes through the training set)\n",
- " # printing statistics every epoch.\n",
- " stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
- "\n",
- " # Plot the change in the validation and training set error over training.\n",
- " fig_1 = plt.figure(figsize=(8, 4))\n",
- " ax_1 = fig_1.add_subplot(111)\n",
- " for k in ['error(train)', 'error(valid)']:\n",
- " ax_1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- " ax_1.legend(loc=0)\n",
- " ax_1.set_xlabel('Epoch number')\n",
- "\n",
- " # Plot the change in the validation and training set accuracy over training.\n",
- " fig_2 = plt.figure(figsize=(8, 4))\n",
- " ax_2 = fig_2.add_subplot(111)\n",
- " for k in ['acc(train)', 'acc(valid)']:\n",
- " ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- " ax_2.legend(loc=0)\n",
- " ax_2.set_xlabel('Epoch number')\n",
- " \n",
- " return stats, keys, run_time, fig_1, ax_1, fig_2, ax_2"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Running the cell below will create a model consisting of an affine layer follower by a softmax transformation and train it on the MNIST data set by minimising the multi-class cross entropy error function using a basic gradient descent learning rule. By using the helper function defined above, at the end of training curves of the evolution of the error function and also classification accuracy of the model over the training epochs will be plotted.\n",
- "\n",
- "You should try running the code for various settings of the training hyperparameters defined at the beginning of the cell to get a feel for how these affect how training proceeds. You may wish to create multiple copies of the cell below to allow you to keep track of and compare the results across different hyperparameter settings."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Varying initialisation scale\n",
- "\n",
- "First try a few different parameter initialisation scales"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `init_scale = 0.01`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 6.4s to complete\n",
- " error(train)=3.10e-01, acc(train)=9.14e-01, error(valid)=2.91e-01, acc(valid)=9.18e-01\n",
- "Epoch 10: 5.4s to complete\n",
- " error(train)=2.88e-01, acc(train)=9.20e-01, error(valid)=2.76e-01, acc(valid)=9.23e-01\n",
- "Epoch 15: 3.5s to complete\n",
- " error(train)=2.78e-01, acc(train)=9.23e-01, error(valid)=2.69e-01, acc(valid)=9.24e-01\n",
- "Epoch 20: 4.2s to complete\n",
- " error(train)=2.71e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 25: 4.7s to complete\n",
- " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.65e-01, acc(valid)=9.26e-01\n",
- "Epoch 30: 3.7s to complete\n",
- " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
- "Epoch 35: 3.8s to complete\n",
- " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 40: 4.2s to complete\n",
- " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n",
- "Epoch 45: 4.3s to complete\n",
- " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 50: 4.0s to complete\n",
- " error(train)=2.54e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 55: 3.6s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
- "Epoch 60: 4.4s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 65: 3.8s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 70: 4.3s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 75: 3.5s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.57e-01, acc(valid)=9.30e-01\n",
- "Epoch 80: 4.0s to complete\n",
- " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 85: 3.9s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 90: 3.6s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.57e-01, acc(valid)=9.29e-01\n",
- "Epoch 95: 4.6s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
- "Epoch 100: 3.8s to complete\n",
- " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n"
- ]
- },
- {
- "data": {
- "image/png": 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kKqyvp5xOtMlPoK67CWPZGxjzn8OoD2/3vBBCiLYnoW2RxBgHv76hLwMTo3mu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\nHAzLRg3LBhpmgjtyCKNkJ5TsxPhiF8b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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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V2YcrozwYVr+N+su7KGcTZAzHMP3ncMmwdvcPF0KIU5FAL8R54tUVxWUNvL3b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\nxWKxnJTmkUceAcDlcrFx40bCw8MBaG5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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.01 # scale for random parameter initialisation\n",
- "learning_rate = 0.1 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine + softmax model\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init),\n",
- " SoftmaxLayer()\n",
- "])\n",
- "\n",
- "# Initialise a cross entropy error object\n",
- "error = CrossEntropyError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `init_scale = 0.1`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 3.8s to complete\n",
- " error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
- "Epoch 10: 4.0s to complete\n",
- " error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
- "Epoch 15: 3.7s to complete\n",
- " error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
- "Epoch 20: 5.4s to complete\n",
- " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 25: 4.7s to complete\n",
- " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 30: 4.2s to complete\n",
- " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
- "Epoch 35: 4.0s to complete\n",
- " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 40: 4.3s to complete\n",
- " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 45: 4.5s to complete\n",
- " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 50: 3.7s to complete\n",
- " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 55: 3.7s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 60: 4.6s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
- "Epoch 65: 4.3s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 70: 4.9s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n",
- "Epoch 75: 4.7s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 80: 4.7s to complete\n",
- " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 85: 4.2s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 90: 4.4s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 95: 4.1s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 100: 4.2s to complete\n",
- " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
- ]
- },
- {
- "data": {
- "image/png": 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DO9LGzz45yOf7KkN2LGWzoR7+Eerme9DXrED731+i+8yfXlUIIUT7IUnbZO5I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\n9Ry31tEr1kmYrWvHxExd6v/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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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MHIqWfhqER4Cug1IHvutHPvb9rED3HPlYV0eWP1gPoJEA6rUg6gzB3u9aMHVa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\ntGj/LmsrhBCi75KgPwl7bS4KdtuZNDS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rw+12U1JSgsPh6NDG5XLh8XgAeOONN0hLS+t0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\nejS33norwcHBbN++nQsXLuDxePjmm284dOiQj/ZEpO/TEb1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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.1 # scale for random parameter initialisation\n",
- "learning_rate = 0.1 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine + softmax model\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init),\n",
- " SoftmaxLayer()\n",
- "])\n",
- "\n",
- "# Initialise a cross entropy error object\n",
- "error = CrossEntropyError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `init_scale = 0.5`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 3.7s to complete\n",
- " error(train)=3.38e-01, acc(train)=9.03e-01, error(valid)=3.17e-01, acc(valid)=9.11e-01\n",
- "Epoch 10: 4.3s to complete\n",
- " error(train)=3.06e-01, acc(train)=9.13e-01, error(valid)=2.94e-01, acc(valid)=9.17e-01\n",
- "Epoch 15: 3.3s to complete\n",
- " error(train)=2.92e-01, acc(train)=9.17e-01, error(valid)=2.83e-01, acc(valid)=9.20e-01\n",
- "Epoch 20: 5.5s to complete\n",
- " error(train)=2.82e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.22e-01\n",
- "Epoch 25: 3.8s to complete\n",
- " error(train)=2.77e-01, acc(train)=9.22e-01, error(valid)=2.75e-01, acc(valid)=9.22e-01\n",
- "Epoch 30: 4.3s to complete\n",
- " error(train)=2.71e-01, acc(train)=9.24e-01, error(valid)=2.71e-01, acc(valid)=9.25e-01\n",
- "Epoch 35: 3.9s to complete\n",
- " error(train)=2.67e-01, acc(train)=9.25e-01, error(valid)=2.69e-01, acc(valid)=9.26e-01\n",
- "Epoch 40: 4.4s to complete\n",
- " error(train)=2.65e-01, acc(train)=9.27e-01, error(valid)=2.68e-01, acc(valid)=9.26e-01\n",
- "Epoch 45: 4.2s to complete\n",
- " error(train)=2.61e-01, acc(train)=9.27e-01, error(valid)=2.66e-01, acc(valid)=9.27e-01\n",
- "Epoch 50: 4.2s to complete\n",
- " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.65e-01, acc(valid)=9.27e-01\n",
- "Epoch 55: 4.2s to complete\n",
- " error(train)=2.57e-01, acc(train)=9.29e-01, error(valid)=2.64e-01, acc(valid)=9.29e-01\n",
- "Epoch 60: 3.6s to complete\n",
- " error(train)=2.56e-01, acc(train)=9.28e-01, error(valid)=2.65e-01, acc(valid)=9.28e-01\n",
- "Epoch 65: 4.6s to complete\n",
- " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n",
- "Epoch 70: 3.7s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.64e-01, acc(valid)=9.28e-01\n",
- "Epoch 75: 5.0s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
- "Epoch 80: 3.7s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n",
- "Epoch 85: 3.8s to complete\n",
- " error(train)=2.48e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.30e-01\n",
- "Epoch 90: 4.1s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n",
- "Epoch 95: 4.3s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
- "Epoch 100: 3.7s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n"
- ]
- },
- {
- "data": {
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RXVP8VHohhBD+pHQjTr+2b9/OnDlz8Hg8jBw5kttvv73e+4sXL2bFihWYzWYi\nIiJ46KGH6NixIwDvvvsuW7duRWtNnz59eOCBB1ANhM/Jkycv4yu1HcfPVjBz02l2ni4lzRnEQ9fG\nkuIIavJ+nA4HZz6aj/7336G4EHXDLajb/wsVHuGHUrcP0dHR5ObKpC2+JvXqe1Kn/uHreo2Li2vU\neuYXX3zxxUut4PF4+O1vf8uUKVO44447mDNnDj179iQi4twv/MrKSn7wgx8wevRoKioqWLFiBYMH\nD2bv3r18/vnn/O53v+OWW25h4cKFxMbG0qlTp0sWqqhIrsECRAZZGJ4UQedwG+uOFfHx3nwKK6rp\nER2Mzdz4KxuhoaGURXdG3XAzVLnRq5egVy8BexB0TUGZ5CpJU4WEhFBa2rx+B+LipF59T+rUP3xd\nr+Hh4Y1ar8Hf1gcOHCA2NpaYmBgsFgtDhgxh06ZN9dbp3bs3drsdgLS0NFwuY7AQpRSVlZW43W6q\nqqqorq4mMjKyqd+lXVNK8d2kSN4al8yotA58ujefRz4+xOojhU2+Rq1CwjD9YCKmF/4AV6Wi//lX\nPC8/IWOXCyFEK9FgaLtcLpxOp/e50+n0hvKFrFy5kv79+wPQrVs3evXqxU9/+lN++tOf0q9fP+Lj\n431Q7PYnzGZm0jWxTB+ViDPEymvrTvLCyuNkFTb9li4V1xXTky9hemgylJfhee15PDNeQeed8UPJ\nhRBC+IpPO6KtXr2aQ4cOUdvifurUKU6cOMGMGTMAePnll9m9ezdXX311ve2WL1/O8uXLAZg2bRrR\n0dG+LFabEh0N16R14aNdp5ix7gg/++QI934nnvuuiSfIar7gNhaL5cJ1evM49I03U/LhPyhZ+Hf0\nzs2Efv8+Qr/3Q1RNy4m4sIvWqbgsUq++J3XqH4Gq1wZD2+FwkJeX532el5eHw+E4b70dO3bwwQcf\n8OKLL2K1WgHYuHEjaWlpBAUZnacGDBjAvn37zgvtjIwMMjIyvM+l00TDboiz0mdsEnO25TB303E+\n++YUk66JIb1L2HnrNthhYsQ4TP0God+fQ8k/Z1Gy7ENMEybCgEENdhpsr6Rzj39Ivfqe1Kl/BKoj\nWoPN4ykpKWRnZ5OTk4Pb7SYzM5P09PR66xw+fJhZs2bx9NNP17tmHR0dze7du6mursbtdvPNN9/Q\npUuXJn4VcTEdgi08OSSO32QkYDMrXv4ii9+uyuJMSVWT96WcHTFNehrTL34DQcF4/vd3eN54AZ19\n3A8lF0L2ZtWHAAAgAElEQVQI0RyNuuVr69atzJ07F4/Hw/Dhw7nzzjuZP38+KSkppKen8/LLL3Ps\n2DE6dOgAGGH9zDPP4PF4+Nvf/sbu3bsB6N+/Pz/+8Y8bLJTc8tV0VdWaj/a4+NfOXBRwd59obrva\ngcWkmvwXoa6uRq/6DP3hPKgoRw0fixp3Nyok1H9foJWRsxf/kHr1PalT/wjUmXajQvtKk9Buvpzi\nKv625TQbsopJiLTx0DWx3Nira7MOLl1UiF70LnrNUgiLQN15H2rISLlFDPlF6C9Sr74ndeofEtp1\nSGhfvo1ZRczafJqcEjfdO4WSEG4hOSqIZIedpKgggiyND1599CCef86Eg3sgMQ3T3T9BpfTwY+lb\nPvlF6B9Sr74ndeofEtp1SGj7RoXbw0d7XOxxudl7upCiSg8ACugSYSM5Kogkh70mzIOIsF+49zmA\n1hq9YRX6/XfgrAs1eATq+z9GRUZdmS/TwsgvQv+QevU9qVP/CFRoy9jjbZjdYmJ872iio6M5c+YM\nuaVuDrnKOZRfzqH8Cr45U8rqo4Xe9aNDLCQ7gkiOOhfk0SEWlFLGY9B30f2vRX/yHvo/H6K3fYka\ndw9qxFiURQ4lIYTwN/lN204opegYaqVjqJXrEs4Nl1dY7uZQfgWH8ss57DJ+bsoqprb5Jdxurhfi\nyVF2Ot9xH6ahN+GZ/zf0e2+j1yzDdM9PUD0HBObLCSFEOyGh3c5FBFno39lC/87neoaXuz0cya/g\nYM1Z+eH8cj7em4/bY0S53ay4ulMIY+/8OQNv3A0L/obnjV/BgEGYxj+I6hgbqK8jhBBtmoS2OE+Q\nxUSPjsH06Bjsfa2qWpNVWFHTvF7B+uNF/GZVFl0inNz2o6nceGgVtk/n4/nVo6hb7kSN+r6MqiaE\nED4mHdHaAX90RHF7NJnHili028VBVzkRdjO3JtgYteNDIjcuB0dHTBMehIFD2uSoatK5xz+kXn1P\n6tQ/pCOaaFUsJsWwxAhuuCqcr3PK+HCPiwUHivl32C18964Mxm2ZT/yMV6BHX0x3/xTVpWugiyyE\nEK2ehLa4LEopeseE0DsmhBOFlXy0x8XKQ2f5z1X3MLDb97jtq4X0eelxTCNqR1U7f2x0IYQQjSOh\nLXymS4SNh66N5d6+0Xy2v4BP9uXzYo8fkdS9iHE7P+H6jY9gu+O/ZFQ1IYRoJglt4XMRQRZ+0Cea\nO3o6WHW4kA/32Pjj1XfzbnUJoz9fxc1rVhJx9wOopG6BLqoQQrQqEtrCb2xmEzeldiAjJZJt2SUs\n2u3iXfNo3q+uZMSC1YyL+YLOd45HRbTPUdWEEKKpJLSF3ymlGBgXxsC4MA7nl/PhrjMsMw9hiYZr\n537O91JCufrmkTKqmhBCNEB+S4orKikqiCduSOBHpVV8svU4S3UK6/PtdHtnDbdd7WRAei/CLjEG\nuhBCtGcS2iIgnCFW7huazF3XVrNy7U4+OhrM9CM2OLKfGHMVKbERpHYMI8URRIojiHAJciGEkNAW\ngRViMzN2RH9GVVSw6/N17N97lIOeUA6WxJN5wuFdLybM6g3wVAlyIUQ7JaEtWgSL3U7/USPod4uG\nowfQa/5D4aaNHLI5ONi5F4fsfTiYG0XmsSLvNp1CredC3GkE+aWmFxVCiNZOQlu0KEopSExDJaYR\nOf4B+m9ZR781y2Dph2C2UNz/eg73z+BgWBcO1kxq8uXxukFu8Z6R1wZ6RJAc5kKItkF+m4kWSwUF\no67PgOsz0CePodf8h7D1K+mzZRV9nJ1Q12egbhhJSaiDQ/nlHHCVc7Dm8eXxYu9+gi0mwmwmwuxm\nwmy1DxPhdjOhtcs2YzncbjwPtZkJsZowtcFx04UQrZdMGNIOtKUJA3RVFXr7BvTaZfDNdlAm6D0Q\n09CboO813tvGiiurOVQT4Hllboorqimu9FBcWV3z8FBcUU2V5+KHv0lBqPVc2IfazITbTITZzCRE\nR5IUpklzBmM1S7D7Sls6VlsKqVP/CNSEIRLa7UBb/U+rz5xCr1uOXrccClwQHmkMkTr0JlRsl0bt\no8LtqRfi9UK9spqiimpKapcrqymprKao0kNRRTVgzC3es1MIfWNC6BMbQnJUEGaThHhztdVjNZCk\nTv1DQrsOCW3fauv/aXV1NXy9Fc+a/8COjeDxQLfeqBtuQg0cgrL5fl5vW1gkq3ZnsfNUCTtOl3L8\nbCVgnJn3jgmhT0wIfWND6Rppa5NTk/pLWz9WA0Hq1D8ktOuQ0Pat9vSfVhe40F+uRK9ZBmdOQXAo\nqk86pPZAJfeA+ESU+fJ7mH+7TvPL3Ow8XcqOUyXsPF3KqeIqACLtZvrE1oR4TCidw60S4pfQno7V\nK0Xq1D9kPm0hfEB1cKBuvQt9y52w/2v02v+gd++AjavQADY7JHVDJXdHpfSA5B6o8IjL/tyoYAvD\nEiMYlmjsK6e4ip2njbPwHadKWXvU6OHuDLHQt+YsvE9MCB1DrZf92UKI9kNCW7RJymSC7n1Q3fug\ntQbXGfSB3XBoL/rgHvSyD4xmdYBOcaiU7pBytfEzrivKdHln453CrIwM68DIlA5orTlZVOU9C99y\nsoTPDxcC0DncSt8YI8B7x4QQZjNjUqAUKJCzciFEPRLaos1TSoGzE8rZCa67EQBdUQFH96MP7kUf\n2oPetRW+/Nw4Gw8KNs7GU2qa1JO7o0LDLuvzu0TY6BJh49ZuUXi05lhBhfcsfM3RQpYeKLjwthgB\nblKgUPWWveGuFCbOXzYpY51gqwlnsAVniBVniKXmYa15zUKI1SR/HAjRSkhoi3ZJ2e1GZ7VuvQGM\ns/Ezp9CH9sDBPcbZ+CfvobXH2KBzAiq5O6T0QKX0QDscl9j7pZmUIjEqiMSoIG7r4aDaoznoKmdP\nbhkVbg9ag6emTB4NWoMGPFqft+ypXQe8z433z21fUllNXpmb/XnlnK3p9V5XkEXhCLYSHWLBEWIh\nOsSKI9hSL+Aj7eYm9YrXWlNRrSmr8hgPt+f8ZXd1vdfK3ZoujiIc1mriwm3ERdiICjLLHxRC1CEd\n0doB6YjSPLq8DA7vQ9c0qXNoL5QY16ZVRAe4dhjq+pGo+KQAl7Txqqo9uMrc5Ja6ySt1k1daRV6Z\nG1ep8ZqrtApXmZvqb/1WMCvjun1tiIfbzFS46waw51sB7OESt8DXYzcrgq0m7BYT+WVuKut8eLDF\nRFyEjS7hRktFXE2LRedwKyFWGbK2MeT/v39I7/E6JLR9S/7T+obWGk6fQB/ci23vV1RsXAvVbuia\nYoT3tcNQYZffqS3QPFpztrya3NKqc2FeVhPwpW7vYDV2i4lgq4ng2p91ly/02gWWgyymemfwDqeT\n3UezOVlUxcnCSk4UVnCiqIqThRWcKXFT95dVVLDFuOwQbiMuwkqXcDtxETZiwqxYfHCvvNaaKo+m\n0q2pqPZQWa2pcHuoqDZaOTqHW4mwt/yWAPn/7x8S2nVIaPuW/Kf1vejoaM4cOYTesBqduRyOHQKL\nBfpdi+n6DOg5wCe3lrU3lzpWK9weThVXcaKwgpOFVZwoquBEYRUniyq9g92A0SoQE2ajS4SVLhF2\nwmwmb+BWVhsBXOHWVFYbAex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- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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G/Px8IiJ86yzfe++9DBs2zL9HJ/qkCoebXaW+YfNdZU1UOz0ADLKYmJoUSXpc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\n9AItXp3HPi1mc1EjCyYMZH6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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.5 # scale for random parameter initialisation\n",
- "learning_rate = 0.1 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine + softmax model\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init),\n",
- " SoftmaxLayer()\n",
- "])\n",
- "\n",
- "# Initialise a cross entropy error object\n",
- "error = CrossEntropyError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "|`init_scale`| Final `error(train)` | Final `error(valid)` |\n",
- "|------------|----------------------|----------------------|\n",
- "| 0.01 | 2.43e-01 | 2.58e-01 |\n",
- "| 0.1 | 2.43e-01 | 2.59e-01 |\n",
- "| 0.5 | 2.45e-01 | 2.62e-01 |\n",
- "\n",
- "\n",
- "Larger initialisation scale of 0.5 seems to give slightly slower initial learning than smaller scales of 0.1 and 0.01 however difference is only slight suggesting for this shallow architecure training performance is not particularly sensitive to initialisation scale.\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Varying learning rate\n",
- "\n",
- "Now let's try some different values for learning rate."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `learning_rate = 0.05`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 4.0s to complete\n",
- " error(train)=3.41e-01, acc(train)=9.05e-01, error(valid)=3.16e-01, acc(valid)=9.12e-01\n",
- "Epoch 10: 4.0s to complete\n",
- " error(train)=3.10e-01, acc(train)=9.14e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
- "Epoch 15: 5.1s to complete\n",
- " error(train)=2.97e-01, acc(train)=9.18e-01, error(valid)=2.82e-01, acc(valid)=9.21e-01\n",
- "Epoch 20: 4.1s to complete\n",
- " error(train)=2.88e-01, acc(train)=9.20e-01, error(valid)=2.76e-01, acc(valid)=9.23e-01\n",
- "Epoch 25: 4.4s to complete\n",
- " error(train)=2.83e-01, acc(train)=9.21e-01, error(valid)=2.73e-01, acc(valid)=9.24e-01\n",
- "Epoch 30: 3.9s to complete\n",
- " error(train)=2.77e-01, acc(train)=9.22e-01, error(valid)=2.69e-01, acc(valid)=9.24e-01\n",
- "Epoch 35: 3.7s to complete\n",
- " error(train)=2.74e-01, acc(train)=9.24e-01, error(valid)=2.67e-01, acc(valid)=9.25e-01\n",
- "Epoch 40: 4.0s to complete\n",
- " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 45: 3.7s to complete\n",
- " error(train)=2.68e-01, acc(train)=9.26e-01, error(valid)=2.64e-01, acc(valid)=9.27e-01\n",
- "Epoch 50: 4.7s to complete\n",
- " error(train)=2.66e-01, acc(train)=9.26e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n",
- "Epoch 55: 3.7s to complete\n",
- " error(train)=2.64e-01, acc(train)=9.26e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
- "Epoch 60: 4.8s to complete\n",
- " error(train)=2.63e-01, acc(train)=9.26e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n",
- "Epoch 65: 3.8s to complete\n",
- " error(train)=2.61e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.27e-01\n",
- "Epoch 70: 4.2s to complete\n",
- " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 75: 4.3s to complete\n",
- " error(train)=2.58e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
- "Epoch 80: 4.5s to complete\n",
- " error(train)=2.57e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
- "Epoch 85: 4.2s to complete\n",
- " error(train)=2.56e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 90: 4.5s to complete\n",
- " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 95: 4.0s to complete\n",
- " error(train)=2.54e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 100: 3.4s to complete\n",
- " error(train)=2.53e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
- ]
- },
- {
- "data": {
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pOxie4JVJCCHE6ZHQDpA0ZwiPXZpCUU0TT356gNomb9DKouwOtKtuQnvybzBo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\nJAwGj0ANHgGA0dQEe/Mwcr8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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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qxqBBfKQ3sKeODO8W2NbwoAs237lqa0UVfYz6JM+7WIjBANMvwnDpQpiaOaCu\nLBdCDEw9/pUoLS0lISGB+Ph4AObNm8eOHTu6hXllZSU/+MEPAMjIyOCJJ54Aui/dZjabiYmJwW63\nd4W5EP5md3n45KidLYeb+arRe2vYjIQIbp0xgrnJUQFb31vpOny519sL//xT71XniWPQvnOHd9nO\n6LiA1EsIMTj1GOY2mw2LxdL13GKxcPDgwW5lUlJSKCws5JprrqGwsJD29nYcDgdRUVFdZUpLS3G7\n3V0fCgBee+013nzzTaZOncqtt95KkMyxLHyg06NTVNXKlrJmdla34NZhbGwIP5w1gqyx0QFdpEQ1\nHENt/wC1/UNorIOwCO9ynZde6b2ATW4VE0L0g0/G75YtW8b69evJz88nPT0ds9ncbRnNpqYmnnnm\nGe69996un99yyy3Exsbidrt54YUXeOedd1i6dOkZ287LyyMvLw+ANWvWYLVazygj+sdkMg2Z9lRK\n8UWtg7/vryPvqwYcLjeW8CC+M3M0iyaPYMKIyAtWl6+3q3I5cX6aj/PD/6Fj707QNIKnZxL6gx8T\nesl8tBA5D96TofS7OlBIm/pHoNq1xzA3m800NjZ2PW9sbMRsNp9R5sEHHwTA6XRSUFDQNZTe1tbG\nmjVruPnmm5k4cWLXe+LivMOIQUFBZGdn8+677551/zk5OeTk5HQ9l1W+fGcorJpW6+gg/4h3itQa\nRyfBRo25yVFkp0YzIyHixHlvJw0NzgtWJ6vVSn19PRz+0tsL3/ExtLeBNR5t8S1o867AYxlJK9Dq\ncIDDccHqNlgNhd/VgUba1D983a6nn64+lx7DPC0tjZqaGurq6jCbzWzfvp3777+/W5mTV7EbDAY2\nbdpEdnY2AG63m7Vr15KVlXXGhW5NTU3ExcWhlGLHjh0kJyf39tjEMNfS4WF7uYMth5spqW9HA6bG\nh/OdDAvfGhNFeFBg7rFWHg8022j9+D309/8KNRUQHIw251K0S3NgQoZM3CKE8Isew9xoNLJ8+XJW\nr16NrutkZ2eTnJzMG2+8QVpaGpmZmZSUlJCbm4umaaSnp7NixQoAtm/fzv79+3E4HOTn5wOnbkF7\n+umnsdvtgPec+1133eW/oxSDnltX7KpuIb/MTmFlC526Iik6mGUzRjA/NZoREX0/D66UApfTO3Oa\nsx1c7V2PlfPU41NfbWe+dtp76PROndoCkDYZ7Qf3oWVehhYW7tvGEEKIr9GUUmddYnigqq6uDnQV\nhoyBPsw+/nbbAAAgAElEQVTmcuvsPdZGUVUL28sdNLs8RIcYuXxsNNmp0Yw3h/brgjHlcqH+ex3q\nk/e9k7H0RkgYhJ75pYWEQdhpPwuLwHxpNsdDL9w5+uFgoP+uDkbSpv4xYIfZhbiQ6ls7Kapqoaiq\nhT3H2ujwKEKMGnNGR5KdGs3sxEhM53H/t6o6iv7iE1BdjnbZlRCfeCqYQ8NOhHb4iWA+EdDBoX0a\nHjdZrSB/JIUQF5CEuQgoj644UN9OUbU3wMubvUPVCZFBXDk+lszECKbGhxNsPL9zzUop1Efvod74\nI4SFY/jZP6NNmeWLQxBCiICTMBcXXLPTza7qVoqqW/i8ppXWDh2jBhkjw8lJi2XO6AhGRwX77J5r\n1dqC/sp/wK7tMGUWhhU/lUlZhBBDioS58DulFIebXF3D5wcbnSggNtTI3KQoMkdHMHNUhF+uQlel\nJegvPQnNNrSlP0S7colcUS6EGHIkzIVftHV62F3rvXhtZ3UrTe1uNGC8JZTvT7eSmRjJOHMIBn8t\nWKJ7UJv/gvprLlhGYvjH36GlTuz5jUIIMQhJmAufaWzrZFu5gx1VLZTUteHWITzIwKxREWSOjmR2\nYgSxof7/lVPHG9H/+G/w5V60i7PQbvux3B4mhBjSJMzFeXG6dT6rcLClzM6e2lZ0BckxwVw/ycyc\n0RGkjwg/r6vP+0rt2YG+4d+hw4X2w/vR5i2U+c6FEEOehLnoM4+u2FfXRn5ZM9vLW3C6dUZGBLE0\nw8L81GiSoi/8XOOqsxP11iuovHcgaSyGux5GG5XU8xuFEGIIkDAXvVbe7GLL4Wa2HrHT2OYmPMjA\n5SlRZKfGkD4yzG/nv3uijlV77x0vP4R2xXXeC92CggNSFyGECAQJc3FOx51uPj5iZ0uZnUM273rg\ns0dFcMeskVycFElIgNYDP0n/dAvqz/8JJhOGe3+BNnNuz28SQoghRsJcnKHDo1NY2cKWw83sqvGe\nB08zh7BizkiyUqKJDQv8r41ytqH+/ALqsy0wMQPDip+jmWU5RyHE8BT4v8piQNCVYn99O1sON7O9\n3EFrp44lzMSSdDPZqTGMiR04a26ro6XoL66F+lq0629Gu+67aIbArJQmhBADgYT5MFdt72BLWTP5\nZXbqWjsJNWl8KzmKBakxTIsPP7Ee+MCglELl/RX1lz9BVAyGB3+DNnFqoKslhBABJ2E+DHl0xSdH\n7bz3QRVf1DrQgBkJ4dwy3crc5CjCggbeDGnK0ey95WxvEcy8BMPtK9EiowNdLSGEGBAkzIcRXSk+\nLXfw2t4GKpo7SIkL4/ZZI5g/NhpLeN/XA78QlFLwxS70l5+BVgfaLXejLbhG7h0XQojTSJgPA0op\nCitbyN3TwJHjLpKig3noskQWz07F1tgY6OqdldJ1KC5A//tfoOwrSBiN4Se/REtODXTVhBBiwJEw\nH8KUUuyqbiV3TwOlNiejooL42bxRXJ4SjdGgBey+8HNR7k5UwVbU3/8CtVUwIgHt1v+HdulCuXdc\nCCG+gYT5EKSUYs+xNv68u4EvG9oZGWFi5dwEslNjBtQFbadTzjbUR/+Hev8dON4Iyalodz2ENnse\nmlGuVBdCiHORMB9ivqhrI3d3Pfvq2rGEmbjnonhy0mIJMg7QEHc0oz54F7Xlf6GtBSZNw3D7SsiY\nJefFhRCilyTMh4gvG9rJ3V1PcW0bsaFG7pwzkkUTYgk2Drwr0wFUwzHU/72N2vY+dHZ6r1C/+ia0\ncZMCXTUhhBh0JMwHuUM2J7m76ymqbiU6xMgPZ43gmolxAZ9m9ZuoyiOo995CFX4EmgFt7gK0RTfK\noihCCHEeJMwHqSNNTl7b28BnFS1EBhu4bYaVayfFER40MM8vq4Ml6Jvf9N4nHhKKtvB6tJxvyxSs\nQgjhAxLmg0xls4vX9jaw7aiDsCADN0+zcv3kOCKCB16IK12HvTvR//4mlO6HyGi0b9+Cln0tWkRU\noKsnhBBDhoT5IFHj6OD1vQ18dMROsFHjpgwLS9LNRIUMwBB3u1E7PvbeXlZdDpaRaDffhXbplWgh\nA2eOdyGEGCokzAc4j65Yt6uOzV81YTJoLJ5s5sYpZmJCB94/nTreiCra5r29zFYPo1PQVvwMLfNy\nNNPAq68QQgwV8hd2AHPrin/bVs22cgdXT4jle9OsmAfA8qMnKd0DZQdRe4pQ+4qg/LD3hfFTMNx6\nD0zLlNvLhBDiAhg4ySC6ceuKtZ9U82mFgztmj2BJuiXQVQJAtbagvtgFe4tQ+3ZBix00A6RNRrvx\nB2jTMtGSxga6mkIIMaxImA9AnR7F2m1VfFbRwvLZI/l2ujlgdVFKQdVR1N6dqL074NAB0HWIjELL\nmO3tfU+dLRe0CSFEAEmYDzCdHp3ff1JNYWULP8ocyXWTLnyQK5cLDuxB7d2B2rvTe/4bvFOsXr0U\nbXompE5AMwy8i++EEGI4kjAfQDo9Oms+qqKoupW7L4rnmolxF2zfqr4WtW8nak8RfLkXOjsgJBTS\nZ6Jd+13v8HncwBjqF0II0V2vwry4uJgNGzag6zoLFy5kyZIl3V6vr6/n+eefx263ExkZycqVK7FY\nLBw5coSXXnqJ9vZ2DAYDN954I/PmzQOgrq6Op556CofDwbhx41i5ciWmYXzFc8eJIN9Z3cr/uzie\nqyf4N8iV7qFj3y70jz9A7S2CmgrvCyNHoWUt8va+J0xFCxqY65wLIYQ4pcf01HWddevWsWrVKiwW\nC48++iiZmZkkJZ2afnPjxo1kZWWxYMEC9u3bR25uLitXriQ4OJj77ruPUaNGYbPZeOSRR5gxYwYR\nERG8+uqrXHvttVx66aW8+OKLfPjhh1x11VV+PdiByuXW+e1HVeyuaeXeSxK4anysX/ennG3oz/0r\nTft3g9EEEzPQsq5Cm5qJljDar/sWQgjhez1O4F1aWkpCQgLx8fGYTCbmzZvHjh07upWprKxk6tSp\nAGRkZFBUVARAYmIio0aNAsBsNhMTE4PdbkcpxRdffMHcuXMBWLBgwRnbHC5cbp3VWyvZXdPKfXMv\nQJA77OhrV8GXe4n60QMYnnoV4wP/giHn2xLkQggxSPUY5jabDYvl1LlSi8WCzWbrViYlJYXCwkIA\nCgsLaW9vx+FwdCtTWlqK2+0mPj4eh8NBeHg4xhPrVJvN5jO2ORw43Tq/ya9kT20b939rFDlpfg7y\nxnr03/8jVJdj+PEvCL9mKVpouF/3KYQQwv98cpJ62bJlrF+/nvz8fNLT0zGbzRgMpz4nNDU18cwz\nz3Dvvfd2+3lv5OXlkZeXB8CaNWuwWofGwhxtHR5+9dcv2FfXxmOLJrJo8ki/7s9dcYSmJx5Fa28l\n9ldPETxlJiaTaci050Ai7ep70qa+J23qH4Fq1x7D3Gw209jY2PW8sbERs9l8RpkHH3wQAKfTSUFB\nAREREQC0tbWxZs0abr75ZiZOnAhAVFQUbW1teDwejEYjNpvtjG2elJOTQ05OTtfzhoaGPh7iwNPW\n6eFftlRyoKGdn81LZI7V4NfjUmUH0Z/+FRiMGH6+GvvIJGhowGq1Don2HGikXX1P2tT3pE39w9ft\nmpiY2KtyPXaT09LSqKmpoa6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- "text/plain": [
- ""
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- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.1 # scale for random parameter initialisation\n",
- "learning_rate = 0.05 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine + softmax model\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init),\n",
- " SoftmaxLayer()\n",
- "])\n",
- "\n",
- "# Initialise a cross entropy error object\n",
- "error = CrossEntropyError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `learning_rate = 0.1`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 5.1s to complete\n",
- " error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
- "Epoch 10: 3.4s to complete\n",
- " error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
- "Epoch 15: 3.8s to complete\n",
- " error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
- "Epoch 20: 3.4s to complete\n",
- " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 25: 4.3s to complete\n",
- " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 30: 3.9s to complete\n",
- " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
- "Epoch 35: 4.5s to complete\n",
- " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 40: 5.9s to complete\n",
- " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 45: 4.0s to complete\n",
- " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 50: 4.1s to complete\n",
- " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 55: 5.4s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 60: 4.4s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
- "Epoch 65: 3.4s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 70: 5.4s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n",
- "Epoch 75: 3.7s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 80: 4.4s to complete\n",
- " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 85: 4.0s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 90: 5.1s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 95: 3.6s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 100: 3.9s to complete\n",
- " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
- ]
- },
- {
- "data": {
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DO9LGzz45yOf7KkN2LGWzoR7+Eerme9DXrED731+i+8yfXlUIIUT7IUnbZO5I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\n9Ry31tEr1kmYrWvHxExd6v/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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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MHIqWfhqER4Cug1IHvutHPvb9rED3HPlYV0eWP1gPoJEA6rUg6gzB3u9aMHVa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\ntGj/LmsrhBCi75KgPwl7bS4KdtuZNDS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rw+12U1JSgsPh6NDG5XLh8XgAeOONN0hLS+t0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\nejS33norwcHBbN++nQsXLuDxePjmm284dOiQj/ZEpO/TEb1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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.1 # scale for random parameter initialisation\n",
- "learning_rate = 0.1 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine + softmax model\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init),\n",
- " SoftmaxLayer()\n",
- "])\n",
- "\n",
- "# Initialise a cross entropy error object\n",
- "error = CrossEntropyError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `learning_rate = 0.2`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 3.5s to complete\n",
- " error(train)=2.90e-01, acc(train)=9.19e-01, error(valid)=2.77e-01, acc(valid)=9.22e-01\n",
- "Epoch 10: 3.8s to complete\n",
- " error(train)=2.75e-01, acc(train)=9.23e-01, error(valid)=2.69e-01, acc(valid)=9.25e-01\n",
- "Epoch 15: 5.3s to complete\n",
- " error(train)=2.66e-01, acc(train)=9.26e-01, error(valid)=2.64e-01, acc(valid)=9.26e-01\n",
- "Epoch 20: 4.3s to complete\n",
- " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 25: 4.9s to complete\n",
- " error(train)=2.57e-01, acc(train)=9.28e-01, error(valid)=2.64e-01, acc(valid)=9.27e-01\n",
- "Epoch 30: 5.0s to complete\n",
- " error(train)=2.53e-01, acc(train)=9.29e-01, error(valid)=2.61e-01, acc(valid)=9.30e-01\n",
- "Epoch 35: 4.2s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.30e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n",
- "Epoch 40: 4.0s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 45: 4.4s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 50: 3.8s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.60e-01, acc(valid)=9.31e-01\n",
- "Epoch 55: 3.9s to complete\n",
- " error(train)=2.43e-01, acc(train)=9.32e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 60: 3.9s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.31e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n",
- "Epoch 65: 3.8s to complete\n",
- " error(train)=2.41e-01, acc(train)=9.34e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n",
- "Epoch 70: 4.2s to complete\n",
- " error(train)=2.40e-01, acc(train)=9.34e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
- "Epoch 75: 3.7s to complete\n",
- " error(train)=2.38e-01, acc(train)=9.34e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n",
- "Epoch 80: 4.3s to complete\n",
- " error(train)=2.38e-01, acc(train)=9.33e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
- "Epoch 85: 3.2s to complete\n",
- " error(train)=2.36e-01, acc(train)=9.35e-01, error(valid)=2.61e-01, acc(valid)=9.30e-01\n",
- "Epoch 90: 4.1s to complete\n",
- " error(train)=2.36e-01, acc(train)=9.34e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 95: 3.0s to complete\n",
- " error(train)=2.37e-01, acc(train)=9.34e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n",
- "Epoch 100: 3.5s to complete\n",
- " error(train)=2.35e-01, acc(train)=9.35e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n"
- ]
- },
- {
- "data": {
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P1er+aDNyXXy4M4c1B/MwNFzVJoLx3e10i639JLPMgjIWbM9i1f4zBJkVY7ra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\nsX6zListD/bcKsI9B44fQW/9Br1iMXT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- "output_type": "display_data"
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ZS9IliFIDIu0nxJaNcPoEOGhto9OvHguDRna6+eInS828lZ7P/qIaNArsLazm\nyTHBjOju0d5FkyTpMshkLkm/IUw1iJ1bbf3g+3eDUKFXP5R7HkAZeW2nXEfdbFH5314D6/cbcNVq\nSIgOZFCAGy/9fJrnU3K4Z4gvt0fqO03rgiRJDclkLkmAUK1wYLetH3znFjCbQO+PMvl2lNFjUQK7\nt3cRWywzr4q3M/LJq6hjbC8v5gz3x9vF9qv/8oQw/pWWz392FXPUaOKRq4Nwc3Ro5xJLktRcMplL\nVzSRc9zWD572E5QawdUdZVSMrR+8TwSKRtPeRWyxMpOFFdsLSTleTpCnI8/FhTIk0L3BMc5aDU+M\nCaKPzoVVOwt56psTPHN9CN29ZD+6JHUmTUrmmZmZrFy5ElVViYuLY9q0aQ2eLyoqYtmyZZSXl+Ph\n4UFCQgJ6vZ6ioiKWLl2KqqpYrVYmTpzIhAkTADh69ChvvvkmtbW1DBs2jPvvv1828UltQtSabQk8\n5Ws4dQwcHCByOJo7fwdDRnX6XbmEEPxwtIxVOwqpsajcHqnn9oF6nLUX/mCiKAo3R+jo5ePMks25\nPPXNcZ64Jpgo2Y8uSZ1Go8lcVVWWL1/OokWL0Ov1LFy4kKioKEJCQuqPWbNmDTExMYwdO5a9e/eS\nlJREQkICPj4+vPDCCzg6OmIymXjyySeJiopCp9Px7rvvMm/ePPr27ctLL71EZmYmw4YNa9Wbla5s\norIckfIV4scNUFF2Zm/sP6CMug7F07u9i2cXOeVmlqXls7ewhgg/Vx4aFUiPbk2b7z440J1XJvbk\npZ9zeCElh7sH+3L7QD0a+SFbkjq8RpN5dnY2gYGBBAQEADBmzBgyMjIaJPOcnBxmz54NQGRkJEuW\nLLGdXPvr6evq6lBVFYCSkhJqamro168fADExMWRkZMhkLl1QZl4V5WYrQ4Pc8XJufn+uKMpHfP8p\n4pdk25zwgSPQ3HAL9B/UZVqD6qwqH+0z8NE+I85ahfmjA4kP9252Ivb3cCRxQhhvpeWTtLuYI0YT\nj42R/eiS1NE1msyNRiN6vb7+sV6v5/Dhww2OCQsLIz09nUmTJpGenk5NTQ0VFRV4enpSXFxMYmIi\n+fn5zJw5E51Ox5EjR847p9FovOD1k5OTSU5OBiAxMRFfX98W3ah0Pq1W2+HjmV9uYvFPB6m1CjQK\nRAR4Et3Th6vDfOgf4HHJZFV3KIuqz5Iwb00BjQaXmAm433Q32rDwVi1zW8d1R04pS348wsmSGsb3\n8+ORmF4ZVnRuAAAgAElEQVTo3C+vq+CFm/z4aFceb/x8lKe/z+GlKRGE6dzsVOLms3dMLarghLGa\nMB9XtA6dd1zE5egMv/+dUXvF1S4D4GbNmsWKFStISUkhIiICnU6H5szAIV9fX5YuXYrRaGTJkiVE\nR0c369zx8fHEx8fXP5Yra9mPr69vh4/nPzafBmDR9SEcNtawI7eKFVtPsnzrSbydHRgW5M7wYHeG\nBbnj5aJFqCrs2Y763SdwaJ9tQNuEW1DiplDXTU8pQCvfc1vFtdxsZdWOQn44WkaAhyN/jQ1heLAH\nak05xTWXf/7YECd840JZsimXuf/N5PFrghgd4nn5J24Be8Y0p9zM66l5HDKY8HR2YEyoJ9eGeRLp\n74aDpuO11AghAOzeitQZfv87I3vHNTg4uEnHNZrMdTodBoOh/rHBYECn0513zIIFCwAwmUykpaXh\n7u5+3jGhoaEcOHCA/v37N3pOSTpQVMOmExXcMVDPyBAPRoZ4cM9gP8pMFnbmVbEjt4odeVWkHC9H\nAfo6mRmWm8nwE+mEO9bgcPsclJgJKC7tV6NsDUIIUo6Vs2JHIVW1Vm4doOPOQb4XHeB2OQYFuPPK\njT156efTvPjTae4apOfOQb6dsh9dFYINB0tYnVmEs4PCfcP8OFpi5qfjZXybXYqPiwNjwry4rocn\n/f1c2/UeK2ut7Mqzvb935FahAHNH+DOmh2eX6RqS7KvRZB4eHk5eXh6FhYXodDpSU1N55JFHGhxz\ndhS7RqNh/fr1xMbGArYk7enpiZOTE5WVlRw8eJApU6bg4+ODq6srhw4dom/fvvz8889MnDixde5Q\n6pRUIVi+vQAfVy3TB+gbPOftomVsL2/G9vLGUllB9o8/seNQHjvce/ChfhTrfEfj5axhmLsHw/Pq\nGBZkqZ9X3dnllteyLCOf3fnV9Pd14aFRofT0cWnVa/q5O/LS+B68nZHP2j0GjhhNPD4mGHenztOP\nXlRVxz+35LG7oJoRwe48HB2EztX2njBbVLadrmTTiQq+zy5lw8ESfN20XBvmxbVhnvTRubR6AhVC\ncKzEzPbcSnbkVnGguAZVgLujhqFB7uRV1PL3zbmMCHZn3sgAAjw63oyLYyUmknYX09/XlVsH6OSH\njjamiLNtOJewY8cO3n//fVRVJTY2lunTp7Nu3TrCw8OJiopi69atJCUloSgKERERzJ07F0dHR3bv\n3s3q1atRFAUhBBMnTqxvMj9y5AhvvfUWtbW1DB06lDlz5jTph5+bm3v5dy0BHbuZ7adjZbyamkdC\ndCDx4d3Oe14YChHff4bY/L1tgZcBw9DccAvlvSLJzK9mR24VO/OqKDNbUYA+eheGB7szItiDPjqX\nVm1ObY241lkF6/cb+HCPAUcHhdlD/bihb7c2rT0KIfjqUCnLtxcQ4OHEwuu708O7bXaGa2lMhRD8\neLSM97YXogpb7XZ8uPdF/9ZU11lJz6lk84lyduZVYVEh0MORa8O8uC7Mk7BuznZLUpVmK5n5VWzP\nrWJnbiUlJisAvX2cGR7swYhgd/r7uuKgUbCqgi8PlpC0uwgh4O7Bvtx0le6y3sf2ep+W1lj4z+4i\nvs8uw0GjYFEF0wfomD3U74pM6O3VzN6kZN6RyGRuPx01mZstKg99cRQvZwdeubFng4QlThxBfPsJ\nYvsvoCgoI2NQJkxDCT1/33BVCI4YTWzPrWJHbiWHik0IwNPZgWGB7kSHenB1D0+7J0R7xzWrsJq3\n0vM5VVbLNT08mTvCH71b+60Jv6+wmpc3ncZsETw2JoirQ1u/H70lMS2tsfBmej7pOZVE+rvy6NVB\nzarRVpqtbM2pYNOJCnbnV6EKCPFy4rowL67t6UmIV/M+yKi/qX0fPFv7dtIwNNCdEcHuDA/2wMf1\n4q1IRVV1vJNRQMbpSnr5OPPQqED6+bo2qxxnXe77tNaq8sWBEv6310CtVWVyfx/uGOjLB7uK+OZw\nKVP7+zB3hP8Vl9BlMm8imcztp6Mm8w/3FvOfXcUsju/BwAA32wCgfTtQv10PB3aDiytKzA0ocVNR\ndH5NPm+52UpmXhXbcyvZmWurtQ/wc+Wh0YGE2rGGaa+4VpqtvJ9ZyHfZZfi5aXlgVGCHWciluLqO\nxJ9Pc9hg4o6Beu4a5NuhWjtST5azLL2AmjqVWUP9mHqVz2V9aCszWUg9WcHmE+XsK6xBAL18nOtr\n7Bf7kFBpttrGd+TZEnjpmdp3uM6Z4UG22ne/M7XvphJCsPVUJf/eVkBJjYVJ/X2YOcS32dMHL6e1\nY8upClbtLKKgso6R3T24f7h//aqBQgiWby/ki4MlTOzbjXkjAzrlGIuWksm8iWQyt5+OmMyNNRYe\n/PwIQ4PcWRgTgigxoL7zMhw5AN30KPFTUa67AcXNvfGTXYJ6pvl15Y5CTBaV6QNsq6Q52WGa0uXG\nVQjBphMVvLe9gAqzlZuu0nH3YF9cWmGA2+Wotaq8nV7AD0fLGBHszhPXBOPRSv3oTY1ppdnKv7cV\n8NPxcvroXHhsTJBdP6gBGKrrSD1ZwaYT5RwsNgHQV+/CdWFejOnhSZnJyo7cSrbnVnHIYKt9ezjZ\n+r5HBHswLMj9krXvpqqqtfKfXUV8dagUnauW348MaFYrSUvep0eMJpZvL2BfYQ1h3s7MGeHP0KDz\nfxeFEKzJLOLjLCPjenvz8OjADjlToDXIZN5EMpnbT0dM5m9szSPlWBn/mtKbwKJjqG+9BKYalDvn\nolwdi6K1b/Ny6Zn1y386Xk6wpyMPjgpkcODlfVC4nLjmV9TydkYBO/Oq6Kt34aFRgfTWte4At8sh\nhOCbw6W8u60Afw9HnokJafKKc83RlJjuyK3kja35lJks3DHQl9sG6tG2cgIpqKzllxMVbD5ZzhGj\nucFz4TqXM03n7vTTN6/23RwHi2t4Ky2f46VmRod48PuoAPzcG/89ac771Fhj4T+7ivjhSBmezg7M\nGOLL+PBul7wnIQTr9hj4755iYsK8eGxM0BWR0GUybyKZzO2noyXzo0YTT3x9nJsjdNxr3odY/S/o\npkPz8CKU7mGteu3MvCqWpeeTX1nHuN5e3D/MH68WjoBvSVwtquCz/UbW7ilGoyjMGurLjX19Os0f\nv6wz/egmi8qEPt0YEexBpL8rjnZakOVSMa2pU1m1s5BvDpcS6u3EY1cH00ff9h+ATpfXkp5TgbeL\nluFB7nSzQ+27qSyq4PMDRv672/b+mTnEl0n9Lv3+acr7tNaq8vn+Ev63z4BFVZnSX8ftA/XNaoH5\naJ+BNZlFXB3qyZPXBOPo0Dne0y0lk3kTyWRuPx0pmQshWPTDKU6WmnlLbMHtu4+g/yA0D/ypzfYP\nN1tUPtxrYH2WATcnB+4f5se43hcf+XwxzY3rgaIa3krP50SpmehQW83Ktx0HuLWUobqOtzMK2JFb\nhUUVODsoDA50qx+ZfTnTqS4W06zCal7fkkdBZR03R+iYMcTXLl0lnVVBZS3vZBSwPbeKcJ0L80cH\nEn6Rlp1LvU+FEKSerGDVzkIKqyyMDrH1iwd5tuxn+PkBI8u3FzKyuzt/vK57l/4ZyWTeRDKZ209H\nSuZbT1Xw0s+n+X3ldm7ctg4ldhLKHb9D0bb9/PCTpWbeTMvnQHENgwLceHBUYLO2BG1y/26tlQ8y\nbSN/dW5a5kUFMLoNRoa3NpNFZU9+tW3Udl4VBZV1AHT3cqqfHhjp79qsP+i/jWmtVSVpVzGf7jfi\n7+HIo9FBRAZ0rcWBWkoIwS8nK3hvWwFlZitT+vtwz2A/XB0bxvti79PDhhpWbC8kq6iGXj7OzBnu\nf9ldTwBfHyrh7YwChga580xM91ZZ5KiprKpgb2E1pjrV7ufuGeRLgNbc+IFNJJO51KiOkszrrIKE\nzw6hLS3m1fRX0d79ezTXt+8iQqoQfJ9dxvs7CzFbBbcP1HPrAF2Tmo0bi+vZWs+7Z/7YTu7vwz2D\nmz8auTMQQnC6opYdubb51PsKqqk7U2sfFPBrrT2wkRrfuTE9ajTxWmouJ8tquaFPN+4b7tclY3e5\nKmutrDnzYdHXTcu8kQGMOmc53t++Tw3VdXywq4gfj5bj7eLAzCF+xPX2tmtXT/KRUv61NZ/IADcW\nXR9y3geMtrC3oJrl2ws4WmK/hHuumHAdT0b72+18MplLjeooyfzTjXtYmevIokNJRM24A6XfwPYu\nUr2SGgvLtxew6UQFIV5OPDQqsNEa4KXi2rAZ1JmHRgW1S/9uezFbVPYUVNeP9s4/U2sP9nSqHyw2\nMMDtvFq7r68vBYVFfLTPwLo9xXi5aEkYHciIDjJVryPbX1TNsrQCTpSZufpMN47ezbH+fWq2qHy2\n38hH+wxYBdx0lQ+3D9S32gekn46V8Y8tefTTu/KX2JA2W0kwv6KWVTuL2HKqAl83LTOH+BHWCoM1\nuwf44lxXabfzyWQuNaq9k7kQgrLvN/BgXnf6mYt4dvoQFN+AdivPpWw/XcnbGQUUVtURH+7NfcP8\n8bzIdqwXiqv1nAFKigIzhvgxuZEBSleC3PLa+kVU9hZWU2sVOJ2ptY8I9mB4sDtBnk5Uadz461dZ\nHDaYiAnz4g8jAy4af+l8FlXw6X4j6/YU46AozBrqx8yr+/DZjmO8v7OQ4moLV4d6ct8wv0ZbSezh\nl5PlvLI5l946F/4aG9qqP8vqOiv/22vg8wMlOChwW6SemyN0rdbML/vMm0gmc/tpz2Qu6uoQ/3mL\nfxd68G3w1fxjQnfC/L3bpSxNZbaorN1j66f1dHJgzgh/ru/pdd4Aud/G9VCxbYDbsRIzI7t7MG9k\n06YOXWnMFpW9BdVsz7Ot2JdXYau1B3k6Yqi24uwAD4wK5NqwthkQ2RXlnZn6mJlXhbeLljKThd4+\nzswdEcDANh5zkJ5Twcubcgn1duJv40Ltvn+CVRX8cLSMD3YVUWayMq63FzOH+LX66okymTeRTOb2\n017JXJSVoC57iZw8I4+NepIJfbrx4OigNi9HSx0vMfFmWj6HDCaGBNoGyJ07yvdsXKvrrHywq5iv\nDpbg46rlD1EBRId6XHHLW7ZUXsWvtXa9pxt3R3rXb44itdzZRYl+PlVNdLALsb3s2y/eHDtyK3np\n59MEejjyXFwPuyymA7A7v4rl2ws5Xmomws+VuSP86atv2bK3zSWTeRPJZG4/7ZHMxYkjqG8uhqpy\nFo//CwdqXVh2U+9Ot6uZVRV8m13KmswiLKrgjoF6pkXocXRQ0Ov1fJl5nHczCjDWWJjUrxszh8pB\nWpejvbuEuqKOEtPd+VW8kJKD3s2RF+JDL6vmnFdRy8odhaTlVOLvruXeYf5c08bbxnbY/cwlyV7U\njE2IVa+Dhxe7/vAS2/dauW+YvtMlcgAHjcKkfj6MDvHgve2FfLCrmJ+PlzNziB8/bylk81EjvXyc\neTqme4s3wpCkK8HgQHf+Ni6Uv23M4ZnvT/J8XA/8PZqX0KtqrXy418CXB41oNRpmDfHjpgifLj2f\n/bdkzfwK1lafzIWqIj5LQnz1IfSJQMz7E49vLqXWKvjXlF52WyWsPWXkVPJORj5F1RZctBruGqRn\n6lW6Vl9O9ErRUWqRXUlHi+mh4hqe3XgKN62G5+N7NGmBGqsq+P5IKUm7iik3W4kL92bGEL927Y6R\nNXOpSxKmatTlr0FmGsq141HueYBvjlVysqyWp6/r3iUSOcDIEA8GBvQm5VgZ8QNDcay139QUSboS\n9PN15YW4Hvzlx1NnauihhFxik5xdZ/rFT5SaifR3Ze6IgIuudnclaFIyz8zMZOXKlaiqSlxcHNOm\nTWvwfFFREcuWLaO8vBwPDw8SEhLQ6/UcP36cd999l5qaGjQaDdOnT2fMmDEA7Nmzhw8++ABVVXFx\ncWH+/PkEBgba/w6ldiOK8m3943mnUO76A8q4yVTXqfx3dzED/V2JDu1ac4RdHTXc2M8HXy8Xiotl\nMpek5uqtc2FxfA/+/MNJnkk+yXPjQunp0zBB55bXsnJnIek5lQR4OPKn64K5OrRt+8U7okaTuaqq\nLF++nEWLFqHX61m4cCFRUVGEhITUH7NmzRpiYmIYO3Yse/fuJSkpiYSEBJycnHj44YcJCgrCaDTy\n9NNPM2TIENzd3Xnvvfd46qmnCAkJ4dtvv+Xjjz9m/vz5rXqzUtsRB/egvp0IqkDz6LMoA4YC8L+9\nBsrNVuaMCLjif/kkSTpfWDdnXozvwZ9/OMWi5JP8La4H4ToXKmutfLinmA2HSnDUaLh3qB9Trrqy\n+sUvpdEoZGdnExgYSEBAAFqtljFjxpCRkdHgmJycHAYOtK3aFRkZybZt2wBbW39QkG3KkU6nw9vb\nm/Ly8vrX1dTUAFBdXY2Pj4997khqd2rKV6iv/QU8u6F5Zml9Is+vqOWLgyXE9va+opvDJEm6tBBv\nZ14c3wMXrYY/J5/kv7uLeODzo3x+oIRxvb15+6beTI/Uy0R+jkZr5kajEb1eX/9Yr9dz+PDhBseE\nhYWRnp7OpEmTSE9Pp6amhoqKCjw9f10HODs7G4vFQkCAbYWvBx54gJdeegknJydcXV1ZvHixve5J\nagfCYoHTxxE/fYPY9B0MikLzuydR3H7doGHVziK0Gpg5xLcdSypJUmcQ5OnEi+PD+PMPJ1m7x8DA\nADfmDvent6wIXJBdBsDNmjWLFStWkJKSQkREBDqdDo3m109MJSUlvPHGG8yfP7/+/zds2MDChQvp\n27cvn3/+OatXr+aBBx4479zJyckkJycDkJiYiK+vTAT2otVqWxRPIQRqcQF1h7KoO7TX9vXoAait\nBcDtlhl4zHgAxeHXedWZp8vYcqqC30X3oH+PzrNATEu0NK7SxcmY2l9niKmvL7x3ty/HjdUM7X7+\naosdUXvFtdFkrtPpMBgM9Y8NBgM6ne68YxYsWACAyWQiLS0Nd3dbjay6uprExETuvvtu+vXrB0B5\neTknTpygb9++AIwZM+aiNfP4+Hji4+PrH3ekqRSdXVOnUAhTDZzIRhw9iDh6CI4dhLIS25OOTtCj\nN8r1N0Kv/ijh/THr/DCXlNS/XhWCV388gd5Ny4Qwly7/M+xoU366AhlT++tMMQ11oUEe6sg67NS0\n8PBw8vLyKCwsRKfTkZqayiOPPNLgmLOj2DUaDevXryc2NhYAi8XC0qVLiYmJITo6uv54d3d3qqur\nyc3NJTg4mN27d9O9e/fm3J90mU6WmUkrKGCIXsHlnA0HhKpCXg7i6AE4dghx9CDkngJxZt9f/2CU\niKHQux9Kr34Q0hNFe+kFHlKOlXPEaOLxMUHtuoexJElSV9VoMndwcGDOnDksXrwYVVWJjY0lNDSU\ndevWER4eTlRUFFlZWSQlJaEoChEREcydOxeA1NRU9u/fT0VFBSkpKQDMnz+fnj17Mm/ePF555RU0\nGg3u7u48+OCDrXqj0q+2na5kyeZcTBYVT0eFid1MTKrMwvt4Fhw7BCbbwETcPKBXX5ThV6P06m/7\n3qN5m1yYLCprMovoq3chpqfcIEOSJKk1yBXgrjAbDpbw3vYCejqYmHHsG7516UOG7wC0wsrYysPc\n7FVB916hKL37Q0DwZfdR/Xd3EWv3GEgc34MI/7bdlam9dKbmy85CxtT+ZExbR4dtZpe6BqsqWL6j\nkA0HSxglCnnsp9fxjBjI8AEO5AbCZ5U6Np6IJFkVjKrz4BaNDxGXmciLq+v4JMvINT08r5hELkmS\n1B5kMr8CVNdZeWVzLttyq5hauY/Z21ajnXQ7PnMfwWA0EgLMB2YMt7DhYAlfHyohLaeS/r6u3BKh\nY1SIR4u2SFyTWYQQcO8wP7vfkyRJkvQrmcy7uOLqOl5IyeFEiZl5ucnccHQjyu+eQDP6ehRNw8Fo\n3Vy0zBjix62Ren44UsZnB4wkbjpNsKcjN12lY1xv7yYPYDtsqCHlWDm3DtAR4NH4hgmSJElSy8lk\n3oUdMZp4ISWHGnMd/7d/DcNMp9E89aKtP/wSXLQaJvf3YWLfbmw9VcH6/UbeziggaXcxk/v5MKlf\nN7wusW2pEILl2wvxdnHgtoH6ix4nSZIk2YdM5l1U2qkKXvklFy9Ry4tp/yJM74rmiaUouqY3eTto\nFK4J82JMD0+yCmtYv9/Af/cU83GWgbje3twcobvgNoWpJyvYX1TD/NGBuDk6XODMkiRJkj3JZN7F\nCCH4/EAJK3cUEq6WsXDr6/gMHIxmzmMozi1bBlFRFCID3IgMcONkmZnP9hv5/kgZ3xwuJTrUk1sG\n6Ojv6wpArVVl1c4ienZzJq63tz1vTZIkSboImcy7EKsqeHdbAV8fLiW65jiPZryLy6TpKFPvPq9/\nvKV6eDuTEB3EjCF+tsFyh0vYcqqCAX6uTBug41RZLYVVdTwXF9qiQXOSJElS88lk3kVU11n5+6Zc\nduZVcUthGjMOf4nD3EfRjIpplevpXLXMGurHrZE6fjhSxucHjLz402kARnb3YEigeyNnkCRJkuxF\nJvMuoLDSNmI9p8zEg0c+Z3z5fjRPLbYtt9rK3BwdmHqVjkn9fPjlZAVbTlVw71A5FU2SJKktyWTe\nyR021PBCSg615loW7VrBEE/Vtoe4rm137XHQKMT09JJLtkqSJLUDmcw7sS0nK3g1NZdulir+lvEW\noRF90Nz/OIqzc3sXTZIkSWpDMpl3QkII1u838v7OIvqZC3l62zJ8bpiKMvUuuw10kyRJkjoPmcw7\nGYsqeCcjn++yy7im9AAP71+Ly/3z0Yy8rr2LJkmSJLUTmcw7kcpaK3/fdJpd+dXclvMTdxVtQfvk\n8yi9+rZ30SRJkqR2JJN5J1FQWctzG3PILzeTcOBDYp3L0PzfKyg+crlUSZKkK12TknlmZiYrV65E\nVVXi4uKYNm1ag+eLiopYtmwZ5eXleHh4kJCQgF6v5/jx47z77rvU1NSg0WiYPn06Y8aMAWz9vmvX\nrmXr1q1oNBrGjx/PpEmT7H+HXcD+ompe+ikHq8nEX3atYFCfYJT7npYD3SRJkiSgCclcVVWWL1/O\nokWL0Ov1LFy4kKioKEJCQuqPWbNmDTExMYwdO5a9e/eSlJREQkICTk5OPPzwwwQFBWE0Gnn66acZ\nMmQI7u7upKSkYDAYeO2119BoNJSVlbXqjXY2Qoj69dAzTlcRaCnn/7a/Q0h8vG2g22XuNS5JkiR1\nHY0m8+zsbAIDAwkICABgzJgxZGRkNEjmOTk5zJ49G4DIyEiWLFkCQHBwcP0xOp0Ob29vysvLcXd3\n57vvvuPRRx9Fc2b0tbe3XMcbbEuynt2p7LDBhJeThjuLtjL5aDJesx9Aibq2vYsoSZIkdTCNJnOj\n0Yhe/2u/rF6v5/Dhww2OCQsLIz09nUmTJpGenk5NTQ0VFRV4enrWH5OdnY3FYqn/UFBQUEBqairp\n6el4eXlx//33ExQUZK/76nRMFrV+WdT8yjqCPB15YGQAY3esx2nfp2ieeB4lYkh7F1OSJEnqgOwy\nAG7WrFmsWLGClJQUIiIi0Ol09TVugJKSEt544w3mz59f//91dXU4OjqSmJhIWloay5Yt47nnnjvv\n3MnJySQnJwOQmJiIr2/brmzW2kqqa/loVx6f7M6j3GQhMtCThOvDua63HuvhfZT8+BmuE6fjdV2c\n3a+t1Wq7XDw7AhlX+5MxtT8Z09bRXnFtNJnrdDoMBkP9Y4PBgE6nO++YBQsWAGAymUhLS8Pd3bbR\nRnV1NYmJidx999306/frWuF6vZ7Ro0cDMGrUKN56660LXj8+Pp74+Pj6x8XFxU29tw7tdHktn+03\nsvFYGXVWwagQD26J0BHh7waAsSAf9fXnwUePedIdrXLfvr6+XSaeHYmMq/3JmNqfjGnrsHdcz+2u\nvpRGk3l4eDh5eXkUFhai0+lITU3lkUceaXDM2VHsGo2G9evXExsbC4DFYmHp0qXExMQQHR3d4DUj\nR45k7969jBs3jqysrCYXuLPbX1TN+iwj6TmVaDUK43p7c1OEDyFeDUemi68+hLxTaB75K4qrWzuV\nVpIkSeoMGk3mDg4OzJkzh8WLF6OqKrGxsYSGhrJu3TrCw8OJiooiKyuLpKQkFEUhIiKCuXPnApCa\nmsr+/fupqKggJSUFgPnz59OzZ0+mTZvGP//5TzZs2ICLiwvz5s1r1RttT1ZVkH66kvVZRg4W1+Dp\npOH2gXom9/Ohm+v5PwKRcwzx9Uco0bEog0a0Q4klSZKkzkQRQoj2LkRz5ObmtncRmsxsUdl4rIzP\n9hvJragjwMORm6/SERfujYv2wmuoC6sV9aWnwFiE5rk3UTxabxcy2czWOmRc7U/G1P5kTFtHh21m\nl5qv3GThq8OlfHWwhDKzlT46F5661o+rQz1x0Fx6frhI/gxOZKOZ98dWTeSSJElS1yGTuR2ZLSrv\nZxbxfXYptVZBVLA7twzQE+nv2qRFXkRBLuKzJBgaDSOuaYMSS5IkSV2BTOZ29MWBEjYcLCGutzfT\nBujo4d305VaFqqKufgO0jmhmzJMrvEmSJElNJpO5nZgtKp8fMDI8yJ1Hrm7+4jfi52/h0D6UexNQ\nusnNUyRJkqSmu/AoLKnZko+UUWa2cltk8xOxMBYhPl4FEUNQrolv9HhJkiRJOpdM5nZgUQXrswxE\n+LkywN+1Wa8VQqCueQtUFc2s+bJ5XZIkSWo2mczt4Ofj5RRVW7gtUt/sZCzSfoK921FumYXiF9hK\nJZQkSZK6MpnML5MqBB/vM9CzmzMjgt2b9VpRXopY9y6EX4UybnIrlVCSJEnq6mQyv0xppyrJKa/l\n1pbUyte+C6YaNLMfRtE4tFIJJUmSpK5OJvPLIITgo30GAj0cuaaHZ+MvOPe1mVsRGZtQJt+JEtyj\nlUooSZIkXQlkMr8Mu/KryTaauDVS3+jKbucS1ZWoH7wNIT1RJt7aiiWUJEmSrgQymV+Gj/YZ0Llq\nie3VvGVXxUeroLwUzX2PoGjlVH9JkiTp8shk3kIHi2vYU1DNzRE+ODo0PYxi/y7Epu9QJkxDCevT\niteYRAcAABooSURBVCX8//buPzqq6u73+PtMhiAkkGRmIBBBAoHYNKAVQ00DRGJSbUErD0UqVfqw\nTK8WQqxWvKL1el1ttVixoFwETAkoPmmhjw+0WisaNIBGScIv5ZcSKlQKEpIZyAAJYXLO/SMyNPIj\nA844GfJ5rcVaGWafM9/zXQe+2fvs2VtERDoKFfOL9N/b6oiNtnHTwISAj7FONGK+9P+gZxLGDyaG\nMDoREelIVMwvwt7DJ6jYd5Sbr0ygS6cL6JWv/C+oPYjtP6dhRAe+bruIiMj5qJhfhP/ZVsdldoMx\nVzoCPsbavRNr9V8xRn0fI3VwCKMTEZGOJqDZV5s3b2bx4sWYpklubi5jx45t9f6hQ4eYP38+9fX1\nxMbGUlhYiNPpZM+ePRQVFdHQ0IDNZmPcuHFkZWW1Ora4uJh33nmHpUuXBu+qQujg0SbW7q3nlisT\n6N45sO+GWydPYr44FxKcGOP+M8QRiohIR9NmMTdNk0WLFvHoo4/idDp5+OGHycjIoE+fPv42S5cu\nJTs7m1GjRrF161ZKSkooLCwkOjqaadOm0bt3b9xuNzNmzODqq68mJqZlpbTdu3dz7Nix0F1dCKzY\n7sZmGNyadgG98tf/DAc+w3bvYxhduoYwOhER6YjaHGavrq6mV69eJCYmYrfbycrKorKyslWbffv2\nMXhwy9Bxeno6VVVVACQlJdG7d8t2oA6Hg7i4OOrr64GWXxJefvll7rzzzqBeUCh5GnyU7j7CDQO6\n4+zaKaBjrH2fYv39zxiZozCGZIQ4QhER6Yja7Jm73W6cztPbejqdTnbt2tWqTb9+/aioqGD06NFU\nVFTQ0NCA1+ulW7fTq6JVV1fj8/lITEwE4I033uDaa68lIeH8s8FLS0spLS0FYObMmbhcrsCvLsiW\nv/spzZZF/vCBuOLb3h3Navbhfmo+Vkw3XFMewtY97muIMnB2uz2s+bxUKa/Bp5wGn3IaGuHKa1BW\nLJk0aRLFxcWUlZWRlpaGw+HAZjvd6fd4PMydO5eCggJsNhtut5v333+fxx9/vM1z5+XlkZd3eo/v\n2traYIR8wY6eaOZ/thxg+BXduMx3jNrath8PmKtWYFXvxLj7f+NuOglhiv1cXC5X2PJ5KVNeg085\nDT7lNDSCndekpKSA2rVZzB0OB3V1df7XdXV1OByOM9pMnz4dgMbGRtavX+9/Ln78+HFmzpzJxIkT\nSU1NBWDPnj18/vnn3HvvvQA0NTVRWFjI3LlzAwo6HF7/xEODz+SH6c62GwPWwf1Yf/kv+FYmRsbw\nEEcnIiIdWZvFPCUlhQMHDlBTU4PD4aC8vNxfhE85NYvdZrOxYsUKcnJyAPD5fMyaNYvs7GwyMzP9\n7YcOHUpRUZH/9aRJk9p1IW/0mbz6sYeMpBj6J1zWZnvLNFsWh7F3wnbHPRe8m5qIiMiFaLOYR0VF\ncdddd/HEE09gmiY5OTn07duXZcuWkZKSQkZGBtu3b6ekpATDMEhLSyM/Px+A8vJyduzYgdfrpays\nDICCggKSk5NDeU1B91b1YepPNDM+0F75ujfhk60YP5mGER/YMSIiIhfLsCzLCncQF2L//v1f6+ed\nbLa456+7SYzpxG9v7Ndme8t9CPP/ToP+qdju/1W77pXrmVloKK/Bp5wGn3IaGuF6Zq4V4NqwZs8R\n6o77uG1w2z1sy7IwX54PpoltUkG7LuQiInLpUDE/j2bT4pVtbgYkdOaa3jFtH7B1I3xUhfEfd2L0\n6BX6AEVERFAxP68P9nnZ721ifLozoF62uebv0D0eY9SYryE6ERGRFirm52BZFv+9tY6kbtFk9u3W\ndnv3IfiwCmN4HoY9KF/fFxERCYiK+TlsOnCMf3hO8MN0B1G2tnvl1rulYJkYI2/8GqITERE5TcX8\nHF7ZVoezq53rk9tegtVqbsZ69y345jV6Vi4iIl87FfOz2HHoOFtrGhib5qBTVAAz0rduAE8ttutv\nCn1wIiIiX6JifhavbKujW+cobhwYH1B7c+0qiEuAq74d4shERETOpGL+JXs8jVT+6xi3XJnAZfa2\n02O5D8FHGzTxTUREwkbF/Ete2ebmMruNMann35r1FOvdtwALY8R3QxuYiIjIOaiY/5sD3ibe/Wc9\n3x8UT2znqDbbW83NWOvegm9+SxPfREQkbFTM/82K7W6iDIMfpDnabgwtE98O12HL/l5oAxMRETkP\nFfMv1B0/yep/HCE3JQ5Hl8CefZtr3vhi4tuwEEcnIiJybirmX/jrTg+mZfEfAfbKrbpDsHUjxvDv\nauKbiIiElYo54D3RzBu7PIzs151e3aIDOsY/8W2kJr6JiEh4BdSl3Lx5M4sXL8Y0TXJzcxk7dmyr\n9w8dOsT8+fOpr68nNjaWwsJCnE4ne/bsoaioiIaGBmw2G+PGjSMrKwuA5557jt27d2O320lJSeHu\nu+/GHqYe7t8+8dDos/hhetvbnMKpFd/ehPRrMFyJIY5ORETk/NqsnqZpsmjRIh599FGcTicPP/ww\nGRkZ9OnTx99m6dKlZGdnM2rUKLZu3UpJSQmFhYVER0czbdo0evfujdvtZsaMGVx99dXExMQwYsQI\nCgsLAXj22Wd5++23ufHGr39d84aTJq/tdDPs8lj6xXcO7KCPquCwG9uPfxba4ERERALQ5jB7dXU1\nvXr1IjExEbvdTlZWFpWVla3a7Nu3j8GDBwOQnp5OVVUVAElJSfTu3RsAh8NBXFwc9fX1AAwdOhTD\nMDAMg4EDB1JXVxfUCwvUm9WH8TaZ3DY4sF45nFrxzQFDMkIYmYiISGDaLOZutxun83ShczqduN3u\nVm369etHRUUFABUVFTQ0NOD1elu1qa6uxufzkZjYelja5/Oxbt06vvWtb130RVysk80mK3e4GZzY\nlStdXQI6pmXi2waMEVrxTURE2oegVKNJkyZRXFxMWVkZaWlpOBwObLbTvyd4PB7mzp1LQUFBq78H\n+MMf/kBaWhppaWlnPXdpaSmlpaUAzJw5E5fLFYyQAfjr1s9xN/j4PzddicsV2IpvR99cwTHA+YMf\nERXEWMLBbrcHNZ/SQnkNPuU0+JTT0AhXXtss5g6Ho9UQeF1dHQ6H44w206dPB6CxsZH169cTExMD\nwPHjx5k5cyYTJ04kNTW11XF//vOfqa+v5+677z7n5+fl5ZGXl+d/XVtbG8Blta3ZtHipYi8pjsvo\n39UX0Hmt5mbMt/4C6UPx2DpBkGIJF5fLFbR8ymnKa/App8GnnIZGsPOalJQUULs2h9lTUlI4cOAA\nNTU1+Hw+ysvLycho/ay4vr4e0zQBWLFiBTk5OUDLEPqsWbPIzs4mMzOz1TGrV69my5Yt3HfffWf0\n1r8O5f/0csB7ktvSnRhGANucAnxU2TLxTVudiohIO9JmzzwqKoq77rqLJ554AtM0ycnJoW/fvixb\ntoyUlBQyMjLYvn07JSUlGIZBWloa+fn5AJSXl7Njxw68Xi9lZWUAFBQUkJycTFFRET169OCXv/wl\nANdddx3jx48P3ZV+yQf7vPTpHs11fWMDPsZcswriHTBEK76JiEj7YViWZYU7iAuxf//+oJzHtCw8\nDT6cXTsF1N6qq8F8+H9hjJmA7dY7ghJDuGmYLTSU1+BTToNPOQ2NdjvMfqmyGUbAhRzAWvcmgLY6\nFRGRdqfDFvML0bLiWykMvhbD2TPc4YiIiLSiYh6IDyvhiBtb9te/Qp2IiEhbVMwDYK5dBfFOTXwT\nEZF2ScW8DVbtQdi2EWPEdzGiosIdjoiIyBlUzNvQstWpoYlvIiLSbqmYn4fl830x8W0ohrNHuMMR\nERE5KxXz8/FPfNOKbyIi0n6pmJ+Hue7UxDdtdSoiIu2Xivk5tEx824QxUhPfRESkfVMxPwdrnSa+\niYhIZFAxPwvL58N67y0Yci2GQxPfRESkfVMxP5sPK+GIRxPfREQkIqiYn4W59g1IcMHga8MdioiI\nSJtUzL/EOvQ5bN+MMSJPE99ERCQi2ANptHnzZhYvXoxpmuTm5jJ27NhW7x86dIj58+dTX19PbGws\nhYWFOJ1O9uzZQ1FREQ0NDdhsNsaNG0dWVhYANTU1zJkzB6/Xy4ABAygsLMRuDyickDq94ps2VRER\nkcjQZs/cNE0WLVrEI488wuzZs3nvvffYt29fqzZLly4lOzubWbNmMX78eEpKSgCIjo5m2rRp/P73\nv+eRRx5hyZIlHDt2DICXX36ZMWPGMHfuXGJiYnj77bdDcHkXpmXiWylclYHhcIU7HBERkYC0Wcyr\nq6vp1asXiYmJ2O12srKyqKysbNVm3759DB48GID09HSqqqoASEpKonfv3gA4HA7i4uKor6/Hsiy2\nbdtGZmYmAKNGjTrjnGHxYUXLxLeRmvgmIiKRo81i7na7cTqd/tdOpxO3292qTb9+/aioqACgoqKC\nhoYGvF5vqzbV1dX4fD4SExPxer107dqVqC+eSTscjjPOGQ7mmlUtE9+GDA13KCIiIgELykPqSZMm\nUVxcTFlZGWlpaTgcDmy2078neDwe5s6dS0FBQau/D0RpaSmlpaUAzJw5E5crNMPfzQf3U7t9EzG3\n5xPbMzEkn9He2O32kOWzI1Neg085DT7lNDTCldc2i7nD4aCurs7/uq6uDofDcUab6dOnA9DY2Mj6\n9euJiYkB4Pjx48ycOZOJEyeSmpoKQLdu3Th+/DjNzc1ERUXhdrvPOOcpeXl55OXl+V/X1tZe4CUG\nxvzLn8Cw0XDNcBpD9BntjcvlClk+OzLlNfiU0+BTTkMj2HlNSkoKqF2b3eSUlBQOHDhATU0NPp+P\n8vJyMjJabzxSX1+PaZoArFixgpycHAB8Ph+zZs0iOzvb/3wcwDAM0tPT+eCDDwAoKys745xfJ018\nExGRSNZmzzwqKoq77rqLJ554AtM0ycnJoW/fvixbtoyUlBQyMjLYvn07JSUlGIZBWloa+fn5AJSX\nl7Njxw68Xi9lZWUAFBQUkJyczB133MGcOXP405/+RP/+/bnhhhtCeqHntaUC6g9rxTcREYlIhmVZ\nVriDuBD79+8P+jmbZz8Gn+/D9tsiDFvHWShGw2yhobwGn3IafMppaLTbYfZL3ekV327sUIVcREQu\nHSrm61aBYdNWpyIiErE6dDG3fCex3v1i4luCs+0DRERE2qEOXczZUgHeI9iu/164IxEREbloHbqY\nm2tXgaMHpF8T7lBEREQuWoct5lbNgZaJbyO/q4lvIiIS0TpuMX/3TbDZMIZr4puIiES2DlvMSeiB\ncf33NPFNREQiXlA2WolEtpzR4Q5BREQkKDpuz1xEROQSoWIuIiIS4VTMRUREIpyKuYiISIRTMRcR\nEYlwKuYiIiIRTsVcREQkwqmYi4iIRDjDsiwr3EGIiIjIxVPPvAObMWNGuEO4JCmvwaecBp9yGhrh\nyquKuYiISIRTMRcREYlwKuYdWF5eXrhDuCQpr8GnnAafchoa4cqrJsCJiIhEOPXMRUREIlyH3c+8\no6mtrWXevHkcPnwYwzDIy8tj9OjRHD16lNmzZ3Po0CF69OjB/fffT2xsbLjDjSimaTJjxgwcDgcz\nZsygpqaGOXPm4PV6GTBgAIWFhdjt+qd2IY4dO8aCBQv47LPPMAyDKVOmkJSUpHv1K3jttdd4++23\nMQyDvn37MnXqVA4fPqx79QI9//zzbNy4kbi4OJ555hmAc/4/alkWixcvZtOmTXTu3JmpU6cyYMCA\nkMQV9fjjjz8ekjNLu3LixAlSU1OZOHEi2dnZLFy4kCFDhvDGG2/Qt29f7r//fjweDx9++CFXXXVV\nuMONKH/729/w+Xz4fD5GjBjBwoULycnJ4Z577uGjjz7C4/GQkpIS7jAjygsvvMCQIUOYOnUqeXl5\ndO3alZUrV+pevUhut5sXXniBWbNmMXr0aMrLy/H5fKxatUr36gWKiYkhJyeHyspKbrrpJgCWL19+\n1ntz06ZNbN68mSeffJL+/ftTXFxMbm5uSOLSMHsHkZCQ4P+NsEuXLlx++eW43W4qKyu5/vrrAbj+\n+uuprKwMZ5gRp66ujo0bN/r/gVqWxbZt28jMzARg1KhRyukFOn78ODt27OCGG24AwG63ExMTo3v1\nKzJNk6amJpqbm2lqaiI+Pl736kX45je/ecaI0LnuzaqqKrKzszEMg9TUVI4dO4bH4wlJXBpP6YBq\namr49NNPGThwIEeOHCEhIQGA+Ph4jhw5EuboIsuSJUu48847aWhoAMDr9dK1a1eioqIAcDgcuN3u\ncIYYcWpqaujevTvPP/88e/fuZcCAAUyePFn36lfgcDi45ZZbmDJlCtHR0Vx99dUMGDBA92qQnOve\ndLvduFwufzun04nb7fa3DSb1zDuYxsZGnnnmGSZPnkzXrl1bvWcYBoZhhCmyyLNhwwbi4uJC9gys\no2pububTTz/lxhtv5He/+x2dO3dm5cqVrdroXr0wR48epbKyknnz5rFw4UIaGxvZvHlzuMO6JIXr\n3lTPvAPx+Xw888wzjBw5kuuuuw6AuLg4PB4PCQkJeDweunfvHuYoI8fHH39MVVUVmzZtoqmpiYaG\nBpYsWcLx48dpbm4mKioKt9uNw+EId6gRxel04nQ6GTRoEACZmZmsXLlS9+pX8NFHH9GzZ09/zq67\n7jo+/vhj3atBcq570+FwUFtb629XV1cXshyrZ95BWJbFggULuPzyy7n55pv9f5+RkcGaNWsAWLNm\nDcOGDQtXiBHnxz/+MQsWLGDevHncd999DB48mHvvvZf09HQ++OADAMrKysjIyAhzpJElPj4ep9PJ\n/v37gZZC1KdPH92rX4HL5WLXrl2cOHECy7L8OdW9GhznujczMjJYu3YtlmXxySef0LVr15AMsYMW\njekwdu7cyWOPPcYVV1zhHwKaOHEigwYNYvbs2dTW1urrPl/Btm3bePXVV5kxYwYHDx5kzpw5HD16\nlP79+1NYWEinTp3CHWJE2bNnDwsWLMDn89GzZ0+mTp2KZVm6V7+C5cuXU15eTlRUFMnJyfzsZz/D\n7XbrXr1Ac+bMYfv27Xi9XuLi4pgwYQLDhg07671pWRaLFi1iy5YtREdHM3Xq1JB9W0DFXEREJMJp\nmF1ERCTCqZiLiIhEOBVzERGRCKdiLiIiEuFUzEVERCKcirlIBzJhwgQ+//zzcIdxhuXLl/Pcc8+F\nOwyRiKUV4ETCpKCggMOHD2Oznf6detSoUeTn54cxKhGJRCrmImH00EMPaRvPIDu1PKlIR6JiLtIO\nlZWVsXr1apKTk1m7di0JCQnk5+czZMgQoGU3pqKiInbu3El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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.1 # scale for random parameter initialisation\n",
- "learning_rate = 0.2 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine + softmax model\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init),\n",
- " SoftmaxLayer()\n",
- "])\n",
- "\n",
- "# Initialise a cross entropy error object\n",
- "error = CrossEntropyError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `learning_rate = 0.5`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 3.5s to complete\n",
- " error(train)=2.79e-01, acc(train)=9.20e-01, error(valid)=2.74e-01, acc(valid)=9.23e-01\n",
- "Epoch 10: 3.7s to complete\n",
- " error(train)=2.68e-01, acc(train)=9.24e-01, error(valid)=2.72e-01, acc(valid)=9.26e-01\n",
- "Epoch 15: 3.9s to complete\n",
- " error(train)=2.55e-01, acc(train)=9.28e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 20: 3.5s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n",
- "Epoch 25: 4.3s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.73e-01, acc(valid)=9.25e-01\n",
- "Epoch 30: 3.5s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.70e-01, acc(valid)=9.27e-01\n",
- "Epoch 35: 3.2s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.69e-01, acc(valid)=9.27e-01\n",
- "Epoch 40: 4.1s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.72e-01, acc(valid)=9.26e-01\n",
- "Epoch 45: 4.3s to complete\n",
- " error(train)=2.36e-01, acc(train)=9.35e-01, error(valid)=2.66e-01, acc(valid)=9.29e-01\n",
- "Epoch 50: 3.7s to complete\n",
- " error(train)=2.38e-01, acc(train)=9.33e-01, error(valid)=2.69e-01, acc(valid)=9.28e-01\n",
- "Epoch 55: 3.9s to complete\n",
- " error(train)=2.36e-01, acc(train)=9.34e-01, error(valid)=2.68e-01, acc(valid)=9.26e-01\n",
- "Epoch 60: 4.0s to complete\n",
- " error(train)=2.46e-01, acc(train)=9.29e-01, error(valid)=2.81e-01, acc(valid)=9.22e-01\n",
- "Epoch 65: 4.1s to complete\n",
- " error(train)=2.33e-01, acc(train)=9.35e-01, error(valid)=2.70e-01, acc(valid)=9.28e-01\n",
- "Epoch 70: 3.6s to complete\n",
- " error(train)=2.35e-01, acc(train)=9.36e-01, error(valid)=2.75e-01, acc(valid)=9.27e-01\n",
- "Epoch 75: 4.4s to complete\n",
- " error(train)=2.31e-01, acc(train)=9.36e-01, error(valid)=2.70e-01, acc(valid)=9.26e-01\n",
- "Epoch 80: 3.6s to complete\n",
- " error(train)=2.35e-01, acc(train)=9.34e-01, error(valid)=2.76e-01, acc(valid)=9.25e-01\n",
- "Epoch 85: 4.0s to complete\n",
- " error(train)=2.32e-01, acc(train)=9.35e-01, error(valid)=2.75e-01, acc(valid)=9.26e-01\n",
- "Epoch 90: 3.6s to complete\n",
- " error(train)=2.29e-01, acc(train)=9.37e-01, error(valid)=2.74e-01, acc(valid)=9.26e-01\n",
- "Epoch 95: 4.2s to complete\n",
- " error(train)=2.31e-01, acc(train)=9.35e-01, error(valid)=2.76e-01, acc(valid)=9.27e-01\n",
- "Epoch 100: 3.4s to complete\n",
- " error(train)=2.31e-01, acc(train)=9.36e-01, error(valid)=2.77e-01, acc(valid)=9.27e-01\n"
- ]
- },
- {
- "data": {
- "image/png": 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pecrtQv0sPDohiQeWHuTRlVk8PSm5xbNDuYtjLuYCEkJ92+weYEywDzed0/hy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\ndCjh4eEsWLCA3NxceYzmBhw6dIg1a9YwdepUzpw5w8KFCyk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XHP0DMeIWhGvtL5ESQqDd+xg0aWbZkCXhBPp7s5CfL4au16A99wbW3kJV9OwL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\nmwAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.1 # scale for random parameter initialisation\n",
- "learning_rate = 0.5 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine + softmax model\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init),\n",
- " SoftmaxLayer()\n",
- "])\n",
- "\n",
- "# Initialise a cross entropy error object\n",
- "error = CrossEntropyError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "|`learning_rate`| Final `error(train)` | Final `error(valid)` |\n",
- "|---------------|----------------------|----------------------|\n",
- "| 0.05 | $2.53\\times 10^{-1}$ | $2.59\\times 10^{-1}$|\n",
- "| 0.1 | $2.43\\times 10^{-1}$ | $2.59\\times 10^{-1}$|\n",
- "| 0.2 | $2.35\\times 10^{-1}$ | $2.63\\times 10^{-1}$|\n",
- "| 0.5 | $2.31\\times 10^{-1}$ | $2.77\\times 10^{-1}$|\n",
- "\n",
- "\n",
- "Increasing the learning rate, as would be expected, increase the speed of learning, with the final training error reached monotonically decreasing over the learning rates tested as the learning rate was increased. Note however the validation set error increases for larger learning rates - this suggests the model is overfitting to the data, with the larger learning rates causing the model to begin overfitting sooner - we could have afforded to halt learning earlier in these cases when there was no further improvement in the validation set error. Notice also the error curves for the largest learning rate value are much more noisy suggesting learning is becoming quite unstable with this large a step size, with a lot of the gradient descent steps overshooting and causing the error function value to increase.\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Optional extra: more efficient softmax gradient evaluation\n",
- "\n",
- "In the lectures you were shown that for certain combinations of error function and final output layers, that the expressions for the gradients take particularly simple forms. \n",
- "\n",
- "In particular it can be shown that the combinations of \n",
- "\n",
- " * logistic sigmoid output layer and binary cross entropy error function\n",
- " * softmax output layer and cross entropy error function\n",
- " \n",
- "lead to particularly simple forms for the gradients of the error function with respect to the inputs to the final layer. In particular for the latter softmax and cross entropy error function case we have that\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_k = \\textrm{Softmax}_k\\lpa\\vct{x}^{(b)}\\rpa = \\frac{\\exp(x^{(b)}_k)}{\\sum_{d=1}^D \\lbr \\exp(x^{(b)}_d) \\rbr}\n",
- " \\qquad\n",
- " E^{(b)} = \\textrm{CrossEntropy}\\lpa\\vct{y}^{(b)},\\,\\vct{t}^{(b)}\\rpa = -\\sum_{d=1}^D \\lbr t^{(b)}_d \\log(y^{(b)}_d) \\rbr\n",
- "\\end{equation}\n",
- "\n",
- "and it can be shown (this is an instructive mathematical exercise if you want a challenge!) that\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E^{(b)}}{x^{(b)}_d} = y^{(b)}_d - t^{(b)}_d.\n",
- "\\end{equation}\n",
- "\n",
- "The combination of `CrossEntropyError` and `SoftmaxLayer` used to train the model above calculate this gradient less directly by first calculating the gradient of the error with respect to the model outputs in `CrossEntropyError.grad` and then back-propagating this gradient to the inputs of the softmax layer using `SoftmaxLayer.bprop`.\n",
- "\n",
- "Rather than computing the gradient in two steps like this we can instead wrap the softmax transformation in to the definition of the error function and make use of the simpler gradient expression above. More explicitly we define an error function as follows\n",
- "\n",
- "\\begin{equation}\n",
- " E^{(b)} = \\textrm{CrossEntropySoftmax}\\lpa\\vct{y}^{(b)},\\,\\vct{t}^{(b)}\\rpa = -\\sum_{d=1}^D \\lbr t^{(b)}_d \\log\\lsb\\textrm{Softmax}_d\\lpa \\vct{y}^{(b)}\\rpa\\rsb\\rbr\n",
- "\\end{equation}\n",
- "\n",
- "with corresponding gradient\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E^{(b)}}{y^{(b)}_d} = \\textrm{Softmax}_d\\lpa \\vct{y}^{(b)}\\rpa - t^{(b)}_d.\n",
- "\\end{equation}\n",
- "\n",
- "The final layer of the model will then be an affine transformation which produces unbounded output values corresponding to the logarithms of the unnormalised predicted class probabilities. An implementation of this error function is provided in `CrossEntropySoftmaxError`. The cell below sets up a model with a single affine transformation layer and trains it on MNIST using this new cost. If you run it with equivalent hyperparameters to one of your runs with the alternative formulation above you should get identical error and classification curves (other than floating point error) but with a minor improvement in training speed.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 0.4s to complete\n",
- " error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
- "Epoch 10: 0.4s to complete\n",
- " error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
- "Epoch 15: 0.4s to complete\n",
- " error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
- "Epoch 20: 0.4s to complete\n",
- " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 25: 0.4s to complete\n",
- " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 30: 0.4s to complete\n",
- " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
- "Epoch 35: 0.4s to complete\n",
- " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 40: 0.4s to complete\n",
- " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 45: 0.4s to complete\n",
- " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 50: 0.4s to complete\n",
- " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 55: 0.4s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 60: 0.4s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
- "Epoch 65: 0.4s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 70: 0.4s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n",
- "Epoch 75: 0.4s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 80: 0.4s to complete\n",
- " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 85: 0.4s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 90: 0.4s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 95: 0.4s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 100: 0.4s to complete\n",
- " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
- ]
- },
- {
- "data": {
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DO9LGzz45yOf7KkN2LGWzoR7+Eerme9DXrED731+i+8yfXlUIIUT7IUnbZO5I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\n9Ry31tEr1kmYrWvHxExd6v/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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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MHIqWfhqER4Cug1IHvutHPvb9rED3HPlYV0eWP1gPoJEA6rUg6gzB3u9aMHVa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\ntGj/LmsrhBCi75KgPwl7bS4KdtuZNDS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rw+12U1JSgsPh6NDG5XLh8XgAeOONN0hLS+t0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\nejS33norwcHBbN++nQsXLuDxePjmm284dOiQj/ZEpO/TEb1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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "init_scale = 0.1 # scale for random parameter initialisation\n",
- "learning_rate = 0.1 # learning rate for gradient descent\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "\n",
- "# Reset random number generator and data provider states on each run\n",
- "# to ensure reproducibility of results\n",
- "rng.seed(seed)\n",
- "train_data.reset()\n",
- "valid_data.reset()\n",
- "\n",
- "# Alter data-provider batch size\n",
- "train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "# Create a parameter initialiser which will sample random uniform values\n",
- "# from [-init_scale, init_scale]\n",
- "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- "# Create affine model (outputs are logs of unnormalised class probabilities)\n",
- "model = SingleLayerModel(\n",
- " AffineLayer(input_dim, output_dim, param_init, param_init)\n",
- ")\n",
- "\n",
- "# Initialise the error object\n",
- "error = CrossEntropySoftmaxError()\n",
- "\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "This gives exactly the same training curves (and error / accuracy values over training) as the two runs with equivalent parameters above (second `init_scale` experiment and second `learning_rate` experiment).\n",
- "\n",
- "\n",
- "\n",
- "The times per epoch seems to be slightly lower on average (0.20s compared to 0.22s) suggesting the reformulation gives a small efficiency gain (though this will become less apparent in deeper architectures as the benefit only applies to the final layer).\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 2: training deeper models on MNIST\n",
- "\n",
- "We are now going to investigate using deeper multiple-layer model archictures for the MNIST classification task. You should experiment with training models with two to five `AffineLayer` transformations interleaved with `SigmoidLayer` nonlinear transformations. Intermediate hidden layers between the input and output should have a dimension of 100. For example the `layers` definition of a model with two `AffineLayer` transformations would be\n",
- "\n",
- "```python\n",
- "layers = [\n",
- " AffineLayer(input_dim, 100),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(100, output_dim),\n",
- " SoftmaxLayer()\n",
- "]\n",
- "```\n",
- "\n",
- "If you read through the extension to the first exercise you may wish to use the `CrossEntropySoftmaxError` without the final `SoftmaxLayer`.\n",
- "\n",
- "Use the code from the first exercise as a starting point and start with training hyperparameters which gave reasonable performance for the shallow architecture trained previously.\n",
- "\n",
- "Some questions to investigate:\n",
- "\n",
- " * How does increasing the number of layers affect the model's performance on the training data set? And on the validation data set?\n",
- " * Do deeper models seem to be harder or easier to train (e.g. in terms of ease of choosing training hyperparameters to give good final performance and/or quick convergence)?\n",
- " * Do the models seem to be sensitive to the choice of the parameter initialisation range? Can you think of any reasons for why setting individual parameter initialisation scales for each `AffineLayer` in a model might be useful? Can you come up with (or find) any heuristics for setting the parameter initialisation scales?\n",
- " \n",
- "You do not need to come up with explanations for all of these (though if you can that's great!), they are meant as prompts to get you thinking about the various issues involved in training multiple-layer models. \n",
- "\n",
- "You may wish to start with shorter pilot training runs (by decreasing the number of training epochs) for each of the model architectures to get an initial idea of appropriate hyperparameter settings before doing one or two longer training runs to assess the final performance of the architectures."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "# disable logging by setting handler to dummy object\n",
- "logger.handlers = [logging.NullHandler()]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Models with two affine layers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.10\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
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2LHNyUe7g3ztprTtlGCqSoXmSoXk99Uw72Mx0+ZIz7R5E9U5BffU7lxuUrEdveQvjwG4Y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\nSbx/+SMc/wSCQ3wLlMSMGi0XogkhhBB9ZQ4xMnN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- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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BSLxq2+hL59FZ69DZG6G2Bnr1Qz01HzU6ERUgzxKIzkuauRBuVtvgICO/TOJV\nb5I2HNTlbsPx6X/Dob3g44u6Y7xz3/B+t8nacOEVpJkL4Qbfj1fdeuY4NfUOiVe9QbqqAr1tAzpr\nPWXFBRBuQT34BCpxKqprhLvLE6JdyU8MIdpRc/GqyQO7MTEmUOJVW0mfzkNvWovO3epcG37bUMLm\nLqCy3yBZGy68lnznC3GLXS9eNbFPV3pHR1JUVOTuMjs03dCA3r0NvWktnDwGAYGocZNRSWmonr0J\ntFqpkjEUXkyauRC3SJnNTub34lW7+Em86o3SJYXozV+gt6ZDZTlE9kQ99k+ouyajugS7uzwhOgxp\n5kK4kMSrtp3WGo4ewMhcC3t3Ov9wWAKmyWlw+3BZGy5EM6SZC+ECEq/adtpWg96R5YxZvXgWQkJR\n9/wINXEayhrp7vKE6NCkmQtxk+yGZtd5Z7CLxKvePH3xHDpzLXrHJrDVQu/+qDkvokZPQPnLL0JC\ntIY0cyFu0MXKejbklbHpRDmlNgdmiVe9YdrhgP25zqn0w1+Dry8q4W5UUir0HShP9Qtxg6SZC9EK\n14pXndo/jDskXrXVdGU5ems6evMXUFIIZitq+pOou6eiuoa7uzwhPJY0cyGu40xZHel5ZWSddMar\nRoZIvOrN0CePOafSc7eC3Q63D8P06DMw/E6Uj4+7yxPC40kzF+IHbHaDbacrSM8r42iRTeJVb5Ju\nqEfnbnM+0HbqOAQEOa/AJ6Wionu5uzwhOpVWNfN9+/bxwQcfYBgGycnJTJ8+vcnrhYWFvPvuu1RU\nVBASEsL8+fOxWCyNr9fU1LBw4UJGjx7NvHnzADhx4gTLli2jvr6ekSNH8vTTT8t9MuE2349X3XKq\nglq70RivmtS3K10lXrXVdHEBevN69NYNUFUBUTGox59FjU1CBXVxd3lCdEot/oQyDIP333+fRYsW\nYbFYePXVV0lISCAmJqbxmJUrV5KYmMikSZM4ePAgq1atYv78+Y2vr169mvj4+Cbnfe+993j22WcZ\nMGAAv//979m3bx8jR4504UcTomXNxatO6B3K1LhwiVe9AVprOPy184G2r3OdfzjiTkxJaXD7MBlH\nIW6xFpt5Xl4eUVFRREY613mOGzeO3NzcJs383LlzzJ49G4DBgwezdOnSxtdOnDhBeXk5I0aMID8/\nH4DS0lJqa2sZOHAgAImJieTm5kozF+2iuXjVOPN38arB/nIPt7V0bQ06e5Nz3/BL5yCkK2raDNTE\ne1GWbu6hp3WLAAAfoklEQVQuTwiv0WIzLykpaTJlbrFYOH78eJNjevfuTU5ODqmpqeTk5FBbW0tl\nZSXBwcF89NFHzJ8/nwMHDlz3nCUlJa74PEJcU3Pxqsn9wpgi8ao3TJ8/g85ai96RBXW1zuVkc3+G\nShiP8pPleUK0N5fcCJw1axYrVqwgKyuL+Ph4zGYzJpOJ9PR0Ro4c2aRx36iMjAwyMjIAWLJkCVar\n1RUlA+Dr6+vS83mjjj6Ghtbkninjs4OX2HqiBLuhGdajK0+N6c3kAVaC/Nx/Fd7Rx/AK7bBTl7OV\nmnV/p+HgHvDzJ3BCCl3unYHfgEFurc1TxrCjk3FsO3eNYYvN3Gw2U1xc3Ph1cXExZrP5qmNefvll\nAGw2Gzt37iQ4OJhjx45x+PBh0tPTsdls2O12AgMDSU1NbfGcV6SkpJCSktL4tSt3l7JarbJbVRt1\n1DFsLl41bWA4Kd+LV60uL6XazXVCxx3DK3RFKXrrBufa8NIiMHdDzXgKNWEKDaFdKQdwc/0dfQw9\nhYxj27l6DKOjo1t1XIvNPC4ujosXL1JQUIDZbCY7O5sFCxY0OebKU+wmk4k1a9aQlJQE0OS4rKws\n8vPzeeKJJwAICgri2LFjDBgwgC1btjBt2rRWfzghmnOteNWnRnZnTIzEq94IrTWcOOpcG75rOzjs\nMGgEpsefhWEJKJP7ZzSEEN9psZn7+Pgwd+5cFi9ejGEYJCUlERsby+rVq4mLiyMhIYFDhw6xatUq\nlFLEx8c3Lj+7nmeeeYbly5dTX1/PiBEj5OE3cdOuFa86pX8YkSFy//ZG6Po6dO5W577hZ/IhMMi5\n0cmkVFSPmJZPIIRwC6W11u4u4kZcuHDBZeeSKaW2c9cYXolXTc8r44CHx6t2hO9DXXjJuTZ8WwZU\nV0J0L1RSKmrsJFRgx18b3hHGsDOQcWy7DjvNLkRHIvGqrqMNAw7tc64NP7ALlIIRY537hg8cImvD\nhfAg0sxFh1fbYLD9jMSruoquqXKuDc9cBwUXIDQMlfoIKnEayixPMgvhiaSZiw5J4lVdT587hc5c\nh/4qE+rrIO521AM/Ro0ah/KTWQ0hPJn8RBQdSvPxql2ZGhcm8ao3QdvtsO8r51T6sW/Azx91ZyIq\nKQ3VO87d5QkhXESauXA7rTWHCmpJz5d4VVfR5aXoLV+it3wBZSVgjUQ9PAc1PgUV0tXd5QkhXEya\nuXCbMpudTSfK2ZBXzoVKiVdtK6015B92TqXvznauDR8yCtOTL8DQUbI2XIhOTJq5aFeG1uy7WE16\nXjk55ypxaIjvFsQjQ3owrlcogb4S7HKjdF0dOmezc9/wsychKNi5rGxSKiqydctahBCeTZq5aBdF\nNQ1k5Jez8XvxqvfdFsGU/uHEfhuvKm6MLrj43drwmiro2Rs163nUmEmoAJnZEMKbSDMXt8yVeNX0\nvDL2SryqS2jDgG/2Oh9oO7gbTCbUyLtQSWkwYJA8ICiEl5JmLlxO4lVdT1dXobdnOPcNL7wEYRGo\n+x5FJd6DCr/5XQmFEJ2DNHPhEs3Fqyb0DGFKnOfFq3Yk+uxJ52YnO7Ogvh76D0JNfxI16i6Ur6wN\nF0I4STMXbXKiqJpPdl2WeFUX0vYG9J4dzoS2vEPg7++8Dz4pFdWrn7vLE0J0QNLMxQ27Ol5VMTY2\nhClxEq/aFrqs+Nu14V9CeSl0i0I9Mte5Njw4xN3lCSE6MGnmolWuFa86/+6+3NndR+JVb5LWmvpv\n9mJ8+t/ovTvAMGDIHZiS0mDwSJRJHhIUQrRMfgKL66qqc7D5VDPxqv3DuN0aRLdu3WTLxJug62zo\nnVnoTWspPX8augSjku9HTbwX1b2Hu8sTQngYaebiKo3xqnllZJ+VeFVX0pcvoLPWobdvhNpqiOlL\n6PO/pHrQHagAWW8vhLg50sxFI4lXvTW04YADezAyP4dv9oKPj3OnsslpEBdPl27dqJHZDSFEG0gz\n93IOQ/P1pabxqoO+jVcd3yuUAIlXvWm6quLbteHroegyhJtRDzyOunsqKtzs7vKEEJ2INHMvJfGq\nt44+ne9cG56zBRrqYeBgTA89BSPGonzl/3JCCNeTnyxepLl41RESr+oS2t6A3p3t3Owk/wj4B6Du\nmuzc8CSmj7vLE0J0ctLMvUBz8aoPD7aQEifxqm2lS4rQW75wrg2vLIfu0ahH56HGJaO6yNpwIUT7\nkGbeSUm86q2jtYZjBzE2rYV9X4HWMGw0pkmpMGiErA0XQrQ7aeadzOmyOjbklTWJV31yuJXJEq/a\nZtpWi/4q0xmzeuEMBIeipjzoXBveLcrd5QkhvJg0806gtsFg22lnsMv341Wn9g9naKTEq7aVvnQO\nnbUenb0RamugVxxqzgLU6LtR/vKwoBDC/aSZe6jvx6tuPlWB7dt41bmjupPUt6vEq7aRNhywf5dz\n3/BD+8DHF5Uw3rlveL/bZN9wIUSHIj/xPUxL8arSZNpGV1agt21Ab14PxQUQbnFuOXr3FFTXCHeX\nJ4QQzZJm7gGaj1cNlHhVF9KnjqM3rUXnbgV7A9w2FNMjc2HEGJSPjK8QomOTZt6BXStedWr/cPpJ\nvGqb6YYG9K5tzrXhJ49BQCBqQgpqUhqqZy93lyeEEK0mzbyDkXjVW08XF6I3r0dv2+BcGx7VE/XY\nT1B3JaG6BLu7PCGEuGHSzDuIH8ardg3w4f7bzaTEhUm8qgtoreHIfucDbftynH84fLRz3/D44fKs\ngRDCo0kzd6NrxavOGdmdOyVe1SV0bc13a8MvnoWQUNS0HznXhlu6u7s8IYRwCWnmbiDxqreevnjW\nudlJdibU1UKfAain/w9q9ASUn4yxEKJzkWbeTuodBjvOVLIhv7xJvOrUuHBGRQdLvKoLaIcDvs5x\nTqUf2Q++vs5gl6Q0VN+B7i5PCCFumVY183379vHBBx9gGAbJyclMnz69yeuFhYW8++67VFRUEBIS\nwvz587FYLBQWFvLmm29iGAYOh4Np06YxdepUAH7zm99QWlqKv7/zKmnRokWEhYW5+OO5n8Sr3nq6\nogy9NR295QsoKQKzFfWjWc59w0M73/eUEEL8UIvN3DAM3n//fRYtWoTFYuHVV18lISGBmJiYxmNW\nrlxJYmIikyZN4uDBg6xatYr58+cTERHB66+/jp+fHzabjZdeeomEhATMZjMACxYsIC4u7tZ9Ojdp\nLl71rtgQpki8qstoreHkMedU+q5tYLdD/HBMj/0Eho2WteFCCK/SYjPPy8sjKiqKyMhIAMaNG0du\nbm6TZn7u3Dlmz54NwODBg1m6dKnz5L7fnb6hoQHDMFxafEdyJV41Pa+MLacqJV71FtEN9ejcrehN\na+F0HgQGoe6+x7lveI9Yd5cnhBBu0WKHKSkpwWKxNH5tsVg4fvx4k2N69+5NTk4Oqamp5OTkUFtb\nS2VlJaGhoRQVFbFkyRIuXbrEk08+2XhVDrB8+XJMJhNjxozhoYce8sjlQT+MVw34Nl51isSrupQu\nuoze/AV6WzpUVUKPWNTjP0XdNQkV2MXd5QkhhFu55HJx1qxZrFixgqysLOLj4zGbzZi+3dPZarXy\n5ptvUlJSwtKlSxk7dizh4eEsWLAAs9lMbW0tb731Flu2bGHixIlXnTsjI4OMjAwAlixZgtVqdUXJ\ngHPm4GbOp7Xm6wsV/O/BS2QeL6beYXBb9xBemRxDysBuhAR4z1X4zY5ha2jDoH7/LmrX/T/qdmcD\nEHBnIl3unYHf0Ds6zS9Kt3IMvYWMoWvIOLadu8awxa5jNpspLi5u/Lq4uLjJ1fWVY15++WUAbDYb\nO3fuJDg4+KpjYmNjOXLkCGPHjm08R1BQEBMmTCAvL6/ZZp6SkkJKSkrj10VFRTfw8a7ParXe0Pma\nj1ft2iRe1VZZhq3SZSV2eDc6hq2ha6rROzY514ZfPg+hYahpD6ESp2G3dKMC4Hvfk57uVoyht5Ex\ndA0Zx7Zz9RhGR0e36rgWm3lcXBwXL16koKAAs9lMdnY2CxYsaHLMlafYTSYTa9asISkpCXA2/tDQ\nUPz9/amqquLo0aPcd999OBwOqqur6dq1K3a7nd27dzN06NCb+Ji3nsSrth99/gw6ay16RybU2Zxb\njc77GeqOCSg/efJfCCGupcVm7uPjw9y5c1m8eDGGYZCUlERsbCyrV68mLi6OhIQEDh06xKpVq1BK\nER8fz7x58wA4f/48H330EUoptNbcf//99OrVC5vNxuLFi3E4HBiGwdChQ5tcfXcEhdUNbDxRTkZe\nGYU138WrTokLI0biVV1G2+3w9U6MzHVw9AD4+qHuTHQ+0NZngLvLE0IIj6C01trdRdyICxcuuOxc\nP5wOsRua3PNVbPg2XlVrGN4jmKlxYdwZE4qfT+e4R+tKNzulpCtK0VvS0Zu/gLJisHRHTboXNX4K\nKrTrLai045KpzbaTMXQNGce267DT7N5A4lXbh9YaThx17hu+ezs47DBoJKYnn4Ohd6BMsjZcCCFu\nhtc283qHQfqRAv6x71xjvOroniFMkXhVl9P1deicLc59w8+cgKAuzqvwSamoqJ7uLk8IITye1zbz\nX6afJr+kjqgQP2YN70ZSv64Sr+piuvASOms9ensGVFdCz96oJ55DjZ2ECgxyd3lCCNFpeG0zf2SI\nlR6WCHoFNUi8qgtpw4BDe50PtB3YBUrByLGYku6DgYM7zdpwIYToSLy2md8VG4rVGi4Pe7iIrqlC\nb9+IzloHBRehazgqbaYzatUsIRRCCHEreW0zF67RcCoPY81/ob/Kgvo6iLsd9cDjqDvGoXzltoUQ\nQrQHaebihmm7Hb33K3Tm55QcPwR+/qgxE51rw3t1vl3whBCio5NmLlpNl5Wgt3yJ3vIllJeANZKQ\nOf9MzYi7UMGh7i5PCCG8ljRzcV1aa8g77Nw3fE82OBwwZBSm2S/AkFEEd4+kVp47EEIIt5JmLpql\n62zonZudm52cOwldglFJ9znXh0e2LpFICCFE+5BmLprQBRe+WxteUw0xfVCzXnDeEw8IdHd5Qggh\nmiHNXDjXhn+zB2PTWvhmD5hMqFHjUElp0D9e1oYLIUQHJ83ci+nqSvT2DHTWeii8BGERqPseQyVO\nRYVb3F2eEEKIVpJm7oX0mXx05jp0zmaor4cBg1A/moUaOVbWhgshhAeSZu4ltL0BvTvbmdCWdxj8\nA1BjJqGS0lCxfd1dnhBCiDaQZt7J6dLib9eGfwEVZdAtCjVzHmpcMio4xN3lCSGEcAFp5p2Q1hqO\nf+PcN3zvDtAahtyBaXIaDBqJMpncXaIQQggXkmbeiWhb7bdrw9fC+dPQJQSV8qBzbXi3KHeXJ4QQ\n4haRZt4J6Evn0Vnr0NkbobYGYvuinpqPGp2ICghwd3lCCCFuMWnmHkobDjiw27k2/NBe8PF17lSW\nlObcuUzWhgshhNeQZu5hdFUFetsG59rw4gIIN6MefNy5b3hYhLvLE0II4QbSzD2EPp3n3OwkZys0\n1MPAIZgeeRqGj0H5yj+jEEJ4M+kCHZhuaEDv3u58oO3EUQgIRI2b7Fwb3rO3u8sTQgjRQUgz74B0\nSSF685forV9CZTlE9kQ99k+ouyajugS7uzwhhBAdjDTzDkJrDUcPYGSuhX07nWvDh43GlJQG8cNl\nbbgQQohrkmbuZtpWg96R5ZxKv3gWgkNRU6ajJk6TteFCCCFaRZq5m+iL55wPtO3YBLZa6N0fNedF\n1OgJKH9ZGy6EEKL1pJm3I204YH+uc2344a/B1xeVMMG5NrzvQFkbLoQQ4qZIM28HurL8u7XhJYUQ\nYUVNfxJ191RU13B3lyeEEMLDSTO/hfTJ4+jMz9G528DeALcPw/ToPOfacB8fd5cnhBCik5Bm7mK6\noR6du835QNup4xAQhJowBZWUioru5e7yhBBCdELSzF1EFxeiN69Hb02HqgqIikH9+CfOteFBXdxd\nnhBCiE5MmnkbaK3h8NcYmevg6xznHw6/07lv+O3D5IE2IYQQ7aJVzXzfvn188MEHGIZBcnIy06dP\nb/J6YWEh7777LhUVFYSEhDB//nwsFguFhYW8+eabGIaBw+Fg2rRpTJ06FYATJ06wbNky6uvrGTly\nJE8//bTHND9dW4PesQmduQ4unYOQrqhpM1AT70VZurm7PCGEEF6mxWZuGAbvv/8+ixYtwmKx8Oqr\nr5KQkEBMTEzjMStXriQxMZFJkyZx8OBBVq1axfz584mIiOD111/Hz88Pm83GSy+9REJCAmazmffe\ne49nn32WAQMG8Pvf/559+/YxcuTIW/ph20pfOIPOXIfekQl1tc7lZHN/hkoYj/Lzd3d5QgghvFSL\nzTwvL4+oqCgiIyMBGDduHLm5uU2a+blz55g9ezYAgwcPZunSpc6Tf283r4aGBgzDAKC0tJTa2loG\nDhwIQGJiIrm5uR2ymWuHA77e6VwbfvQA+PqhRt/t3Oyk7wB3lyeEEEK03MxLSkqwWCyNX1ssFo4f\nP97kmN69e5OTk0Nqaio5OTnU1tZSWVlJaGgoRUVFLFmyhEuXLvHkk09iNpvJz8+/6pwlJSUu/Fht\npyvK0FvT0Zu/gNIiMHdDzZjtfDI9NMzd5QkhhBCNXPIA3KxZs1ixYgVZWVnEx8djNpsxfbsxiNVq\n5c0336SkpISlS5cyduzYGzp3RkYGGRkZACxZsgSr1eqKkgHnzMH3z6e1puHYN9Su/zu27ZvA3oD/\n8NEEPfsyAQnjZW14M344huLGyRi2nYyha8g4tp27xrDFZm42mykuLm78uri4GLPZfNUxL7/8MgA2\nm42dO3cSHBx81TGxsbEcOXKE2267rcVzXpGSkkJKSkrj10VFRa34WK1jtVopKipC19ehc7c6H2g7\nnQeBQajEe1CTUnH0iKEKqCotddn7diZXxlDcPBnDtpMxdA0Zx7Zz9RhGR0e36rgW99WMi4vj4sWL\nFBQUYLfbyc7OJiEhockxFRUVjffD16xZQ1JSEuBs0vX19QBUVVVx9OhRoqOjiYiIICgoiGPHjqG1\nZsuWLVedsz04Ci5i/L8PMX4+F/3hH6C+DvXETzEt/QDTj3+C6hHT8kmEEEIIN2vxytzHx4e5c+ey\nePFiDMMgKSmJ2NhYVq9eTVxcHAkJCRw6dIhVq1ahlCI+Pp558+YBcP78eT766COUUmituf/+++nV\ny5mC9swzz7B8+XLq6+sZMWJEuz/8Zrz3FkW5W0ApGDHWuTZ84BCPWR4nhBBCXKG01trdRdyICxcu\nuOQ8xv+soktQILUJiSiz3CO6WTIt13Yyhm0nY+gaMo5t565pdq9NgDM9+DghVis2+cYVQgjh4Vq8\nZy6EEEKIjk2auRBCCOHhpJkLIYQQHk6auRBCCOHhpJkLIYQQHk6auRBCCOHhpJkLIYQQHk6auRBC\nCOHhPC4BTgghhBBNefWV+S9/+Ut3l+DxZAzbTsaw7WQMXUPGse3cNYZe3cyFEEKIzkCauRBCCOHh\nfH7zm9/8xt1FuFO/fv3cXYLHkzFsOxnDtpMxdA0Zx7ZzxxjKA3BCCCGEh5NpdiGEEMLDee1+5i+8\n8AKBgYGYTCZ8fHxYsmSJu0vyONXV1fzpT3/i7NmzKKV47rnnGDhwoLvL8hgXLlzg7bffbvy6oKCA\nmTNnkpaW5saqPM/nn3/Opk2bUEoRGxvL888/j7+/v7vL8ijr1q1j48aNaK1JTk6W78FWWr58OXv2\n7CEsLIy33noLgKqqKt5++20KCwvp1q0bP/vZzwgJCbn1xWgv9fzzz+vy8nJ3l+HR/vjHP+qMjAyt\ntdYNDQ26qqrKzRV5LofDoZ955hldUFDg7lI8SnFxsX7++ed1XV2d1lrrt956S2dmZrq3KA9z+vRp\nvXDhQm2z2bTdbtevvfaavnjxorvL8gjffPONzs/P1wsXLmz8s5UrV+o1a9ZorbVes2aNXrlyZbvU\nItPs4qbU1NRw+PBhJk+eDICvry/BwcFurspzHThwgKioKLp16+buUjyOYRjU19fjcDior68nIiLC\n3SV5lPPnz9O/f38CAgLw8fEhPj6enTt3urssjzBo0KCrrrpzc3OZOHEiABMnTiQ3N7ddavHaaXaA\nxYsXAzBlyhRSUlLcXI1nKSgooGvXrixfvpzTp0/Tr18/5syZQ2BgoLtL80jbt29n/Pjx7i7D45jN\nZu6//36ee+45/P39GT58OMOHD3d3WR4lNjaWjz/+mMrKSvz9/dm7dy9xcXHuLstjlZeXN/5CGR4e\nTnl5ebu8r9c289/+9reYzWbKy8t5/fXXiY6OZtCgQe4uy2M4HA5OnjzJ3LlzGTBgAB988AGffvop\njz32mLtL8zh2u53du3fz+OOPu7sUj1NVVUVubi7Lli2jS5cu/Pu//ztbtmwhMTHR3aV5jJiYGB58\n8EFef/11AgMD6dOnDyaTTNq6glIKpVS7vJfX/ouZzWYAwsLCGD16NHl5eW6uyLNYLBYsFgsDBgwA\nYOzYsZw8edLNVXmmvXv30rdvX8LDw91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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.75e-01\n",
- " final error(valid) = 1.72e-01\n",
- " final acc(train) = 9.50e-01\n",
- " final acc(valid) = 9.53e-01\n",
- " run time per epoch = 9.98\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.20\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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I0W5FinbX01rjLq+nwO1hx7Eq6r0GA23h5LrsTB9iIy6iey9Womuq0MWvoovX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\n+723bUJlzPBdtNa7fb/kRfcinbZoF8n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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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eew5dXIg+VAK9dhg/CXXvOtTMeSh/+ZUrbg35zhJC3FLtNie7qtvYXmWludtJ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\nSkhLS8NisWD6ZGGR2NhYfvGLX9DS0sK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l2rXvt956i3379vHGG28QExPDmjVrtHb93evN\n5eXl+Hw+Zs+ezdtvv/3AsxcUFFBVVcXx48dxuVy4XK7/tL/MzEyam5upqqoiKSmJjRs3oih3PqI2\nbdrEwYMHKSkpIRAIkJKSwtq1ax/EYQgxJch85kI8hO7emrZ161a9owghDEDa7EIIIYTBSTEXQggh\nDE7a7EIIIYTByZm5EEIIYXBSzIUQQgiDk2IuhBBCGJwUcyGEEMLgpJgLIYQQBifFXAghhDC43wEN\naHuZcMLyOgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.70e-01\n",
- " final error(valid) = 1.69e-01\n",
- " final acc(train) = 9.52e-01\n",
- " final acc(valid) = 9.55e-01\n",
- " run time per epoch = 10.36\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.50\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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EPx65ryuHL5SR+dkFso5eYnN+CfdEBDJ1QGem9e9EfESQt4d692Jj4f4UavKP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\nVXVsO+leuORPhdH8ZeDjJA+/yoQj7zP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PVjuWplRXV5ORkeF8X1tbS0JCAvHx8Sqm0p6D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\n",
- "text/plain": [
- ""
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- "output_type": "display_data"
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- {
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1aJpGenr6Ze+Y//jjj3n22Wfb/FlBQQH79+/HaDQSHR3NI488cnVXJoQQ1xCl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\nIYRoHyn0fkB5mlH5G1qHqa0sh+5XoT04G801BM1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PnRI3KCiIxsbG71w3KirK93VgYCAe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\n9EIIIYQf+3+DEMdHykKKUgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.69e-01\n",
- " final error(valid) = 1.71e-01\n",
- " final acc(train) = 9.51e-01\n",
- " final acc(valid) = 9.52e-01\n",
- " run time per epoch = 10.14\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=1.00\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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ECLxmXfK1b98+Xn75ZQzDIDMzk3nz5pGVlUVycjKpqak89dRTFBYW0qlTJ+CL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\n69BvvwnlHug3CMusRdA3pdlH7205QzM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3/wSTCbU8F/dfs3Fv+hLV1qp3RA850hZCCCEA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\nRK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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pOe3E6fZiDjMxM91CTnosY5P82061K7TWsG+370j8xGGIt6F++jNUzjxU+LU7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\nUv6p/TXtOIf+6F3qthWgW1t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Tw+rVq7HZbJSXl/PSSy/hcrkwmUwsXbqUGTNmAFBTU8Mf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\ne5w4cQKBQADNzc0ICwtDVlYW5s+fj8jISDQ1NWF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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 2.05e-01\n",
- " final error(valid) = 2.07e-01\n",
- " final acc(train) = 9.40e-01\n",
- " final acc(valid) = 9.39e-01\n",
- " run time per epoch = 10.99\n"
- ]
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "num_epochs = 10 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "learning_rate = 0.2 # learning rate for gradient descent\n",
- "\n",
- "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n",
- "final_errors_train = []\n",
- "final_errors_valid = []\n",
- "final_accs_train = []\n",
- "final_accs_valid = []\n",
- "\n",
- "for init_scale in init_scales:\n",
- "\n",
- " print('-' * 80)\n",
- " print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
- " .format(learning_rate, init_scale))\n",
- " print('-' * 80)\n",
- " # Reset random number generator and data provider states on each run\n",
- " # to ensure reproducibility of results\n",
- " rng.seed(seed)\n",
- " train_data.reset()\n",
- " valid_data.reset()\n",
- "\n",
- " # Alter data-provider batch size\n",
- " train_data.batch_size = batch_size \n",
- " valid_data.batch_size = batch_size\n",
- "\n",
- " # Create a parameter initialiser which will sample random uniform values\n",
- " # from [-init_scale, init_scale]\n",
- " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- " # Create a model with two affine layers\n",
- " hidden_dim = 100\n",
- " model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
- " ])\n",
- "\n",
- " # Initialise a cross entropy error object\n",
- " error = CrossEntropySoftmaxError()\n",
- "\n",
- " # Use a basic gradient descent learning rule\n",
- " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
- "\n",
- " plt.show()\n",
- "\n",
- " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
- " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
- " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
- " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
- " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
- "\n",
- " final_errors_train.append(stats[-1, keys['error(train)']])\n",
- " final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
- " final_accs_train.append(stats[-1, keys['acc(train)']])\n",
- " final_accs_valid.append(stats[-1, keys['acc(valid)']])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n",
- "|------------|--------------------|--------------------|------------------|------------------|\n",
- "| 0.1 | 1.75e-01 | 1.72e-01 | 0.95 | 0.95 |\n",
- "| 0.2 | 1.70e-01 | 1.69e-01 | 0.95 | 0.95 |\n",
- "| 0.5 | 1.69e-01 | 1.71e-01 | 0.95 | 0.95 |\n",
- "| 1.0 | 2.05e-01 | 2.07e-01 | 0.94 | 0.94 |\n"
- ]
- }
- ],
- "source": [
- "j = 0\n",
- "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
- "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
- "for init_scale in init_scales:\n",
- " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n",
- " .format(init_scale, \n",
- " final_errors_train[j], final_errors_valid[j],\n",
- " final_accs_train[j], final_accs_valid[j]))\n",
- " j += 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Models with three affine layers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.10\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
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HmDx5MsePH+cHP/gBAGPGjLmpwxetp2zxqOV/h3YuR+9xofO34v35E5AyBJPzXhiV3qXL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\nkLsQQggxnJhNGqlxIaTGheCwBdHYOPRN+6YXonk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- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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xZZ1nCn//RCNtbsWouBCemO6ZwsODAmsKh/Ohth2o7dsk1Cb8To/NXNd1Xn31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\nWlrYsGEDDz74IGPHjgUgKiqKlpYW3G4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5EyZMYNasWeh0OgwGw09nFGKokGF2IVSUk5Pz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\nCKFxfwIUz77RSc81WQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.86e-01\n",
- " final error(valid) = 1.83e-01\n",
- " final acc(train) = 9.46e-01\n",
- " final acc(valid) = 9.48e-01\n",
- " run time per epoch = 13.08\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.20\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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DaQ9E90TNvg81JRUVdOM/UNpKZ8ww0EiG5kmG/hGwT0QTXZPVopgQH8WE+CiK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\njWRc30iKq+rZmedlR14Jz+eeJ+r9i8w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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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14PYaZFsi+fG0dGbdlEBUkO0Z/ve0txNdvhtdsgUunIEkC+q+h1GzFqBi4gJd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\nn6amJoLBIPPmzSMQCLBs2TJmzpzJxo0bOXTo0A3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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.74e-01\n",
- " final error(valid) = 1.75e-01\n",
- " final acc(train) = 9.49e-01\n",
- " final acc(valid) = 9.51e-01\n",
- " run time per epoch = 15.16\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.50\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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qgWhtFu2UlBTOnDlDWVkZMTExFBUV8dBDD12xjcfj4cUXX+TJJ5/E4XDcXItF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\nwWb01nXgvQT9h/qK99DUNgvqjWTY2Kz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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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AAVe8bodBbzKZqKur832uq6vDZDJdsM5TTz0FgNPppLS0lMjISI4dO8aRI0co\nKCjA6XTidrsJCwtj8eLFvn2Eh4czc+ZMLBYLc+bM8R3PbDbj8Xhobm4mOlreFxVCBAalFJZ6J4UW\nGztO2HG6dVJiQlg2oR9ZQ2KICfvuz6pqa0XtLER99DY0nIchwzB8/4cwJlNuR4pu02HQp6WlcebM\nGWpqajCZTJSUlLBixYp263z7tL3BYGDTpk1kZWUBtFuvuLiY8vJyFi9ejMfjoampiZiYGNxuN/v3\n72f06NEATJw4keLiYoYNG8Ynn3zCyJEj5T8IIUSPa2z7rqBMZYO3oMzM1Bhy02MZnhDe7u+Ucrag\ndnyIKtgEdivcOALDg/mQMU7+nolu12HQBwUFsWzZMp577jl0XScrK4tBgwaxceNG0tLSyMzM5PDh\nw2zYsAFN08jIyPC9QncpLpeL5557Do/Hg67rjB492ncZft68eTz//PPk5+cTFRXFE0884Z8zFUKI\nq6SU4nBtC4UWK7tPOWjzKIbGh/LopCRm3xBDZEj7++qqpRm17X1U0d+g0QEZYzH88KdoN43qoTMQ\nAjSllOrpRvhDdXW13/Yl96P8Q/qx86QPO+9a+tDmdLO90kahxUaVvY3wbwrK5KbHkWYKu2B91eRA\nFb2H2vYeNDfB6EwMC+9FSxvur9PoUfI97LyAvkcvhBDXA10pvjjrLShTWuXArcPwhHBWTE1mRmoM\nYUbDBdsouxVV9DfU9s3gbIHxUzEsvA8tNe0iRxCiZ0jQCyGua3XNLrZW2Cgqt3Gu0UV0iIFbhsWT\nmxbH4LiLz0qnrPWojzahPt4CLhda5ky0vHvQUm7o3sYLcQUk6IUQ1x2Prthf3UiBxcb+am9BmTFJ\nEdw/NpGpg6IICbpw9A6g6mpRH72F2lkIusc7wU3ePWjJKRddX4hAIEEvhLhunGtso6jcO3qvb3ET\nFxbE9zJMzE+Po390yCW3U7VnUVveRJVsA0CbPg/tlrvREpO7q+lCXDMJeiFEn+by6Ow+aafAYuXz\ns81oGozvH8kjk5LIHBiF0XDp193UmSrU5jdQZTvAEIQ2Oxft5rvQzIndeAZCdI4EvRCiT6qyt1Jo\nsVF8woK1xU1ihJHvj0kge2gsiZGXLxijqk54A37fLggOQcu+DS33e2hxpstuJ0QgkqAXQvQZrW6d\nklMOCixWDte2EKTBzKFm5g4OZ2yyt6DM5aiTFvT3X4cDn0BoONqCO9HmL0KLju2mMxDC/yTohRC9\nXmWDkwLuRDSSAAAgAElEQVSLlR2VdppcOgOig1k6LpF5Q2NJH5Tc4fvLqvyot1TswX0QEYl22/e9\no/hImX5b9H4S9EKIXqnZ5WHnCQeF5VaO1zkJNmhMGxxNbnoso/pFXNFUs+qrQ+gfbIQjn0NUDNr3\nlqBlLUQLj+iGMxCie0jQCyF6DaUUx+q8o/ddJ+043YrU2FAentiPuUNiiQ7tuNSrUgoOH/AG/PHD\nEBOHds9DaHNuQQu9cNY7IXo7CXohRMBztHoorrRRWG7jpLWVMOO3BWXiGGYOu7LRu1LwxV7vJfrK\nYxCfgPYPP0SbOR8t5OIT4wjRF0jQCyECklKKL2taKLBYKTnlwKUr0k1hPDY5mVk3RBMR3PHoHUDp\nOmp/iXcEf7oSzP3QljyGNi0bLfjyT98L0RdI0AshAoq1xc22ChuF5VaqHS4igw3MT49lflocQy9S\nUOZSlO5B7d1F3Udvo5+uhKSBaA89jjZ5DppR/vSJ64d824UQPc6jKz4/20SBxUZZlQOPghGJ4dwz\nKoEZg6MJvUhBmUtRbjeqdAdq8xtQUw2DhqD941NomTPQDFd2FUCIvkSCXgjRY843uygqt7G13EpN\nk5uY0CBuG25iflosKbFXd99cuVyokq2oLW9CXQ0MHorhRz/DnHMrdfX1XXQGQgQ+CXohRLdy64p9\nXzdSYLHy2ZkmdAXjkiNYOr4fU1KiCL5EQZlLUW2tqJ0FqA/fBmsdDBmG4QePwOhMNE1DM1zd/oTo\nayTohRDd4oyjzTd6b3B6MIUbuWuEmfnpsSRFXbqgzKUoZwtqx4eogk1gt8KwkRgeehwyxl7RU/hC\nXC8k6IUQXcbl0dlzupFCi5UvzjVj0GDigCjmp8eSOSCqwylpL0Y1N6G2f4Aq+hs0OiBjLIZHfoo2\nbFQXnIEQvd8VBf2BAwd45ZVX0HWd7OxsFi1a1G55bW0tL730Ena7naioKPLz8zGbzb7lzc3NrFy5\nkkmTJrF8+XJaW1v5zW9+w7lz5zAYDEycOJHFixcDUFxczPr16zGZvMUjFixYQHZ2tr/OVwjRDU7Z\nWim0WNleacfR6qFfZDCLxySQnRaLOeLaXmlTTQ5U0buore9DSxOMzsSw8F60tOF+br0QfUuHQa/r\nOi+//DKrV6/GbDazatUqMjMzSUlJ8a2zfv16Zs+ezdy5czl06BAbNmwgPz/ft3zjxo1kZGS02+9t\nt93GqFGjcLvdPPvss3z22WeMHz8egOnTp7N8+XJ/naMQohs43d+Wg7Vx9HwLRgNMSYkmNz2OMckR\nGK7xcrqyW1GFf0Nt3wytLTB+KoaF96Glpvn5DITomzoMeovFQnJyMklJSYA3hPfu3dsu6Kuqqnjg\ngQcAGDlyJGvXrvUtq6iowGazMW7cOMrLywEIDQ1l1CjvZTaj0ciQIUOoq6vz31kJIbpNeb13StqP\nT9hpdukMjAnhoQmJzB0SS1zYtd8dVNY61EebUB9/CC4XWuZMtIX3og1M9WPrhej7OvyvsL6+vt1l\neLPZzPHjx9utk5qaSllZGXl5eZSVldHS0oLD4SAyMpJXX32V/Px8Dh48eNH9NzU1sX//fvLy8nz/\nVlpaypEjR+jfvz9Lly4lISHhWs9PCNEFml0edlTaKSy3Ul7fSkiQxvTB3tH7iMTwTj0Mp+pqUB++\njdpVCLoHbcpctLy70ZJTOt5YCHEBvzyMt2TJEtatW0dxcTEZGRmYTCYMBgMFBQWMHz++3Q+Fv+fx\nePiv//ovbrnlFt8Vg4kTJzJjxgyCg4MpLCzkhRde4Jlnnrlg26KiIoqKigBYs2aNX38MGI1G+XHh\nB9KPnRdIfaiU4tAZB+8eOsu24+dxunXSEyJZOXcg82/qR0wnRu8A7jNVNL29Huf2zaBphGflEXHn\nEozJAzu130Dqw95K+rDzerIPO/wv02QytbusXldX53tQ7u/XeeqppwBwOp2UlpYSGRnJsWPHOHLk\nCAUFBTidTtxuN2FhYb4H7/77v/+b5ORkFi5c6NtXdPR39Z+zs7N57bXXLtqunJwccnJyfJ87qjd9\nNRISEvy6v+uV9GPnBUIf2r8pKFNgsXLa1kaY0cDsG7yj93STt6BMW6OV843Xtn91pgq1+Q1U2Q4w\nBKHNXoC24E7aTIm0AXTy/AOhD3s76cPO83cfDhgw4IrX7TDo09LSOHPmDDU1NZhMJkpKSlixYkW7\ndb592t5gMLBp0yaysrIA2q1XXFxMeXm5L+T/+te/0tzczKOPPtpuXw0NDcTHxwOwb9++ds8CCCG6\nh64Uh841U2Cxsud0I25dMcwcxo+nJDMzNYbw4M5PQqOqKlEfvIHavxuCQ9BybkebvwgtztTxxkKI\nK9Zh0AcFBbFs2TKee+45dF0nKyuLQYMGsXHjRtLS0sjMzOTw4cNs2LABTdPIyMjo8In5uro63n77\nbQYOHMjTTz8NfPca3ZYtW9i3bx9BQUFERUXx2GOP+edMhRAdqm9xs63cW1DmbKOLqBADC26MY35a\nLDfE+6dWuzppQX9/IxwohbBwtAV3oc2/Ay061i/7F0K0pymlVE83wh+qq6v9ti+5TOUf0o+d1x19\n6NEVn51posBiZe/XjegKRvULZ356HNMGXV1BmctRliPeWvCH9kNEJFr27WjZt6FFRvll/5ci38PO\nkz7svIC+dC+E6JtqGl0UVVgpKrdR1+wmNiyIRRkmctLiGBhz9VPSXoxSCo4d8o7gj34BUTFodz6A\nNjcPLTzCL8cQQlyeBL0Q1xG3rthb9V1BGYDx/SN5eGI/Jg2MJjjIP3PEK6Xgy8+8I3jLYYiNR7tn\nGdqcBWih/rkFIIS4MhL0QlwHqu1tFJZb2Vphw+b0YI4wcu9oMzlD4+gXdW1T0l6MUgq+2OsdwZ84\nDqYEtB88gjYjBy3k6srOCiH8Q4JeiD6qzaOz55SDgnIbh74pKDNpYBS56XGM7x95TQVlLkXpOny2\nB/3916GqEhKS0Jb8E9r0eWhG//2QEEJcPQl6IfqYk9ZWCixWiittNLbpJEcFs2RsIvPSYjGF+/c/\neeXxoPbuRG1+A86chqSBaA89gTZlDlpQkF+PJYS4NhL0QvQBLS6dXSe9U9J+dd6J0aAxbZB39D4q\n6doLylyKcrtRpcXegK85AwNT0X74E7SJ09EMEvBCBBIJeiF6KaUUlnonhRYbO07Ycbp1UmJCWDah\nH1lDYjo9Je1Fj+lyoXYXoT58C+pqYPBQDD9aBeOmoBn88xqeEMK/JOiF6GUa274rKFPZ4C0oMzM1\nhtz0WIYndK6gzKWo1lbUzo9QH70N1noYehOGxY/CqIldcjwhhP9I0AvRCyilOFzbQqHFyu5TDto8\niqHxoTw6KYnZN8QQGdI1l8uVswW1Ywvqo03gsMGwkRgeegIyxkrAC9FLSNALEcAaml28c6SOQouN\nKnsb4UYD84bGkpseR5qp695HV81NqG3vo4rehSYHjBiHYeG9aMNGddkxhRBdQ4JeiACjK8UXZ70F\nZUqrvsKtK4YnhLNiajIzUmMI89OUtBejGu2ore+htr4PLU0wZpI34Ife1GXHFEJ0LQl6IQJEXbOL\nrRU2isptnGt0ER1i4M4x/Zk1MJTBcV072YyyW1EF76CKt0BrC0yY5g34wWldelwhRNeToBeiB3l0\nxf7qRgosNvZXewvKjEmK4P6xiUwbFEX/pH5dWkxENdShCjahPv4QXG60STPR8u5FGzi4y44phOhe\nEvRC9IBzjW0UlXtH7/UtbuLCgvhehon56XH0j/ZPQZnLUXU1qA/fQu0qBF1Hm5qFdsvdaMkDu/zY\nQojuJUEvRDdxeRRlVQ4KLFY+P9uMpnkLyjwyKYnMgVEY/Tgl7aWommrU5jdRn2wHNLQZ2d568InJ\nXX5sIUTPkKAXootV2VsptNjYVmHD3uohMcLI98ckkD00lsTI7pkHXp05jdr8Bqr0YzAa0ebcgnbz\nnWimhG45vhCi50jQC9EFWt06Jae8o/fDtS0EaTA5xTsl7dhk/xaUuRxVVYl6/3XUpyUQHII2/w60\n3EVosfHdcnwhRM+ToBfCjyobnBRYrOyotNPk0hkQHczScYnMGxpLnJ8LylyOOnHcWwv+QCmEhXvv\nv+fcgRYd021tEEIEBgl6ITqp2eVh5wkHheVWjtc5CTZoTBscTW56LKP6RXTrDHLKcgT9g41w6FOI\niEK7/Qdo825Fi4zqtjYIIQLLFQX9gQMHeOWVV9B1nezsbBYtWtRueW1tLS+99BJ2u52oqCjy8/Mx\nm82+5c3NzaxcuZJJkyaxfPlyACoqKnjhhRdoa2tj/PjxPPTQQ2iaRmNjI7/97W+pra0lMTGRJ598\nkqgo+SMlAotSimN13tH7rpN2nG5FamwoD0/sx9whsUSHdl8FN6UUfHUQ/f2N8NVBiIpBu/MBtLl5\naOER3dYOIURg6jDodV3n5ZdfZvXq1ZjNZlatWkVmZiYpKSm+ddavX8/s2bOZO3cuhw4dYsOGDeTn\n5/uWb9y4kYyMjHb7/eMf/8gjjzzCjTfeyL//+79z4MABxo8fzzvvvMPo0aNZtGgR77zzDu+88w73\n33+/H09ZiGvnaPVQXGmjsNzGSWsrYcZvC8rEMcwc1r2jd6Xgy0+9l+gtRyDWhHbvcrTZN6OFdt30\nuEKI3qXDuTQtFgvJyckkJSVhNBqZPn06e/fubbdOVVUVo0Z558AeOXIk+/bt8y2rqKjAZrMxduxY\n3781NDTQ0tLCsGHD0DSN2bNn+/a5d+9e5syZA8CcOXMuOJYQ3U0pxaFzzfxmdzUPvW3hf/bXEGzQ\neGxyMq/cmU7+1P7c1EVV4y7VHnWgFP25f0b/r/8H9bVoP3gUw7//AcP8OyTkhRDtdDiir6+vb3cZ\n3mw2c/z48XbrpKamUlZWRl5eHmVlZbS0tOBwOIiMjOTVV18lPz+fgwcPXnaf9fX1ANhsNuLjvU8E\nx8XFYbPZOneGQlwja4ubbRU2CsutVDtcRAYbmJ8ey/y0OIZ2YUGZS1G6Dp+WeEfwVScgMRntgR+j\nTctCM3bPa3pCiN7HLw/jLVmyhHXr1lFcXExGRgYmkwmDwUBBQQHjx49vF+pXQ9O0S46SioqKKCoq\nAmDNmjUkJPjvfWCj0ejX/V2vemM/enTFvtNW3j10lp0V9Xh0xdgBMSybdgNZ6QmEBXffvXfw9qE5\nPg7nziKa3noVT9UJggYOJvLx/0vYrPloQfI8bUd64/cw0Egfdl5P9mGHfyVMJhN1dXW+z3V1dZhM\npgvWeeqppwBwOp2UlpYSGRnJsWPHOHLkCAUFBTidTtxuN2FhYeTl5V1yn7GxsTQ0NBAfH09DQwMx\nMRd/HSgnJ4ecnBzfZ3/OB56QkNCl84tfL3pTP55vdlFUbmNruZWaJjcxoUHcdlM889NiSYn1FpRp\ntDXQ2I1tUm4XUYf2Y3/jFag5AwNT0X74U9TEaTQZgmhqsHZja3qv3vQ9DFTSh53n7z4cMGDAFa/b\nYdCnpaVx5swZampqMJlMlJSUsGLFinbrfPu0vcFgYNOmTWRlZQG0W6+4uJjy8nIWL14MQHh4OMeO\nHePGG2/k448/ZsGCBQBkZmayY8cOFi1axI4dO5g0adIVn4wQV8OtK/Z93UiBxcpnZ5rQFYxLjuDB\n8f2YnBJFcFDXlYO9HOVyoXYXora8hb2+FganYXjs5zB2MpqhZ9okhOi9Ogz6oKAgli1bxnPPPYeu\n62RlZTFo0CA2btxIWloamZmZHD58mA0bNqBpGhkZGb5X6C7n4Ycf5sUXX6StrY1x48Yxfvx4ABYt\nWsRvf/tbtm3b5nu9Tgh/OuNo843eG5weTOFG7hphZn56LElRXV9Q5lJUaytq50eoj94Gaz2kDSfu\nsZ9hH5zerU/zCyH6Fk0ppXq6Ef5QXV3tt33JZSr/CKR+dHl09pxupNBi5YtzzRg0mDggitz0WCYO\niOq2KWkvRjmbUcVbUAXvgMMGw0ZhuPU+GD6GxMTEgOnD3iqQvoe9lfRh5wX0pXsherNTtlYKLVa2\nV9pxtHroFxnM4jEJZKfFYo7o2SfVVXMjatv7qKL3oMkBI8ZjWHgv2rCRPdouIUTfIkEv+hynW2f3\nSTsFFhtHz7dgNMCUlGhy0+MYkxyBoYcvg6tGO6roXdS296GlGcZO9gb8kGE92i4hRN8kQS/6jPJ6\n75S0H5+w0+zSGRgTwkMTEpk7JJa4sJ7/qit7A6rgHVTxFmh1woTp3oAfPLSnmyaE6MN6/q+fEJ3Q\n7PKwo9JOYbmV8vpWQoI0pg/2jt5HJHbfbHWXoxrqUB+9jdr5EbjcaJNmoeXdgzZwcE83TQhxHZCg\nF72OUoqj51sosNjYfdJOq0cxJD6UH2YmMeeGGKK6saDM5ai6GtSWN1G7i0AptKlz0W65By3pyh+i\nEUKIzpKgF72G/ZuCMgUWK6dtbYQZDcwZ4i0ok27q3oIyl6NqqlGb30B9UgyahjY9B+2Wu9ASknq6\naUKI65AEvQho+jcFZQosVvacbsStK4aZw/jxlGRmpsYQHhw4E8io6lPegC/bCUajt0xs7vfQTDJ1\nqBCi50jQi4BU3+JmW7m3oMzZRhdRIQYW3BjH/LRYbogPrOps6nQl+gcb4dM9EBKKNv8OtNxFaLHx\nPd00IYSQoBeBw6MrPjvTRIHFyt6vG9EVjOoXzj+MSWDaoGhCjYEzegdQlce9Af95GYRHeO+/59yO\nFn3x+gxCCNETJOhFj6tpdFFUYaWo3EZds5vYsCAWZZjISYtjYEzPTUl7KcpyGP39jfDlZxARhXbH\nD9Dm3YoWEdXTTRNCiAtI0Ise4dYVe6u+KygDML5/JA9P7MekgdEEBwXGg3XfUkrB0S+8teC/OgjR\nsWh3LkXLugUtLKKnmyeEEJckQS+6VbW9jcJyK1srbNicHswRRu4dbSZnaBz9onp2StqLUUrBoU+9\nl+jLj0KsCe2+5WizFqCFhvZ084QQokMS9KLLtXl09pxyUFBu49A3BWUmDYwiNz2O8f0je7SgzKUo\nXYcvytDffx1OWsCUiLb4UbQZOWjBgXc7QQghLkWCXnSZivNNbNx3juJKG41tOslRwSwZm8i8tFhM\n4YH51VO6B7V/D2rz61B1AhKT0R74Mdq0LDRj4F1xEEKIjgTmX1vRa7W4dHad9E5J+9V5J0aDxrRB\n3tH7qKSeLyhzKcrjQZV9jNr8BpytguQUtOVPok2ajRYUGDPtCSHEtZCgF52mlMJS76TQYmPHCTtO\nt05KTAj5s4YwuV8QMQFQUOZSlNuF2rMdteVNqD0LA1PRfvhTtInT0AwS8EKI3i9w/wKLgNfY9l1B\nmcoGb0GZmakx5KbHMjwhnMTERM6fP9/Tzbwo5WpD7SpCffgW1NdCajqGf/o5jJmMZgis9/WFEKIz\nJOjFVVFKcbi2hUKLld2nHLR5FGmmUB6dlMTsG2KIDAnsUbBqbUXt/BD10Saw1kPacAxLHoOREwJm\nrnwhhPAnCXpxRWxON9srbRRabFTZ2wg3Gpg3NJbc9DjSTIE1Je3FKGczavsWVOE74LDBTaMxLHsS\nho+RgBdC9GlXFPQHDhzglVdeQdd1srOzWbRoUbvltbW1vPTSS9jtdqKiosjPz8dsNlNbW8uvfvUr\ndF3H4/GwYMECcnNzaWlp4Re/+IVv+/r6embNmsWDDz5IcXEx69evx2QyAbBgwQKys7P9eMriSulK\n8cVZb0GZ0ioHbh2GJ4SzYmoyM1JjCAuwKWkvRjU3ora9jyp6D5ocMHI8hoX3od04oqebJoQQ3aLD\noNd1nZdffpnVq1djNptZtWoVmZmZpKSk+NZZv349s2fPZu7cuRw6dIgNGzaQn59PfHw8//Zv/0Zw\ncDBOp5N//ud/JjMzE5PJxNq1a33bP/3000yePNn3efr06SxfvtzPpyquVF2zi60VNorKbZxrdBEd\nYuCWYfHkpsUxOK53TBKjHHZU0buo7e9DSzOMnYxh4b1oQ4b1dNOEEKJbdRj0FouF5ORkkpK8tbSn\nT5/O3r172wV9VVUVDzzwAAAjR470hbjR+N3uXS4Xuq5fsP/q6mrsdjsZGRmdOxPRKR5dsb+6kQKL\njf3V3oIyY5IiuH9sItMGRREcFPijdwBla0AVvIPasQXaWmHCNAx596INHtrTTRNCiB7RYdDX19dj\nNpt9n81mM8ePH2+3TmpqKmVlZeTl5VFWVkZLSwsOh4Po6GjOnz/PmjVrOHv2LPfff7/vkvy3SkpK\nmDZtWrv7pKWlpRw5coT+/fuzdOlSEhKknndXOdfYRlG5d/Re3+ImPiyIO0eYyUmLpX9075kBTtWf\nRxVsQn38EbjdaJNnoeXdgzZgcE83TQghepRfHsZbsmQJ69ato7i4mIyMDEwmE4ZvXlFKSEjgV7/6\nFfX19axdu5apU6cSFxfn23b37t3k5+f7Pk+cOJEZM2YQHBxMYWEhL7zwAs8888wFxywqKqKoqAiA\nNWvW+PXHgNFo7NM/LlwenZ0V9bx76Cz7TlnRNJiSGs/to5KZfkM8Rj+N3rujHz01Z2h6az0t2z4A\npRM29xYi71yCccCgLj1ud+nr38XuIH3YedKHndeTfdhh0JtMJurq6nyf6+rqLhiVm0wmnnrqKQCc\nTielpaVERkZesM6gQYM4evQoU6dOBeDEiRPous7Qod9dVo2Ojvb97+zsbF577bWLtisnJ4ecnBzf\nZ3++r52QkBCw7393RpW9lUKLjW0VNuytHhIjjHx/TALZQ2NJjAwGFNaGer8dryv7UZ2rRm15A/VJ\nMWiadw76BXfhSkjCCtBH/v/rq9/F7iR92HnSh53n7z4cMGDAFa/bYdCnpaVx5swZampqMJlMlJSU\nsGLFinbrfPu0vcFgYNOmTWRlZQHeHwXR0dGEhITQ2NjIV199xa233urbbvfu3cyYMaPdvhoaGoiP\njwdg37597Z4FEFev1a1TcspBgcXK4doWgjSYnBJNbnosY5MDs6DM5ajqU6gP3kDt3QlGI9rcPLTc\n76GZZLQhhBAX02HQBwUFsWzZMp577jl0XScrK4tBgwaxceNG0tLSyMzM5PDhw2zYsAFN08jIyPA9\nMf/111/z6quvomkaSiluu+02Bg/+7p7pnj17WLVqVbvjbdmyhX379hEUFERUVBSPPfaYn0/5+lDZ\n4KTAYmVHpZ0ml86A6GCWjktk3tBY4gK0oMzlqFMV3lrwn+2BkFC03DvQchehxcT3dNOEECKgaUop\n1dON8Ifq6mq/7au3XqZqdnnYecJBYbmV43VOgg0a0wZ7R++j+kV0+8Qw/uhHVXnMG/Cfl0F4BNq8\nW9FybkeLivFTKwNbb/0uBhLpw86TPuy8gL50LwKbUopjdd7R+66TdpxuRWpsKA9P7MfcIbFEhwb2\nlLSXoo4fRn9/Ixz+DCKj0e5YjDZvIVpEVE83TQghehUJ+l7K0eqhuNJGYbmNk9ZWwozfFpSJY5g5\nrFdO66qUgqNfeAP+2CGIjkW7ayna3FvQwiJ6unlCCNErSdD3IkopvqxpocBipeSUA5euuNEcxmOT\nk5l1QzQRwb109K4UHNrvvURffhTiTGj3PYw262a00N4xE58QQgQqCfpewNriZluFjcJyK9UOF5HB\nBuanewvKDIkP/IIyl6J0HT4v8wb8SQuYEtEWP+p9VS6490zWI4QQgUyCPkB5dMXnZ5sosNgoq3Lg\nUTAiMZx7RiUwY3A0ob2goMylKN2D2l+C+uB1+PokJCajLc1HmzoXzRjc080TQog+RYI+wJxvdlFU\nbmNruZWaJjcxoUHcNtzE/LRYUmJ792Vs5fGgyj5GbX4DzlZB/0Foy1eiTZqFFtQ7bzsIIUSgk6AP\nAG5dse/rRgosVj4704SuYFxyBA+O78fklN5TUOZSlNuF2rMdteVNqD0LKTdgeOSnMGE6mqF3n5sQ\nQgQ6CfoedMbR5hu9Nzg9mMKN3DXCzPz0WJKiev89atXWir59M+rDt6C+FlLTMfzT/4ExkyTghRCi\nm0jQdzOXR2fP6UYKLVa+ONeMQYOJA6LITY9l4oCoXjcl7cWoVifq4484X/g3VMN5SBuOYcljMHJC\nr3ztTwghejMJ+m5yytZKocXK9ko7jlYP/SKDWTwmgey0WMwRfeMBNOVsRm3fjCr8GzhsGEdNgGVP\nwE2jJeCFEKKHSNB3IadbZ/dJOwUWG0fPt2A0wJSUaHLT4xiTHIGhj4SfampEbXsfVfQuNDfCqAkY\nFt6LaepsmTZTCCF6mAR9Fyiv905J+/EJO80unYExITw0IZGsIbHEhvWdLlcOO6rob6jtH0BLM4yb\ngiHvXrQhN/Z004QQQnyj76ROD2t2efj/27v34CjqdP/j756Z3Mh9ZghJIBgIsAYQD8tEIiABgiwG\nKTleImdFNofoWQkHV7n8WKoo10XYDUWQVS5CeYAVdlnBVdhSQSVIQAFJwkVBQAkoQrjkSi6Q20x/\nf39knTUqJusEOjM+ryqqZjI93Z9+QuWZ/nZPf3d/Uc2O01c4XdGAv1ljSPfmo/e+nYN8auhaVVWi\n3tuCytsOTY1oPx+CNi4dLa6H0dGEEEJ8izR6DyilOFlWx3tFVew9W02DS9EjMoD/cXQhJT6MEC+d\nUOZ6VEUZ6t03UB+8B04n2uDhaGkPocXEGR1NCCHEdUij/xGq/zmhzHtFVzhX1UigxURKj+YJZXpZ\nvXNCmR+iSi+h3nkdtXcnoNCSR6KlPYgW1fZpEoUQQhhDGn0b6Upx7PI13iu6wv5ztTh1RR9bIP87\nOJpht4QR5Od73wtXl4pR2/+O+mgXmExod92NNvYBNFuU0dGEEEK0kTT6VlTUOXn/dPOEMpdqmwjx\nNzG2dwR3J4QT78UTyvwQVfwVattmVMGH4GdBG3Uv2pj/RIu0GR1NCCHEv0ka/fdwfeOWtAXFtegK\n+kcF8V8D7NwZ590TyvwQ9dXp5pnkDu2HgEC0MRPQxtyHFhZpdDQhhBA/kjT6b/noXA1r/nGGktpG\nwlLd/FkAABSySURBVAPNTEi0Mjohgq5h3n9L2utRZz5rbvCfFEBQMNq9D6OljkcLCTM6mhBCCA+1\nqdEfOXKEdevWoes6qampTJgwocXrpaWlvPTSS1RXVxMSEsL06dOx2WyUlpaSk5ODruu4XC7Gjh3L\nmDFjAHj22WeprKzE37+5gc6bN4/w8HCamppYvnw5Z86cITQ0lKeeeoqoqJt3Tjg8wEwPWyf+e6Cd\npK6h+Jl968K6b1Kff4r+9iY4fgSCQ9EmTEIbmYbWKcToaEIIIdpJq41e13XWrFnDvHnzsNlszJ07\nF4fDQbdu3dzLbNiwgeHDhzNixAiOHTvGxo0bmT59OpGRkSxYsAA/Pz/q6+uZOXMmDocDq9UKwJNP\nPklCQkKL7b3//vsEBwezbNky9u7dy1//+leefvrpdt7t60uM6sRdfbv77B3dlFJw4uPmBv/5pxAa\njvZgBlrKPWiBQUbHE0II0c5aPdlcVFREdHQ0Xbp0wWKxMGTIEAoKClosc/78efr37w9Av379KCws\nBMBiseDn13wf96amJnRdbzVQYWEhI0aMACA5OZljx441NyfhEaUU6mghevb/Q1/6DJRcQpv4OKY/\n/h+mX9wvTV4IIXxUq0f0FRUV2Gz/utraZrNx6tSpFsvccsst5Ofnk5aWRn5+PnV1ddTU1BAaGkpZ\nWRnZ2dlcunSJSZMmuY/mAVauXInJZGLw4ME88MADaJrWYntms5lOnTpRU1NDWFjL88W5ubnk5uYC\nkJ2djd1u//FV+BaLxdKu6zOS0nUa8j/g6mt/xnnmM0ydown+9WyCUseh+d3Y6w58qY5GkRp6Tmro\nOamh54ysYbtcjPfoo4+ydu1a8vLySExMxGq1YvrnfON2u52cnBwqKipYvHgxycnJRERE8OSTT2K1\nWqmrq2PJkiXs2bOHlJSUNm9z9OjRjB492v28PYfa7Xa71w/dK92FKtyL2vYaFJ+FqBi0jCdh8Aiu\nWSxcq6q+4Rl8oY5Gkxp6TmroOamh59q7hrGxbb9hWauN3mq1Ul5e7n5eXl7e4qj862VmzZoFQH19\nPQcOHCA4OPg7y8TFxXHy5EmSk5Pd6wgKCmLYsGEUFRWRkpLi3p7NZsPlcnHt2jVCQ0PbvEM/dcrl\nQh3Yjdr+Glwqhpg4tMwZaEl3oZl965a8QgghWtfqOfqEhAQuXrxISUkJTqeTffv24XA4WixTXV3t\nPv++ZcsWRo4cCTR/KGhsbASgtraWzz77jNjYWFwuF9XVzUeUTqeTgwcPEhfXfL/0QYMGkZeXB8BH\nH31Ev379fO6WsjeCcjah73kXfd4TqHV/Aos/pifmYHp2GabkEdLkhRDiJ6rVI3qz2cyUKVNYuHAh\nuq4zcuRI4uLi2LRpEwkJCTgcDo4fP87GjRvRNI3ExEQyMzMBKC4uZv369WiahlKK8ePH0717d+rr\n61m4cCEulwtd17ntttvcw/CjRo1i+fLlTJ8+nZCQEJ566qkbWwEvp5oaUR/uQL3zOlSUQXxvTBMf\nhwFJ8gFJCCEEmvKRS9ovXLjQbuvyhvNRqqEetfsd1HtboKoSeiViGvcw9BvYYRq8N9Sxo5Maek5q\n6Dmpoec69Dl60bGoumuoXW+jdvwDaqvh1gGYHp8Fffp3mAYvhBCi45BG7yXU1VrUzjdRO9+Ea7XQ\nfxCmcelovRKNjiaEEKIDk0bfwamaKtSOf6B2vQ31dfAfyZjGPYQW39voaEIIIbyANPoOSl2pQO3Y\nisrbDk2NaIOGoo17CK1bD6OjCSGE8CLS6DsYVVGKeucN1Afvge5CuyMFLe1BtJg4o6MJIYTwQtLo\nOwhVegm1/e+ofe8DCu3OUWj3PIgWFWN0NCGEEF5MGr3B1KXzqG1/Rx3IA5MJ7a4xaGMfQLN1Njqa\nEEIIHyCN3iCq+Czq7c2owr3gZ0EbNR7tFxPQImytv1kIIYRoI2n0N5k6e7p5LvjDH0FAENov/hPt\n7vvQwiKMjiaEEMIHSaO/SdTpk+hvb4ajhRAUjHbvRLTUe9FCwlp/sxBCCPEjSaO/wdTnx9Df2gQn\nPoaQULQJk9BGjkPrFNz6m4UQQggPSaO/AZRScOJIc4M/dRzCItAe/G+0lLFogUFGxxNCCPETIo2+\nHSml4Ghhc4P/4nOIsKFNfLz5Snr/AKPjCSGE+AmSRt8OlK7DkY+az8F/dQZsUWiTstCGpKL5+Rkd\nTwghxE+YNHoPKN2FKtyL2vYaFJ+FqFi0jN+gDU5Bs0hphRBCGE+60Y+gnE5U/m7Utr/D5WKIiUN7\nbCZa0jA0k9noeEIIIYSbNPp/g2pqQu3fidr+OpRdhrgemJ74LQxMRjOZjI4nhBBCfIc0+jZQjQ2o\nD3eg3nkDKsugRx9ME/8HBjjQNM3oeEIIIcR1tanRHzlyhHXr1qHrOqmpqUyYMKHF66Wlpbz00ktU\nV1cTEhLC9OnTsdlslJaWkpOTg67ruFwuxo4dy5gxY2hoaOD555/n8uXLmEwmBg0axCOPPAJAXl4e\nGzZswGq1AjB27FhSU1PbebfbRjXUo3ZvR723FaoqoVdfTL+aDn3/Qxq8EEIIr9Bqo9d1nTVr1jBv\n3jxsNhtz587F4XDQrVs39zIbNmxg+PDhjBgxgmPHjrFx40amT59OZGQkCxYswM/Pj/r6embOnInD\n4SA4OJjx48fTv39/nE4n8+fP5/DhwwwcOBCAIUOGkJmZeeP2uhX6tavo215D7fgH1FZD4u2YHp+N\n9rP+hmUSQgghfoxWG31RURHR0dF06dIFaG7CBQUFLRr9+fPnmTx5MgD9+vVj8eLFzSv/xpXnTU1N\n6LoOQEBAAP3793cv06NHD8rLy9tplzyjCj+k7C8voa7WwG0OTOPS0RJuNTqWEEII8aO02ugrKiqw\n2f41o5rNZuPUqVMtlrnlllvIz88nLS2N/Px86urqqKmpITQ0lLKyMrKzs7l06RKTJk1yD8l/7erV\nqxw8eJC0tDT3zw4cOMCJEyeIiYnhV7/6FXa73dP9bLuoGPz7D6Tp7glot/S6edsVQgghboB2uRjv\n0UcfZe3ateTl5ZGYmIjVasX0z6vQ7XY7OTk5VFRUsHjxYpKTk4mIaJ6pzeVy8cILL3DPPfe4RwwG\nDRrE0KFD8fPzY8eOHaxYsYLf/e5339lmbm4uubm5AGRnZ7ffhwG7HcsdQ3E6ne2zvp8wi8Vycz+k\n+SCpoeekhp6TGnrOyBq22uitVmuLYfXy8vLvHJVbrVZmzZoFQH19PQcOHCA4OPg7y8TFxXHy5EmS\nk5MBWL16NdHR0YwbN869XGhoqPtxamoqf/nLX7431+jRoxk9erT7eVlZWWu70mZ2u71d1/dTJXX0\nnNTQc1JDz0kNPdfeNYyNjW3zsq1++TshIYGLFy9SUlKC0+lk3759OByOFstUV1e7z79v2bKFkSNH\nAs0fChobGwGora3ls88+c4d79dVXuXbtGhkZGS3WVVlZ6X5cWFjY4loAIYQQQvx7Wj2iN5vNTJky\nhYULF6LrOiNHjiQuLo5NmzaRkJCAw+Hg+PHjbNy4EU3TSExMdF8xX1xczPr169E0DaUU48ePp3v3\n7pSXl/PGG2/QtWtX5syZA/zra3Tbt2+nsLAQs9lMSEgIWVlZN7YCQgghhA/TlFLK6BDt4cKFC+22\nLhmmah9SR89JDT0nNfSc1NBzHXroXgghhBDeSxq9EEII4cOk0QshhBA+TBq9EEII4cN85mI8IYQQ\nQnyXHNF/j9/+9rdGR/AJUkfPSQ09JzX0nNTQc0bWUBq9EEII4cOk0QshhBA+zPzss88+a3SIjqhn\nz55GR/AJUkfPSQ09JzX0nNTQc0bVUC7GE0IIIXyYDN0LIYQQPqxd5qP3JdOmTSMwMBCTyYTZbCY7\nO9voSF7n6tWrrFq1inPnzqFpGlOnTqVPnz5Gx/IaFy5cYOnSpe7nJSUlpKent5jOWbTurbfe4v33\n30fTNOLi4sjKysLf39/oWF5l27Zt7Ny5E6UUqamp8n+wjVauXMmhQ4cIDw9nyZIlQPMMrkuXLqW0\ntJTOnTvz9NNPExIScnMCKdFCVlaWqqqqMjqGV1u2bJnKzc1VSinV1NSkamtrDU7kvVwul3rsscdU\nSUmJ0VG8Snl5ucrKylINDQ1KKaWWLFmidu3aZWwoL3P27Fk1Y8YMVV9fr5xOp5o/f766ePGi0bG8\nwqeffqpOnz6tZsyY4f7Zhg0b1JYtW5RSSm3ZskVt2LDhpuWRoXvRrq5du8aJEycYNWoUABaLheDg\nYINTea+jR48SHR1N586djY7idXRdp7GxEZfLRWNjI5GRkUZH8irFxcX06tWLgIAAzGYziYmJHDhw\nwOhYXqFv377fOVovKCggJSUFgJSUFAoKCm5aHhm6/x4LFy4E4O6772b06NEGp/EuJSUlhIWFsXLl\nSs6ePUvPnj3JyMggMDDQ6Gheae/evQwdOtToGF7HarUyfvx4pk6dir+/P7fffju333670bG8Slxc\nHK+++io1NTX4+/tz+PBhEhISjI7ltaqqqtwfNiMiIqiqqrpp25ZG/y3PPfccVquVqqoqFixYQGxs\nLH379jU6ltdwuVx88cUXTJkyhd69e7Nu3Tq2bt3KxIkTjY7mdZxOJwcPHuSXv/yl0VG8Tm1tLQUF\nBaxYsYJOnTrx/PPPs2fPHoYPH250NK/RrVs37rvvPhYsWEBgYCDx8fGYTDII3B40TUPTtJu2Pfmt\nfYvVagUgPDycpKQkioqKDE7kXWw2Gzabjd69ewOQnJzMF198YXAq73T48GF69OhBRESE0VG8ztGj\nR4mKiiIsLAyLxcLgwYP5/PPPjY7ldUaNGsWiRYv4/e9/T3BwMDExMUZH8lrh4eFUVlYCUFlZSVhY\n2E3btjT6b6ivr6eurs79+JNPPqF79+4Gp/IuERER2Gw2Lly4ADT/we3WrZvBqbyTDNv/eHa7nVOn\nTtHQ0IBSiqNHj9K1a1ejY3mdr4eXy8rKyM/PZ9iwYQYn8l4Oh4Pdu3cDsHv3bpKSkm7atuWGOd9w\n+fJlcnJygOYh6GHDhnH//fcbnMr7fPnll6xatQqn00lUVBRZWVk372skPqK+vp6srCyWL19Op06d\njI7jlTZv3sy+ffswm83Ex8fzxBNP4OfnZ3Qsr/LMM89QU1ODxWJh8uTJ3HbbbUZH8gp/+tOfOH78\nODU1NYSHh5Oenk5SUhJLly6lrKzspn+9Thq9EEII4cNk6F4IIYTwYdLohRBCCB8mjV4IIYTwYdLo\nhRBCCB8mjV4IIYTwYdLohRAApKenc+nSJaNjfMfmzZt58cUXjY4hhNeSW+AK0QFNmzaNK1eutLjl\n6IgRI8jMzDQwlRDCG0mjF6KDmjNnDgMGDDA6hk9xuVyYzWajYwhxU0mjF8LL5OXlsXPnTuLj49mz\nZw+RkZFkZma671pWUVHByy+/zMmTJwkJCeG+++5zz8Ko6zpbt25l165dVFVVERMTw+zZs7Hb7QB8\n8skn/OEPf6C6upphw4aRmZn5vZNvbN68mfPnz+Pv709+fj52u51p06a5ZzdLT0/nxRdfJDo6GoAV\nK1Zgs9mYOHEin376KcuWLeOee+7hzTffxGQy8dhjj2GxWHjllVeorq5m/PjxLe5K2dTUxNKlSzl8\n+DAxMTFMnTqV+Ph49/6uXbuWEydOEBgYyLhx40hLS3PnPHfuHH5+fhw8eJDJkyeTmpp6Y34xQnRQ\nco5eCC906tQpunTpwpo1a0hPTycnJ4fa2loAXnjhBWw2G6tXr2bmzJn87W9/49ixYwC89dZb7N27\nl7lz5/LKK68wdepUAgIC3Os9dOgQf/zjH8nJyWH//v18/PHH181w8OBBhgwZwp///GccDgdr165t\nc/4rV67Q1NTEqlWrSE9PZ/Xq1XzwwQdkZ2czf/58Xn/9dUpKStzLFxYWcuedd7J27VqGDh3K4sWL\ncTqd6LrOokWLiI+PZ/Xq1TzzzDNs27aNI0eOtHhvcnIy69at46677mpzRiF8hTR6ITqoxYsXk5GR\n4f6Xm5vrfi08PJxx48ZhsVgYMmQIsbGxHDp0iLKyMk6ePMkjjzyCv78/8fHxpKamuifT2LlzJxMn\nTiQ2NhZN04iPjyc0NNS93gkTJhAcHIzdbqdfv358+eWX181366238vOf/xyTycTw4cN/cNlvM5vN\n3H///VgsFoYOHUpNTQ1paWkEBQURFxdHt27dWqyvZ8+eJCcnY7FYuPfee2lqauLUqVOcPn2a6upq\nHnzwQSwWC126dCE1NZV9+/a539unTx/uuOMOTCYT/v7+bc4ohK+QoXshOqjZs2df9xy91WptMaTe\nuXNnKioqqKysJCQkhKCgIPdrdrud06dPA1BeXk6XLl2uu81vTokbEBBAfX39dZcNDw93P/b396ep\nqanN58BDQ0PdFxp+3Xy/vb5vbttms7kfm0wmbDZbiyk/MzIy3K/ruk5iYuL3vleInyJp9EJ4oYqK\nCpRS7mZfVlaGw+EgMjKS2tpa6urq3M2+rKwMq9UKNDe9y5cv3/DplwMCAmhoaHA/v3LlikcNt7y8\n3P1Y13XKy8uJjIzEbDYTFRUlX78T4gfI0L0QXqiqqort27fjdDrZv38/xcXFDBw4ELvdzs9+9jM2\nbtxIY2MjZ8+eZdeuXe5z06mpqWzatImLFy+ilOLs2bPU1NS0e774+Hg+/PBDdF3nyJEjHD9+3KP1\nnTlzhgMHDuByudi2bRt+fn707t2bXr16ERQUxNatW2lsbETXdb766iuKioraaU+E8H5yRC9EB7Vo\n0aIW36MfMGAAs2fPBqB3795cvHiRzMxMIiIimDFjhvtc+29+8xtefvllfv3rXxMSEsJDDz3kPgXw\n9fntBQsWUFNTQ9euXZk1a1a7Z8/IyGDFihW8++67JCUlkZSU5NH6HA4H+/btY8WKFURHRzNz5kws\nluY/X3PmzGH9+vVMmzYNp9NJbGwsDz/8cHvshhA+QeajF8LLfP31uueee87oKEIILyBD90IIIYQP\nk0YvhBBC+DAZuhdCCCF8mBzRCyGEED5MGr0QQgjhw6TRCyGEED5MGr0QQgjhw6TRCyGEED5MGr0Q\nQgjhw/4/zKYGnsCH5MEAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.68e-01\n",
- " final error(valid) = 1.75e-01\n",
- " final acc(train) = 9.50e-01\n",
- " final acc(valid) = 9.50e-01\n",
- " run time per epoch = 14.17\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=1.00\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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Hs0GqLD6o0ZPRqRPR+Tno9avRL/8GHRWNSp+HGjYGZZbwFkK0f3JNWzTL7dSw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\nrq6murqa/fv3B6bORURE7GR5gnANeRDXvJ9jTZ4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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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DQXTJZvSG1+DsKeiTibrvF6icqaioy79abgmafHIq1Mj28akG/KYmPTGaxSND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\nmkiXJoQQooP12DDvqrTHjS78G3rTO9D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NzbfcNioqKvTY5XLR2tp6x+eku3fvHvpy340G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\ngg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.98e-01\n",
- " final error(valid) = 2.10e-01\n",
- " final acc(train) = 9.41e-01\n",
- " final acc(valid) = 9.38e-01\n",
- " run time per epoch = 16.21\n"
- ]
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "num_epochs = 10 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "learning_rate = 0.2 # learning rate for gradient descent\n",
- "\n",
- "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n",
- "final_errors_train = []\n",
- "final_errors_valid = []\n",
- "final_accs_train = []\n",
- "final_accs_valid = []\n",
- "\n",
- "for init_scale in init_scales:\n",
- "\n",
- " print('-' * 80)\n",
- " print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
- " .format(learning_rate, init_scale))\n",
- " print('-' * 80)\n",
- " # Reset random number generator and data provider states on each run\n",
- " # to ensure reproducibility of results\n",
- " rng.seed(seed)\n",
- " train_data.reset()\n",
- " valid_data.reset()\n",
- "\n",
- " # Alter data-provider batch size\n",
- " train_data.batch_size = batch_size \n",
- " valid_data.batch_size = batch_size\n",
- "\n",
- " # Create a parameter initialiser which will sample random uniform values\n",
- " # from [-init_scale, init_scale]\n",
- " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- " # Create a model with three affine layers\n",
- " hidden_dim = 100\n",
- " model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
- " ])\n",
- "\n",
- " # Initialise a cross entropy error object\n",
- " error = CrossEntropySoftmaxError()\n",
- "\n",
- " # Use a basic gradient descent learning rule\n",
- " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
- "\n",
- " plt.show()\n",
- "\n",
- " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
- " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
- " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
- " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
- " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
- " \n",
- " final_errors_train.append(stats[-1, keys['error(train)']])\n",
- " final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
- " final_accs_train.append(stats[-1, keys['acc(train)']])\n",
- " final_accs_valid.append(stats[-1, keys['acc(valid)']])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n",
- "|------------|--------------------|--------------------|------------------|------------------|\n",
- "| 0.1 | 1.86e-01 | 1.83e-01 | 0.95 | 0.95 |\n",
- "| 0.2 | 1.74e-01 | 1.75e-01 | 0.95 | 0.95 |\n",
- "| 0.5 | 1.68e-01 | 1.75e-01 | 0.95 | 0.95 |\n",
- "| 1.0 | 1.98e-01 | 2.10e-01 | 0.94 | 0.94 |\n"
- ]
- }
- ],
- "source": [
- "j = 0\n",
- "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
- "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
- "for init_scale in init_scales:\n",
- " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n",
- " .format(init_scale, \n",
- " final_errors_train[j], final_errors_valid[j],\n",
- " final_accs_train[j], final_accs_valid[j]))\n",
- " j += 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Models with four affine layers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.10\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
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WR6Qr0FMZDgf8183gycR6+W9YFWXYrr8TIyGpWTm7zeCiYWmcNSCFv322l9c3\nVvCfrQf4yanp5OekYjOMCH0CEREJF+05R5BhGNimXI3xsxmw6QvMB+/EKmv91pIpsXZ++Z3ePHLh\nQPqlxDD/493MfGMbhaU1nVxrEREJN8OKYE+jXbt2RWrTUcf66jPMP82BmBj/Ie8BOUcua1m8t/UA\nz6zdS0WNlwmDU5gxYThWzYFOrHHP4PF4KC0tjXQ1uhW1aXh0hXb1mRY1DSY+y8JngWlZmCb4LAvT\nCjybTaZbKWM2ljUPzTeuy2f6p/3PFr7G9wWeG5cdXrZxe16zednkhHhuzHOH7PNnZWW1u6zCOYpY\nO7/FfPx+qKrEdt3tGCPzjlq+usHHCxvKeOXrcgzDwBXvICXWTkqsneTAs//RZHlc4DnGjt2mQ+Jt\n6Qp/8LoatWl4hLNdLcui3mdR3WAGHj6q6v3PwWX1JlXN5n1UBcv75+uiYBREu+E/XWgzDOw2sBsG\ndgNsNsM/3bjMZtArIZbfjG9/oLZF4dyFWfvKMZ/4DezYivHDX2I758I237PzQD3/2VnHrvJKDtT5\nOFDno7LOy4E686jXSSfF2AJB7mgl0P2PgWmxZCQ6MXrouW0FSej1lDa1LIt9tT5KqhrYc7CBkqoG\nSg42sK/WS6zdRrzT/0gIPMc7mk8fet1OvNOGo40f00dqV8uyqPNZVNUfCs7WpqsCAXr4dGMYt2fI\nhTiHQYLTTkLgcyXE2EkMTCfG2IOfzWEzsAVDEmxGYN4wsAXCsXH5oTKNy/0B2uw9TdZlbwzZxmlb\nIHwD5Y7lb1mov6vHEs7qEBZljF4ubDN/j/nkw1jP/RGzdA/G936CYTty94C+KTHcODir1S9Rvc+k\nMhDYB+p8HKhtDG8fB+q8weVl1Q1sqajlQJ2P+sN+3aYnODgpM4GTMhMYmZlAZlJMyD+3SFdzpPAt\nqfI/9lY1tPi/lBJrJy3OQZ3PpKbBpMZrtihzJE6bcSi8mwR4gtNGnMOG3VlG+cGa5nusgem27lBr\nQHDdiU47CTE20uId9EvxTycElsc7bSQ2mW98rTGQdTQudBTOUciIi8d2w91Yf1uI9caLUFYCP5uB\n4XQe87pi7DbcCTbcCe1/b53X5ECdj321XjaV1bJ+TzWf7qrinS3+c9oZiYGwzvAHtsJaugvLsrAA\nK3AO82C9eczhm5HoZECvWE7vm0RGopPMJCcZiU7SE53EO1v+yPYGzsM2hnV1gy84HVweCNvGZY3T\n+2p9FFfH5VXDAAAaW0lEQVTWU9NgEhfjIM4O8Q4bngRHMDATYw7tyTZOJwb2ahMCYRvnsOnKjyij\ncI5Sht0OP77ef6nVP5/F2leG7Ya7MRKTw77tWIeNdIeN9EQnQ93xTBqWhmVZbN9fz/o91azfU83q\nnVW8vbkxrJ3BveqTMhLISDr2HxEih7Msi1qvdegIT62P/YGjPvtrDx31aXw0duyxLAsTgtOWBSZN\npi2waCx7+PTRHU/4tsVhM0gOnFbqiJ5yuqCn0DnnLsD85H2sxfPAk4nt5v/FSO/dokxn/8c0A2G9\nYU816/dUsaGkhsrA4CgZiU5/UAcCOz2x64a1/uB1nNe0qPf5D9/Wey3iklP4dncZB+q87K8NhG3w\ntIs/dBtD+EiHfO2GPyhT4g51dnTaDIzAOUWb4T9UazP8yxrPNdogMG9gtDrdvFyC097h8O0s+q6G\nXiTPOSucuwhr4xeY82eD3Y7tpv/BGDSs2euR/o9pWhbf7qtjQ4l/z/qLPdVU1vt7kGQmOTkpIyEY\n2NEe1tUNPvZWedlb1UBsQhLVByubdSyx24xghxZHoPOJzQaOQOeVpuXshoHDdqhjSyRYgUtEGkx/\nj9uGwKPeZ9JgBubNJsuazTcJVl/zkK03Tf9za683mW7rfCf4z3c264wYCN3UWDspcYeuOkiN8+9h\nJjptPbaT4pFE+m9Ad6Rwlnaxinf4L7U6UIHtF7/GOHVM8LVo+4/ZGNbr91SzoaR5WKfF2XEnOHEl\nOHDF+x/uJtOueAfJsfaw/PG1LIsDdb7gOcO9Vd7gdOPzwfrw3AnMgGa9TA0ae48231tr3PPzzzcp\n06S8jca9RP/eHhj+APZZNDQJ3fpA0HaUzYAYu4HTbiPGbgQebU877TZi7QYxDgOnzUasw8DTKxWj\nobpZGDvt0btH2lVE29+A7kDhLO1mHajAfOK3sK0I46pfYJs4BYj+/5imZbEtENZbK+qoqPFSVuOl\nvMYbPBzelMNm4Iq3kxbv9Ad2ILzdTaZd8Q4SDtuD8pkW5TUtA7ekyktpYP7wQ6XxDlvgsKWD9MRD\nhzAzkpz09rgoLa/wD1gQGLTAa1qYgb1RnwWmeWj68MEPGgc18AYGPTAD729sk9bOh5qBTkmtnTM1\nA+dFW5t22BoD0R+EwWm7QYzNFpx22gLLAuWC08HXAu+1HQpVh+3YLkE5mmj/rnZVatfQi/pLqdat\nW8fixYsxTZOJEydy2WWXNXv9yy+/5Nlnn2Xbtm3MmDGDMWPGHGFN0lFGShq2X8/G/PMjWP940n+p\n1ZU/i3S12mQzDAalxTEoLa7Fa/U+k4pAUJfXeCmvbjJd42X7/jo+311FVUPLPdpYu4ErwUFyjJ19\ntV5Kq70tDqOmxtpJT3SSnRrLaVmJzQM40UlizJEPkXo8iaSiIVJFpHO1Gc6mabJo0SLuuece3G43\ns2bNIi8vj379+gXLeDwepk+fzquvvhrWyoqfERuH7fo7sQoWYS17Gau8BOv230W6Wsctxm4jMymm\nzUuyahoOC/GahmCQH6jz0Sc5oUnwOoIBHOvQIVMR6VraDOeioiJ69+5NZmYmAGPHjmXVqlXNwjkj\nIwMI3WEvaZths8PVv/BfavXC05TfMx1z3PkYw0diuDMiXb2w8A++EENWiq6rFpHurc1wLi8vx+0+\nNPC32+1m06ZNYa2UtI9hGBjnXYrlSsf3twVYz/yf/zpNTyZG7kmQezJG7kgMlyfSVRURkWPQqYOQ\nLFu2jGXLlgEwZ84cPB6FRkhccAn2iy6jdssmGtZ/Sv2GNdR/9gnWiuVYgL13X2JOGo1z5GnEnDQK\nuys90jXuMhwOh76nIaY2DQ+1a+hFsk3bDGeXy0VZWVlwvqysDJfLdVwby8/PJz8/PzivnoWh4/F4\n2J+YCmMmwJgJGKaJsWMr1sb1+L5eT82Kt6lZFugTkNk3sGc90r9nnZoW2cpHMfWADT21aXioXUMv\nqntr5+TkUFxcTElJCS6Xi5UrV3LzzTd3qIISfobNBv0HY/QfDPmXYpk+2L4Vq/BzrMINWKv+A++/\n6T8M3ruf/1x17kgYdhJGSq9IV19EpEdr13XOa9as4dlnn8U0TcaPH8/ll19OQUEBOTk55OXlUVRU\nxNy5c6mqqsLpdNKrVy8effTRNjeu65xD51h/4Vk+H3y7GWvjeqyv18OmL6EucMlQVn+M3JMwck+G\nEaMw4uLDVOvop72R0FObhofaNfQ0CIl0WEe/RJbPB9uKsArXYxWuh6KvoK4WklIwLvgexrmTemRI\n6w9e6KlNw0PtGnpRfVhbegbDbofBuRiDc+GiK7C8Xij6EvPNf2K9+CzWmy/16JAWEelMCmdpleFw\nwPCTsQ8/GeubrzFf/fuhkL7wcn9Ix7Yc7UtERDpOQydJm4yc4dhn3I/tzodgQA7W//cM5qxf+Peq\n62ojXT0RkW5H4SztFgzpOx6E7MFNQvolhbSISAgpnOWYGUNOwH5L05Be7A/ptxTSIiKhoHCW43Yo\npOdA9iCsF5qGdF2kqyci0mUpnKXDjCEnYr/lN9hunwP9BgZC+ueYby1RSIuIHAeFs4SMMfRE7Lc+\n0CSkn1ZIi4gcB4WzhFwwpGf+/lBI3/ULzH+/rJAWEWkHXecsYWMMG4H91gewNn6BufQfWM8vwnrz\nnxjjJ2Occjr0Hah7gIuItELhLGF3KKQ3YL76D6wlz2EteQ5S0zBOOBVGnIpx4qkYKbo7logIKJyl\nExnDTsJ+22+xKsqwvlwHX67F2vApfPSO/+5Y2YMwThyFMWIUDDkBwxkT6SqLiESEwlk6nZHmxjhr\nIpw1Ecs0YftmrC/WYn25DmvZK1hv/hNiYvy3rxwxCuPEUdAnW4fARaTHUDhLRBk2GwwYgjFgCEy6\nEqu2BjZuCIT1WqyCRf696l5ujBGnwojRGMNPwUhOiXTVRUTCRuEsUcWIi4eTT8c4+XQArLIS/x71\nF2uw1n4EK5ZjGQb0zzm0V52Ti+FwRrjmIiKho3CWqGa4MzC+ez5893ws0wdbi/x71F+sxXrjRax/\nvQCx8TBsBIY7HZJTITkVI/BMUiqkpEJiEobNHumPIyLSLgpn6TIMW5N7Tk+5Gqu6CgrX+8N605dY\nWwrhYCWA/1B48zdDUjIkpTQP72CYp0ByL2h8TkzyH3IXEYkAhbN0WUZCIowagzFqTHCZ5fNB1QGo\nPAAH9mEdPAAH9sPB/VC5H6sy8Lxzq79M1dHDvCy9N740N0Z6H0jPxPD0hvTe4ErHsGtPXETCQ+Es\n3Ypht0NKmv/RdwBt9e+2vF5/QFc2D+/Gh61yP+zYhrXuE/B5D4W4zQbuDPBkHgru9N4QCG8jITHM\nn1REujOFs/RohsMBqWn+B7QI8zSPh9LSUv/57opyKN2NtXc3BB5W6R6sNSvgYGXzve/EZH9wZ/QB\nT6Y/sD2ZkNEH0tw6/y0iR6VwFmkHw2YHdzq40zFyR7Z43aqugtI9zcLb2rsHa+smWLMSfL5D4W13\ngMsT2Ovu7Q9vTyC8PZmQlKxrukV6OIWzSAgYCYnQfzD0H9xi79vy+aCiNBDYu6F0N5SWYO3djbXm\nQzh4wF+u8Q1x8S0C20gPBLc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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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ZmcmePXtYv349iqKQnp7OvHnzANDpdMyePZunnnoKTdMYPHiwV4tanKF9s8M9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\nsIqKCrZv3+75j6hpGt988w3jx48HIDs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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 2.03e-03\n",
- " final error(valid) = 1.35e-01\n",
- " final acc(train) = 1.00e+00\n",
- " final acc(valid) = 9.73e-01\n",
- " run time per epoch = 19.51\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.20\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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6hiaPTZ/rOvUNBh7dwBUWFZQ6yr/unYRSCn59E8bu7ejPzke753GUI77V9ULM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\nUVIc7KoJIUSX1qYhX3l5eSx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jsC6cHLbX8db5Wbe/itWFXtYUVgWHvAemxzNtaDrnZiczwB2vfdAiIhGm0G6j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\nkS5LRESkWR06tPP2ekmLd9I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Q559/Pg6Hg9jY2FbXKNJeaHhc5Ay47777jrtP2+12Nxm2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\nIiIiUUKhLSIiEiX+H2dunxAXe5sDAAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 1.99e-03\n",
- " final error(valid) = 1.17e-01\n",
- " final acc(train) = 1.00e+00\n",
- " final acc(valid) = 9.75e-01\n",
- " run time per epoch = 20.58\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=0.50\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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aF8o5xVXkl7nrLBsZYtA+KoT2kSEkRtm896NstI8MISbUIn901iLf1cCQ3eO1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\nI3Tp0oXFixczZsyYgNUmu8dbkbi4ON9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- "text/plain": [
- ""
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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7qy8GdY2+ZoEcCOam+y6v3qy9JdJAiiWC9IRIsrpZSI6NoJc1Bjx1Z1zDmWrx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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 3.07e-03\n",
- " final error(valid) = 1.34e-01\n",
- " final acc(train) = 1.00e+00\n",
- " final acc(valid) = 9.71e-01\n",
- " run time per epoch = 20.54\n",
- "--------------------------------------------------------------------------------\n",
- "learning_rate=0.20 init_scale=1.00\n",
- "--------------------------------------------------------------------------------\n"
- ]
- },
- {
- "data": {
- "image/png": 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/zyW2dzqOC9KCvk273d5gbdqqFXTrUPu5I+VVbD9cyjeHS9ieW8r2vBLWbM73\nn/AS7bLTuVUkqZ4ITA1lVdWUVVo/5VXV/unymt++evZWbIYiwmFQVlnNiXsVnTaDdnFhtIs98Sec\ndrFhtIlxYa/HiXaBaFNftUmR18cRbxVHy30UV/iIctnwRDpJiHAS4bQ1if+4Ta05Wl5FbkkluSUV\n5NX8zi2p5HBxBYdLKzlcUkF51ckHWN0RDlpFOUlxR9I7wolPawpLKzni9fFtYSVHy6soqTz5Wt5j\nIp02YsMdVs8z3EFsuIP4cIe1ezjcQVy4A1Nr8korySuptH4f+ymppLjCd9I6nTaDhCgnCZFOuraJ\nJCHSefwnyklCpAt3hINwh61JnMvRkH//LUmo2jXoJ6K98MILuN1uDh06xBNPPEH79u1JSkqqNU9G\nRgYZGRn+6ea+K0fffj88MYGCP07CeGQWKjwiqNtrDLvHOkZAxwvCuPqCMMCD12ey+0gFOQVedhZW\nkFPoZdlXxTgM/HcwOnbThKQIg7AYu3867ISbJxx77sTpY8s6DOvEGZ+pOVxaxYHiSg6WVHGw5vee\n/FJW7S6sdXclQ1mXrCRFOUiq6Zkf66EnRTkJd1iB/v02NbWmtNK0zsT1+mpdHnP87Nzaz5dWnnlg\nF6dN4Q63Ex9uXb/qDrcRF263nguzno8Pt3qkgQqPalNTVmVSWllNSaVJaVU1pZVWrSU1v0urrPoL\nanrIBeW+k75I2RS4w+24IxykRNvpnRiGJ8KOJ9xBQoQdd4Qdd7jjpJ7yqT6rVdWaksrjlxoVn6Jt\niyqqyS0qY0duNUcrqk95xyybgvia9kuMsNPVE4U73I4nwmHVWvMT6TxTL9cH1T68xdBURrJvDH//\nzVGjPabtdrvJz8/3T+fn5+N2u+tdyLF5ExMT6datG7t27ToptFsaFROP8YsHMWc9iv7rc/CLh5pE\njyqQwuwGFyaE+wc1CSa7oWgT7aRNtPOk17TWFHqrrUD3h3oVB0oqydpTTHFF7V5eXJiNpCgnrWJy\nyS8u918qU3wWl8q0jnTUPLafsLvXRqTDRmlVNYXlvhN+qin0+thztIKNB32UnqK3aijrbnfHQjy+\nVqjbsClFadWx0D0evCWV5vHpmpAuP93pxidsK9JpI8pp4Ilw0K1VOO4IOwkRDiuUI6wQjA3gFwmH\nTfnfF9TvBM4Kn1kT8tUYNV8gYsJsclhENHl1hnZaWhoHDhwgNzcXt9tNVlYW999fv5OoSkpKcLlc\nOBwOiooOOLukAAAgAElEQVSK2LZtG9dff/15F90cqC49UGN+gn5rEXTuhrpidKhLapGUUv4eVvfW\nJ+/xKKms5mBxFQdLKv1hfrCkiv1HvYTbreP6pzzmGqRLZSp8Jke8NSdDlVf7T4oqqAn5I14fOYUV\nHPX6TvslIsJhEOU0iHTaiHQYJEY5iHSGEek0iHLYiDz22immm8pZxy67QSu7QavIhr8LoRDBVGdo\n22w27rzzTqZOnYppmgwfPpyUlBQWL15MWloa6enp7Nixg5kzZ1JaWsqaNWtYsmQJTz/9NPv27ePF\nF1/EMAxM02TMmDG1TmBr6dTVN6B3bEEvWYDu2AXVsUuoSxLfE+W00cljo5MnrNbzodrl6LIbJEY5\nSYw6ea/BiapNTXGFFeqmhiinQZTT1mzvxyxESyH3Hg8xXVqM+eQEAIzfzkZFRgd8G3JMK/CkTYND\n2jXwpE2DI1THtJvv3S+aCBUZjfHLh+FoAeb82WjzzMcUhRBCtFwS2o2A6tgZdctdsGk1etlroS5H\nCCFEIyWh3UioYSNRg0ag330NvSYr1OUIIYRohCS0GwmlFOp/xkHqhZgLZqP37gx1SUIIIRoZCe1G\nRDkcGPdMhogozOemoouL6l5ICCFEiyGh3cioODfGuClwtBBz7nS07+TbLAohhGiZJLQbIdWxM+pn\n98E3m9GLXwp1OUIIIRqJoN97XJwbY8AwzL070R+9jZncEePyH4S6JCGEECEmPe1GTI39KfToi/7H\nPPQ3m0NdjhBCiBCT0G7ElGHDuPsBaJWE+X/T0fm5oS5JCCFECEloN3IqIgrj3keguto6o7yiqQwI\nKIQQItAktJsAlZSM8YsHYd9u9MI/0whvFy+EEKIBSGg3EapHX9SNP0OvWYF+b0moyxFCCBECEtpN\niLpqDGrAMPQ/X0WvXxnqcoQQQjQwCe0mRCmF+sm90KEz5l9mo/ftDnVJQgghGpCEdhOjnC7rjmlh\n4ZjP/QFdIrc6FUKIlkJCuwlS8R6MeybBkXzMeU/JrU6FEKKFkNBuolTaRdau8q83ol9fEOpyhBBC\nNAC5jWkTZgwagblnFzrzn5jJHTCGXBXqkoQQQgSR9LSbOHXT7dCtN/rVuegdW0JdjhBCiCCS0G7i\nlM2G8YuJ4GmF+cIf0QWHQ12SEEKIIJHQbgZUZBTGrx+FqkrM56ehKypCXZIQQoggkNBuJlSbFIy7\nH4Q9OehFz8itToUQohmS0G5GVM9+qBt+gs7+HP3BG6EuRwghRIBJaDcz6gc3ovoPRS99Bb1hVajL\nEUIIEUAS2s2MUgr10/sgJRXzL7PQ+78LdUlCCCECREK7GVIuF8a9U8Dpwnx+Kqbc6lQIIZoFCe1m\nSrlbWbc6zT9MweRfoffuCnVJQgghzpOEdjOmOnXD+N/foUuKMKc9iPnJB3JWuRBCNGES2s2c6toL\n9+y/Qpfu6Ff/D3Pun9ClJaEuSwghxDmQ0G4BbHFujPt/h7rpDtjwJeaT49Hffh3qsoQQQpyleg0Y\nsn79ehYuXIhpmowYMYIxY8bUen3Lli0sWrSI3bt3M378eAYMGOB/7ZNPPuGtt94CYOzYsQwbNixw\n1Yt6U4aBuvoGdJfumC/OwHxqEur6H1uXiBny3U0IIZqCOv+3Nk2T+fPnM2XKFGbPns2KFSvYu3dv\nrXkSEhIYN24cgwcPrvV8SUkJb7zxBtOmTWPatGm88cYblJTIrtlQUh27YPx2DqrvZei3/4Y553fo\nIwWhLksIIUQ91BnaO3bsICkpicTEROx2O4MGDSI7O7vWPK1bt+aCCy5AKVXr+fXr19OzZ0+ioqKI\nioqiZ8+erF+/PrDvQJw1FRGJuvtB1E9/Dd9uxXzif9Gb14S6LCGEEHWoM7QLCgrweDz+aY/HQ0FB\n/Xpm31/W7XbXe1kRXEopjCFXYTzyNMTEYf7595ivL0T7qkJdmhBCiNOo1zHtYMvMzCQzMxOA6dOn\nk5CQEOKKmhe73X76Nk1IQM96meKXn6H8w7ex5XxN7ANPYE9q17BFNjFnbFNxzqRdA0/aNDhC1a51\nhrbb7SY/P98/nZ+fj9vtrtfK3W43W7Zs8U8XFBTQrVu3k+bLyMggIyPDP52Xl1ev9Yv6SUhIqLtN\nb7wDo8OF+P76LPm/+RnqJ/di9BvSMAU2QfVqU3HWpF0DT9o0OALdrm3btq3XfHXuHk9LS+PAgQPk\n5ubi8/nIysoiPT29Xivv3bs3GzZsoKSkhJKSEjZs2EDv3r3rtaxoeKrvIIzH/gxt26NfnIH51+dk\nbG4hhGhElK7HLbLWrl3LokWLME2T4cOHM3bsWBYvXkxaWhrp6ens2LGDmTNnUlpaisPhIC4ujqef\nfhqA5cuX8/bbbwPWJV/Dhw+vs6j9+/ef59sSJzrbb4Ta50O/+w9reM+kZIxfPIRK7hC8Apsg6b0E\nh7Rr4EmbBkeoetr1Cu2GJqEdWOf64dJbN2DOfxpKS1C33oW6/JqTrhBoqeQ/wuCQdg08adPgaLS7\nx0XLpbr2snaXX3Qx+tW5mHOnyy1QhRAihCS0xRmpmDiM+x6ruQXqKuua7h1bQ12WEEK0SBLaok7K\nMDCuvgHj4T+BzYY5YzLmstfkmm4hhGhgEtqi3lTHLhiPzkalD0b/8++YT4xHb9sc6rKEEKLFkNAW\nZ0VFRGLc/SDGr38LlRWYM6dgLpiNLjoS6tKEEKLZaxR3RBNNj+rVD+Oinuj3X0d/9BZ6wyrUDT9B\nDb0aZdhCXZ4QQjRL0tMW50y5XBg3/A/G756BlFTrDPM/TkTv3hHq0oQQolmS0BbnTbVJxnjgD6if\nPwAFhzGnPoj593nostJQlyaEEM2K7B4XAaGUQl16Ofrivuilr6I/+QC9ZgXqlrtQ/YfKTVmEECIA\npKctAkpFRGH86JcYj8yE+AT0X2ZhPv1b9IG9oS5NCCGaPAltERTqgk4YU2agfvwr2P0t5u/vx3z7\nbzIAiRBCnAcJbRE0yrBhDBuJ8YcXUP2GoN9/HfN396I3ZIe6NCGEaJIktEXQqZh4jLsmYDw4DZwu\nzOeepPr5aej8w6EuTQghmhQJbdFg1IU9MB6bgxr7M9iyFvOxcZgfvon2+UJdmhBCNAkS2qJBKbsD\n45obMZ54Abr1Rr+5yBqE5Bu5HaoQQtRFQluEhPK0xnbvIxi/ftS6HeqMKZjzZ6N3bacRDvEuhBCN\nglynLUJK9epv3Q71vSXofy1Fr/wPuBNQlwxE9RkInbrKbVGFEKKGhLYIOeUKQ439KfqqMegN2eh1\nX6A//RD98bsQHYvqfakV4Bf1RNkdoS5XCCFCRkJbNBoqKgZ12Qi4bATaW4betBbWfYFe9Tn6839B\neCSqZ7oV4N37oFxhoS5ZCCEalIS2aJRUWASq32DoNxhdVQlbNqDXZaHXr0J/+Sk4ndCjr7UbvWc6\nKiIq1CULIUTQSWiLRk85nNCrH6pXP3R1NXyz2dqFvm4leu0XaJsduva0Arz3paiYuFCXLIQQQSGh\nLZoUZbNB116orr3Qt/0Cdn5jBfjaL9B/ex79yv9B565WgF8yEOVpFeqShRAiYCS0RZOlDAPSLkKl\nXYS+8XbYt8sK73Ur0Yv/gl7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KYvz48QCkpKQwcOBAfvOb32AYBnfddReGUef3BCGEEEKc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\nRK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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206dPb7W+srKSJUuWUFtbS2xsLLm5uVgsFgBuu+02+vXrB4DVauXf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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " final error(train) = 7.72e-03\n",
- " final error(valid) = 1.62e-01\n",
- " final acc(train) = 1.00e+00\n",
- " final acc(valid) = 9.62e-01\n",
- " run time per epoch = 21.37\n"
- ]
- }
- ],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "learning_rate = 0.2 # learning rate for gradient descent\n",
- "\n",
- "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n",
- "final_errors_train = []\n",
- "final_errors_valid = []\n",
- "final_accs_train = []\n",
- "final_accs_valid = []\n",
- "\n",
- "for init_scale in init_scales:\n",
- "\n",
- " print('-' * 80)\n",
- " print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
- " .format(learning_rate, init_scale))\n",
- " print('-' * 80)\n",
- " # Reset random number generator and data provider states on each run\n",
- " # to ensure reproducibility of results\n",
- " rng.seed(seed)\n",
- " train_data.reset()\n",
- " valid_data.reset()\n",
- "\n",
- " # Alter data-provider batch size\n",
- " train_data.batch_size = batch_size \n",
- " valid_data.batch_size = batch_size\n",
- "\n",
- " # Create a parameter initialiser which will sample random uniform values\n",
- " # from [-init_scale, init_scale]\n",
- " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- " # Create a model with four affine layers\n",
- " hidden_dim = 100\n",
- " model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
- " ])\n",
- "\n",
- " # Initialise a cross entropy error object\n",
- " error = CrossEntropySoftmaxError()\n",
- "\n",
- " # Use a basic gradient descent learning rule\n",
- " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
- "\n",
- " plt.show()\n",
- "\n",
- " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
- " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
- " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
- " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
- " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
- "\n",
- " final_errors_train.append(stats[-1, keys['error(train)']])\n",
- " final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
- " final_accs_train.append(stats[-1, keys['acc(train)']])\n",
- " final_accs_valid.append(stats[-1, keys['acc(valid)']])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n",
- "|------------|--------------------|--------------------|------------------|------------------|\n",
- "| 0.1 | 2.03e-03 | 1.35e-01 | 1.00 | 0.97 |\n",
- "| 0.2 | 1.99e-03 | 1.17e-01 | 1.00 | 0.97 |\n",
- "| 0.5 | 3.07e-03 | 1.34e-01 | 1.00 | 0.97 |\n",
- "| 1.0 | 7.72e-03 | 1.62e-01 | 1.00 | 0.96 |\n"
- ]
- }
- ],
- "source": [
- "j = 0\n",
- "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
- "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
- "for init_scale in init_scales:\n",
- " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n",
- " .format(init_scale, \n",
- " final_errors_train[j], final_errors_valid[j],\n",
- " final_accs_train[j], final_accs_valid[j]))\n",
- " j += 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Models with five affine layers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set training run hyperparameters\n",
- "batch_size = 100 # number of data points in a batch\n",
- "num_epochs = 100 # number of training epochs to perform\n",
- "stats_interval = 5 # epoch interval between recording and printing stats\n",
- "learning_rate = 0.2 # learning rate for gradient descent\n",
- "\n",
- "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n",
- "final_errors_train = []\n",
- "final_errors_valid = []\n",
- "final_accs_train = []\n",
- "final_accs_valid = []\n",
- "\n",
- "for init_scale in init_scales:\n",
- "\n",
- " print('-' * 80)\n",
- " print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
- " .format(learning_rate, init_scale))\n",
- " print('-' * 80)\n",
- " # Reset random number generator and data provider states on each run\n",
- " # to ensure reproducibility of results\n",
- " rng.seed(seed)\n",
- " train_data.reset()\n",
- " valid_data.reset()\n",
- "\n",
- " # Alter data-provider batch size\n",
- " train_data.batch_size = batch_size \n",
- " valid_data.batch_size = batch_size\n",
- "\n",
- " # Create a parameter initialiser which will sample random uniform values\n",
- " # from [-init_scale, init_scale]\n",
- " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
- "\n",
- " # Create a model with five affine layers\n",
- " hidden_dim = 100\n",
- " model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
- " SigmoidLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
- " ])\n",
- "\n",
- " # Initialise a cross entropy error object\n",
- " error = CrossEntropySoftmaxError()\n",
- "\n",
- " # Use a basic gradient descent learning rule\n",
- " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
- "\n",
- " plt.show()\n",
- "\n",
- " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
- " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
- " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
- " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
- " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
- "\n",
- " final_errors_train.append(stats[-1, keys['error(train)']])\n",
- " final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
- " final_accs_train.append(stats[-1, keys['acc(train)']])\n",
- " final_accs_valid.append(stats[-1, keys['acc(valid)']])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "j = 0\n",
- "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
- "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
- "for init_scale in init_scales:\n",
- " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n",
- " .format(init_scale, \n",
- " final_errors_train[j], final_errors_valid[j],\n",
- " final_accs_train[j], final_accs_valid[j]))\n",
- " j += 1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> How does increasing the number of layers affect the model's performance on the training data set? And on the validation data set?\n",
- "\n",
- "\n",
- "The best final training set error across the four initialisation scales used above for each model architecture, consistently decreases as we increase the number of layers.\n",
- "\n",
- "\n",
- "| Number of affine layers | Best final training set error |\n",
- "|-------------------------|-------------------------------|\n",
- "| 2 | $1.85 \\times 10^{-2}$ |\n",
- "| 3 | $5.21 \\times 10^{-3}$ |\n",
- "| 4 | $1.99 \\times 10^{-3}$ |\n",
- "| 5 | $1.14 \\times 10^{-3}$ |\n",
- "\n",
- "\n",
- "This makes sense as because the number of layers increase, for a fixed hidden layer width, the total number of free parameters in the model increases and so we would expect the model to be able to fit too the training data better.\n",
- "\n",
- "\n",
- "\n",
- "If we look at the validation set however we see the opposite trend; as the number of layers increases the best final validation set error increases.\n",
- "\n",
- "\n",
- "| Number of affine layers | Best final validation set error |\n",
- "|-------------------------|---------------------------------|\n",
- "| 2 | $7.47 \\times 10^{-2}$ |\n",
- "| 3 | $8.77 \\times 10^{-2}$ |\n",
- "| 4 | $1.17 \\times 10^{-1}$ |\n",
- "| 5 | $1.47 \\times 10^{-1}$ |\n",
- "\n",
- "\n",
- "If we look more closely at the training curves for the models with more layers we can see what is happening here. For the models with three or more layers, after a certain number of epochs the validation set error begins to *increase* even as the training set error continues to decrease. This indicates that these models have begun *overfitting* to the training data. We could get a better validation set error in these cases by stopping the training early. *Early stopping* like this is one way of trying to overcome overfitting, in later labs we will consider other methods for improving generalisation by reducing overfitting.\n",
- "\n",
- "\n",
- "> Do deeper models seem to be harder or easier to train (e.g. in terms of ease of choosing training hyperparameters to give good final performance and/or quick convergence)?\n",
- "\n",
- "> Do the models seem to be sensitive to the choice of the parameter initialisation range? Can you think of any reasons for why setting individual parameter initialisation scales for each AffineLayer in a model might be useful? Can you come up with (or find) any heuristics for setting the parameter initialisation scales?\n",
- "\n",
- "\n",
- "The final performance of the deeper models becomes increasingly sensitive to the choice of parameter initialisation. For the models with two affine layers, the final training errors for initialisation scales 0.1, 0.2 and 0.5 are all within approximately 10% of each other, while for the models with five affine layers there is an approximately 400% increase in final training error if moving from an initialisation scale of 0.2 to 0.1 and a 50% increase in final training error when moving from 0.2 to 0.5. The smaller parameter initialisation scales for the deeper models in particular seem to give poorer initial performance (error curves start from higher values) and for the five affine layer model the smallest parameter initialisation scale run shows a pronounced flatter section at the start of training with around 15 epochs before the error starts significantly decreasing.\n",
- "\n",
- "\n",
- "\n",
- "In general the models with more layers also take longer to train per epoch, so on top of issues of potential overfitting and difficulty of choosing parameter initilisations we also need to factor in the potentially slower training of deeper models if computational time is a key constraint.\n",
- "\n",
- "\n",
- "\n",
- "We might expect the appropriate initialisation scale for a given affine layer to depend on its input and output dimensionalities. Each output is calculated as the weighted sum of all the inputs, and so for a larger number of inputs the typical magnitude of the output activations will become larger as each will be calculate from a sum over more values. Similarly the backpropagated gradient at each input is calculated as a weighted sum over the gradients at each output, and so for a larger number outputs the typical magnitude of backpropagated gradients will become larger.\n",
- "\n",
- "\n",
- "\n",
- "If we wish to keep some measure of the typical magnitude of the activations and backpropagated gradients at a given layer roughly constant through the network then we may therefore wish to set the parameter initialisation in a layer dimensionality dependent way. One heuristic based on trying to achieve a roughly constant variance in activations and backpropagated gradients through the network is to initialise the weights for a layer from a distribution with variance inversely proportional to the sum of the input and output dimensions of the layer. This is sometimes known as the Glorot or Xavier initialisation, after the name of the author of [the paper](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) in which this scheme was proposed. This is discussed in [lecture 4](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp04-learn.pdf).\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 3: Hyperbolic tangent and rectified linear layers\n",
- "\n",
- "In the models we have been investigating so far we have been applying elementwise logistic sigmoid transformations to the outputs of intermediate (affine) layers. The logistic sigmoid is just one particular choice of an elementwise non-linearity we can use. \n",
- "\n",
- "As mentioned in [lecture 3](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp03-mlp.pdf), although logistic sigmoid has some favourable properties in terms of interpretability, there are also disadvantages from a computational perspective. In particular that the gradients of the sigmoid become very close to zero (and may actually become exactly zero to a finite numerical precision) for very positive or negative inputs, and that the outputs are non-centred - they cover the interval $[0,\\,1]$ so negative outputs are never produced.\n",
- "\n",
- "Two alternative elementwise non-linearities which are often used in multiple layer models are the hyperbolic tangent and the rectified linear function.\n",
- "\n",
- "For a hyperbolic tangent (`Tanh`) layer the forward propagation corresponds to\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_k = \n",
- " \\tanh\\left(x^{(b)}_k\\right) = \n",
- " \\frac{\\exp\\left(x^{(b)}_k\\right) - \\exp\\left(-x^{(b)}_k\\right)}{\\exp\\left(x^{(b)}_k\\right) + \\exp\\left(-x^{(b)}_k\\right)}\n",
- "\\end{equation}\n",
- "\n",
- "which has corresponding partial derivatives\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = \n",
- " \\begin{cases} \n",
- " 1 - \\left(y^{(b)}_k\\right)^2 & \\quad k = d \\\\\n",
- " 0 & \\quad k \\neq d\n",
- " \\end{cases}.\n",
- "\\end{equation}\n",
- "\n",
- "For a rectified linear (`Relu`) layer the forward propagation corresponds to\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_k = \n",
- " \\max\\left(0,\\,x^{(b)}_k\\right)\n",
- "\\end{equation}\n",
- "\n",
- "which has corresponding partial derivatives\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = \n",
- " \\begin{cases} \n",
- " 1 & \\quad k = d \\quad\\textrm{and} &x^{(b)}_d > 0 \\\\\n",
- " 0 & \\quad k \\neq d \\quad\\textrm{or} &x^{(b)}_d < 0\n",
- " \\end{cases}.\n",
- "\\end{equation}\n",
- "\n",
- "Using these definitions implement the `fprop` and `bprop` methods for the skeleton `TanhLayer` and `ReluLayer` class definitions below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from mlp.layers import Layer\n",
- "\n",
- "class TanhLayer(Layer):\n",
- " \"\"\"Layer implementing an element-wise hyperbolic tangent transformation.\"\"\"\n",
- "\n",
- " def fprop(self, inputs):\n",
- " \"\"\"Forward propagates activations through the layer transformation.\n",
- "\n",
- " For inputs `x` and outputs `y` this corresponds to `y = tanh(x)`.\n",
- " \"\"\"\n",
- " return np.tanh(inputs)\n",
- "\n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " \"\"\"Back propagates gradients through a layer.\n",
- "\n",
- " Given gradients with respect to the outputs of the layer calculates the\n",
- " gradients with respect to the layer inputs.\n",
- " \"\"\"\n",
- " return (1. - outputs**2) * grads_wrt_outputs\n",
- "\n",
- " def __repr__(self):\n",
- " return 'TanhLayer'\n",
- " \n",
- "\n",
- "class ReluLayer(Layer):\n",
- " \"\"\"Layer implementing an element-wise rectified linear transformation.\"\"\"\n",
- "\n",
- " def fprop(self, inputs):\n",
- " \"\"\"Forward propagates activations through the layer transformation.\n",
- "\n",
- " For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.\n",
- " \"\"\"\n",
- " return np.maximum(inputs, 0.)\n",
- "\n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " \"\"\"Back propagates gradients through a layer.\n",
- "\n",
- " Given gradients with respect to the outputs of the layer calculates the\n",
- " gradients with respect to the layer inputs.\n",
- " \"\"\"\n",
- " return (outputs > 0) * grads_wrt_outputs\n",
- "\n",
- " def __repr__(self):\n",
- " return 'ReluLayer'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Test your implementations by running the cells below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Outputs and gradients calculated correctly for TanhLayer.\n"
- ]
- }
- ],
- "source": [
- "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n",
- "test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n",
- "test_tanh_outputs = np.array(\n",
- " [[ 0.09966799, -0.19737532, 0.29131261],\n",
- " [-0.37994896, 0.46211716, -0.53704957]])\n",
- "test_tanh_grads_wrt_inputs = np.array(\n",
- " [[ 4.95033145, 9.61042983, -9.15136962],\n",
- " [-4.27819393, 0., 7.11577763]])\n",
- "tanh_layer = TanhLayer()\n",
- "tanh_outputs = tanh_layer.fprop(test_inputs)\n",
- "all_correct = True\n",
- "if not tanh_outputs.shape == test_tanh_outputs.shape:\n",
- " print('TanhLayer.fprop returned array with wrong shape.')\n",
- " all_correct = False\n",
- "elif not np.allclose(test_tanh_outputs, tanh_outputs):\n",
- " print('TanhLayer.fprop calculated incorrect outputs.')\n",
- " all_correct = False\n",
- "tanh_grads_wrt_inputs = tanh_layer.bprop(\n",
- " test_inputs, tanh_outputs, test_grads_wrt_outputs)\n",
- "if not tanh_grads_wrt_inputs.shape == test_tanh_grads_wrt_inputs.shape:\n",
- " print('TanhLayer.bprop returned array with wrong shape.')\n",
- " all_correct = False\n",
- "elif not np.allclose(tanh_grads_wrt_inputs, test_tanh_grads_wrt_inputs):\n",
- " print('TanhLayer.bprop calculated incorrect gradients with respect to inputs.')\n",
- " all_correct = False\n",
- "if all_correct:\n",
- " print('Outputs and gradients calculated correctly for TanhLayer.')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Outputs and gradients calculated correctly for ReluLayer.\n"
- ]
- }
- ],
- "source": [
- "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n",
- "test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n",
- "test_relu_outputs = np.array([[0.1, 0., 0.3], [0., 0.5, 0.]])\n",
- "test_relu_grads_wrt_inputs = np.array([[5., 0., -10.], [-0., 0., 0.]])\n",
- "relu_layer = ReluLayer()\n",
- "relu_outputs = relu_layer.fprop(test_inputs)\n",
- "all_correct = True\n",
- "if not relu_outputs.shape == test_relu_outputs.shape:\n",
- " print('ReluLayer.fprop returned array with wrong shape.')\n",
- " all_correct = False\n",
- "elif not np.allclose(test_relu_outputs, relu_outputs):\n",
- " print('ReluLayer.fprop calculated incorrect outputs.')\n",
- " all_correct = False\n",
- "relu_grads_wrt_inputs = relu_layer.bprop(\n",
- " test_inputs, relu_outputs, test_grads_wrt_outputs)\n",
- "if not relu_grads_wrt_inputs.shape == test_relu_grads_wrt_inputs.shape:\n",
- " print('ReluLayer.bprop returned array with wrong shape.')\n",
- " all_correct = False\n",
- "elif not np.allclose(relu_grads_wrt_inputs, test_relu_grads_wrt_inputs):\n",
- " print('ReluLayer.bprop calculated incorrect gradients with respect to inputs.')\n",
- " all_correct = False\n",
- "if all_correct:\n",
- " print('Outputs and gradients calculated correctly for ReluLayer.')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/04_Generalisation_and_overfitting.ipynb b/notebooks/04_Generalisation_and_overfitting.ipynb
deleted file mode 100644
index ca58f4f..0000000
--- a/notebooks/04_Generalisation_and_overfitting.ipynb
+++ /dev/null
@@ -1,543 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Generalisation and overfitting\n",
- "\n",
- "In this notebook we will explore the issue of overfitting and how we can measure how well the models we train generalise their predictions to unseen data. This will build upon the introduction to generalisation given in the [fourth lecture](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp04-learn.pdf)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise: overfitting and model complexity in a 1D regression problem\n",
- "\n",
- "As an exercise we will consider a regression problem. In particular we will attempt to use a multiple layer network model to learn to predict output values from inputs, given a fixed set of (noisy) observations of the underlying functional relationship between inputs and outputs. The aim of the exercise will be to visualise how increasing the complexity of the model we fit to the training data effects the ability of the model to make predictions across the input space.\n",
- "\n",
- "### Function\n",
- "\n",
- "To keep things simple we will consider a single input-output function defined by a fourth degree polynomial (quartic)\n",
- "\n",
- "$$ f(x) = 10 x^4 - 17 x^3 + 8 x^2 - x $$\n",
- "\n",
- "with the observed values being the function values plus zero-mean Gaussian noise\n",
- "\n",
- "$$ y = f(x) + 0.01 \\epsilon \\qquad \\epsilon \\sim \\mathcal{N}\\left(\\cdot;\\,0,\\,1\\right) $$\n",
- "\n",
- "The inputs will be drawn from the uniform distribution on $[0, 1]$.\n",
- "\n",
- "First import the necessary modules and seed the random number generator by running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "plt.style.use('ggplot')\n",
- "seed = 17102016 \n",
- "rng = np.random.RandomState(seed)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Write code in the cell below to calculate a polynomial function of one dimensional inputs. \n",
- "\n",
- "If $\\boldsymbol{c}$ is a length $P$ vector of coefficients corresponding to increasing powers in the polynomial (starting from the constant zero power term up to the $P-1^{\\textrm{th}}$ power) the function should correspond to the following\n",
- "\n",
- "\\begin{equation}\n",
- " f_{\\textrm{polynomial}}(x,\\ \\boldsymbol{c}) = \\sum_{p=0}^{P-1} \\left( c_p x^p \\right)\n",
- "\\end{equation}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "def polynomial_function(inputs, coefficients):\n",
- " \"\"\"Calculates polynomial with given coefficients of an array of inputs.\n",
- " \n",
- " Args:\n",
- " inputs: One-dimensional array of input values of shape (num_inputs,)\n",
- " coefficients: One-dimensional array of polynomial coefficient terms\n",
- " with `coefficients[0]` corresponding to the coefficient for the\n",
- " zero order term in the polynomial (constant) and `coefficients[-1]`\n",
- " corresponding to the highest order term.\n",
- " \n",
- " Returns:\n",
- " One dimensional array of output values of shape (num_inputs,)\n",
- " \n",
- " \"\"\"\n",
- " return (inputs[:, None]**np.arange(coefficients.shape[0])).dot(coefficients)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Run the cell below to test your implementation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_coefficients = np.array([-1., 3., 4.])\n",
- "test_inputs = np.array([0., 0.5, 1., 2.])\n",
- "test_outputs = np.array([-1., 1.5, 6., 21.])\n",
- "assert polynomial_function(test_inputs, test_coefficients).shape == (4,), (\n",
- " 'Function gives wrong shape output.'\n",
- ")\n",
- "assert np.allclose(polynomial_function(test_inputs, test_coefficients), test_outputs), (\n",
- " 'Function gives incorrect output values.'\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We now need to use the random number generator to sample input values and calculate the corresponding target outputs using your polynomial implementation with the relevant coefficients for our function. Do this by running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "coefficients = np.array([0, -1., 8., -17., 10.])\n",
- "input_dim, output_dim = 1, 1\n",
- "noise_std = 0.01\n",
- "num_data = 80\n",
- "inputs = rng.uniform(size=(num_data, input_dim))\n",
- "epsilons = rng.normal(size=num_data)\n",
- "targets = (polynomial_function(inputs[:, 0], coefficients) + \n",
- " epsilons * noise_std)[:, None]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We will split the generated data points in to equal sized training and validation data sets and use these to create data provider objects which we can use to train models in our framework. As the dataset is small here we will use a batch size equal to the size of the data set. Run the cell below to split the data and set up the data provider objects."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "from mlp.data_providers import DataProvider\n",
- "num_train = num_data // 2\n",
- "batch_size = num_train\n",
- "inputs_train, targets_train = inputs[:num_train], targets[:num_train]\n",
- "inputs_valid, targets_valid = inputs[num_train:], targets[num_train:]\n",
- "train_data = DataProvider(inputs_train, targets_train, batch_size=batch_size, rng=rng)\n",
- "valid_data = DataProvider(inputs_valid, targets_valid, batch_size=batch_size, rng=rng)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can now visualise the data we will be modelling. Run the cell below to plot the target outputs against inputs for both the training and validation sets. Note the clear underlying smooth functional relationship evident in the noisy data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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h1qxZHglWRORcYRYWYG7bjNErDSM5xdfhiAQclwucV155pck2Q4YM4Z133mlV\nQCIi5zqzsADHi0+CzYZptWJ5+FkVOSLN5Na9qKKionj66afdeUkRkXOOuW0z2GxgOsBuqzkWkWZx\na4FjGAYXX3yxOy8pInLOMXqlgdUKFguEWGuORaRZtBeViIifMZJTsDz8rObgiLSC3xU4lZWVzJ49\nm6KiIuLj45k8eTIRERH12k2fPp3t27eTkpLCY4895nz98OHDvPTSS1RUVNC9e3fuv//+OpOhRUQC\ngZGcosJGpBVcHqLau3dvnZV+v/nmG+bMmcP777/v1m0bsrOzSUtLY86cOaSlpZGdnd1guxEjRnDf\nfffVe/3tt99m+PDhzJ07l3bt2rFq1Sq3xSYiIiKBweUC55VXXmHXrl0AFBcX84c//IFjx46xfPly\n3n33XbcFlJeXR0ZGBlDz6HleXl6D7dLS0ggLq7vyp2mabNmyhYEDBwIwePDgRs8XERGR4OXy2M2+\nffu44IILAFi3bh09evRgypQp5Ofn88orr3DLLbe4JaDy8nJiYmIAaN++PeXlru+sW1FRQXh4OCEh\nIQDExsZSWlraaPucnBxycnIAmDFjBnFxca2IvD6r1er2a0p9yrN3KM/eoTx7h/LsHb7Ms8sFjsPh\ncM5lyc/Pd26ymZiYyJEjR5r1oVlZWQ2eM3bs2DrHhmFgGEazrt0cmZmZZGZmOo/dvSGYNnPzDuXZ\nO5Rn71CevUN59o6A2GyzS5cufPbZZ1x22WVs3rzZ2WNTWlpKVFRUs4KbOnVqo+9FR0dTVlZGTEwM\nZWVlzbp2ZGQkVVVV2O12QkJCKC0tJTY2tlmxiYiISOBzeQ7OuHHjWLlyJdOmTeOnP/0pXbt2BWD9\n+vUkJye7LaD09HRyc3MByM3NpV+/fi6faxgGvXv3Zt26dQCsXr2a9PR0t8UmIuItBUXHWZpfQkHR\ncV+HIhKQDNM0TVcbOxwOqqqq6jy2ffjwYdq2bUt0dLRbAqqoqGD27NkUFxfXeUy8sLCQFStWMGHC\nBACeeuop9u3bx4kTJ4iMjGTChAn06dOHQ4cO8dJLL1FZWckFF1zA/fffT2hoqEufffpTYu6gLlDv\nUJ69Q3n2jri4ONb8dw9TV36PzW5iDTHIGtaVlPiwpk8Wl+n72Tt8OUTlcoFTXFxMhw4d6s2JMU2T\nkpKSoJispQInMCnP3qE8e0dcXByvrt7G4k1FOACLAeMuiWd0agdfhxZU9P3sHb4scFweopo4cSJH\njx6t93oU0gFxAAAd70lEQVRlZSUTJ050PTIRETmr1IRwrCEGFgOsFoPUhHBfhyQScJq1xG9DTzSd\nOHGCNm3auC0gEZFzXUp8GFnDupJ/qIrUhHANT4m0QJMFzuuvv+7877/85S91ihmHw0FhYSHdunXz\nSHAiIueqlPgwFTYirdBkgbNnzx7nf+/bt6/Ovk5Wq5ULLriA66+/3jPRiYiIiLRAkwXO008/DcDL\nL7/MHXfcQXi4xoJFRETEv7k8B+fee+/1ZBwiItJMZmEB5rbNGL3StPO4yBlcLnB+//vfn/X93/72\nt60ORkREmmYWFmD+axXmmhxwODCtViwPP6siR+Q0Lhc4kZGRdY5tNhu7d++mpKSE/v37uz0wERGp\nzywswPHik1B96ocX7baanhwVOCJOrR6iWrRoEWFhmukvIuIN5rbNYLP98IJhQIgVo1ea74IS8UMu\nL/TXmMzMTJYvX+6OWEREpAlGrzSwWsFiAWso/OxqDU+JNKBZC/01xN3bG4iISOOM5BQsDz+rycUi\nTXC5wDl9wb9aZWVlbNy4kSFDhrg1KBERaZyRnKLCRqQJLhc4py/4BzXbNkRFRXH77berwBERERG/\n4nKBU7vgn4iIiIi/a9Ek4xMnTnDixAl3xyIiIiLiFs2aZPzRRx+xbNkySktLAYiNjWX48OEMHz68\nwZ3GRURERHzB5QLn7bffJicnhxEjRtCzZ08Avv32W/72t79x5MgRxo8f77EgRURERJrD5QJn5cqV\nTJgwgYEDBzpfS01NJSkpiT/+8Y8qcERERMRvNGsOTteuXRt8zTRNtwUkIiKeZxYW4Ph4CWZhga9D\nEfEIl3twMjIyWL58OXfeeWed1z/77DN+9rOfuS2gyspKZs+eTVFREfHx8UyePJmIiIh67aZPn872\n7dtJSUnhsccec74+f/58tm7dSnh4OAATJ06kW7dubotPRCTQOfezstm0UacELZcLnOrqatasWcOm\nTZvo0aMHADt27KC0tJSf/exndRYCvOuuu1ocUHZ2NmlpaYwcOZLs7Gyys7MbHP4aMWIEJ0+eJCcn\np957t956a52hNBER+YFzPyvToY06JWi5PES1f/9+unfvTkxMDMXFxRQXF9O+fXu6d+/Ovn372LNn\nj/N/rZGXl0dGRgZQ02uUl5fXYLu0tDRt8iki0gJ19rPSRp0SpPxuob/y8nJiYmIAaN++PeXl5c2+\nxjvvvMPSpUtJTU1l3LhxhIaGNtguJyfH2QM0Y8YM4uLiWh54A6xWq9uvKfUpz96hPHuHV/IcN4hT\nz8ylesvXhPbuS5uUc6/A0fezd/gyzy4XOL///e8bfc8wDB599FGXPzQrK4sjR47Ue33s2LH1rtvc\n9XVuueUW2rdvj81mY8GCBXzwwQeMHj26wbaZmZlkZmY6j4uLi5v1WU2Ji4tz+zWlPuXZO5Rn7/Ba\nnuM6QUYnjgOcg19XfT97hyfynJSU5FI7lwucyMjIOsc2m43du3dTUlJC//79mxXc1KlTG30vOjqa\nsrIyYmJiKCsrIyoqqlnXru39CQ0NZciQIXz44YfNOl9ERORcUFB0nPxDVaQmhJMSH3xTPlwucO69\n994GX1+0aJFb58Kkp6eTm5vLyJEjyc3NpV+/fs06v7Y4Mk2TvLw8unTp4rbYREREgkFB0XGmrvwe\nm93EGmKQNaxr0BU5LdqL6nSZmZksX77cHbEAMHLkSL755hseeOABNm/ezMiRIwEoLCzk1VdfdbZ7\n6qmnmDVrFps3b2bChAls3LgRgDlz5vDwww/zyCOPcPToUW688Ua3xSYiIhIM8g9VYbObOACbwyT/\nUJWvQ3K7Zu1F1ZD9+/e7Iw6nyMhInnrqqXqvJycnk5yc7Dx+5plnGjxfu56LiIicXWpCONYQA5vD\nxGoxSE0I93VIbudygXP6Oje1ysrK2LhxI0OGDHFrUCIiIuI5KfFhZA3rqjk4QL31bQzDICoqittv\nv10FjoiISIBJiQ8LysKmlt+tgyMiIiLSWs2ag1NVVcWBAwcASExMpF27dh4JSkRERKQ1XCpwiouL\n+dOf/sTGjRudO4cbhkHfvn256667iI+P92iQIiIiIs3RZIFTWlrKE088gWEY3HTTTXTu3BmAvXv3\nsnz5cp588kmef/55YmNjPR6siIiIiCuaLHCWLFlCx44dmTp1Km3atHG+3r9/f4YPH86zzz7L0qVL\nueeeezwaqIiIiIirmlzo7+uvv+bmm2+uU9zUatu2LWPHjmXDhg0eCU5ERESkJZoscI4ePUpCQkKj\n7ycmJnL06FG3BiUiIiLSGk0WONHR0Rw8eLDR9w8cOEB0dLRbgxIRERFpjSYLnD59+vDuu+9SXV1d\n771Tp07x3nvv0bdvX48EJyIiItISTU4yHjNmDFOmTOGBBx7g6quv5rzzzgNqnqL67LPPsNvtTJ48\n2eOBioiIZ5mFBZjbNmP0SsNITvF1OCKt0mSBExsbS1ZWFgsXLuSdd96p816fPn2466679Ii4iEiA\nMwsLcLz4JNhsmFYrloefVZEjAc2lhf46duzIlClTqKysdM7HSUxMJCIiwqPBiYiId5jbNoPNBqYD\n7LaanhwVOBLAmrVVQ0REBBdeeKGnYhERER8xeqVhWq1gt0GIFaNXmq9DEmmVZhU4IiISnIzkFCwP\nP4tj7SoMw9fRiLRek09RiYjIOeRfqzC/+AzHi09iFhb4OhqRFlOBIyIiQMPzcEQClQocEREBaubh\nYLWCxaJ5OBLw/G4OTmVlJbNnz6aoqIj4+HgmT55c72mt7777jtdee43jx49jsVgYNWoUl19+OQCH\nDx/mpZdeoqKigu7du3P//fdjtfrdbYqI+J3aeThaC0eCgd/14GRnZ5OWlsacOXNIS0sjOzu7Xps2\nbdpw3333MWvWLB5//HHefPNNjh07BsDbb7/N8OHDmTt3Lu3atWPVqlXevgURkYBlJKdguXaMihsJ\neH5X4OTl5ZGRkQFARkYGeXl59dokJSXRqVMnoGYhwujoaI4ePYppmmzZsoWBAwcCMHjw4AbPFxGR\nhhUUHWdpfgkFRcd9HYpIq/jd2E15eTkxMTEAtG/fnvLy8rO237FjBzabjYSEBCoqKggPDyckJASo\nKX5KS0sbPTcnJ4ecnBwAZsyYQVxcnJvuoobVanX7NaU+5dk7lGfv8GWe8w8c5alV31JtdxAaYmHO\nqFRSO0X5JBZP0/ezd/gyzz4pcLKysjhy5Ei918eOHVvn2DAMjLMsyFBWVsbcuXOZOHEiFkvzO6My\nMzPJzMx0HhcXFzf7GmcTFxfn9mtKfcqzdyjP3uHLPK/ZVkK1zYEDqLY7WLPtAImhp9xybX/b50rf\nz97hiTwnJSW51M4nBc7UqVMbfS86OpqysjJiYmIoKysjKqrhvx6qqqqYMWMGN998Mz179gQgMjKS\nqqoq7HY7ISEhlJaWap8sEREXpSaEYw0xsDlMrBaD1IRwt1xX+1yJL/jdHJz09HRyc3MByM3NpV+/\nfvXa2Gw2XnjhBa644grnfBuo6fHp3bs369atA2D16tWkp6d7J3ARkQCXEh9G1rCujLsknqxhXUmJ\nD2uwnVlYgOPjJS4vBKj1dcQX/G4OzsiRI5k9ezarVq1yPiYOUFhYyIoVK5gwYQJr167lv//9LxUV\nFaxevRqAiRMn0q1bN8aNG8dLL73Eu+++ywUXXMDQoUN9eDciIoElJT6s0cIGWtYbo32uvKug6Dj5\nh6pITQg/69cy2BmmaZq+DsJf7N+/363X0xivdyjP3qE8e4e/59nx8RLM7MU1vTEWC8YN47BcO6bJ\n8zQHxzsKio4zdeX32Owm1hDjrD1x3nDOzcEREZHA1NLeGCM5xS8Km2CXf6gKm93EAdgcJvmHqs7Z\nXhwVOCIi4jKtduzfPDVRPBCpwBERkWZxtTfG34alzgW9ju7md+GFbGmfTNpFvh2e8jUVOCIi4nZ6\nNNz7anPey2aj1/9yTvy5m3O/e0xcREQCnx4N9z7lvC4VOCIi4nZGrzSwWsFi0aPhXqKc16UhKhER\ncTtNRvY+5bwuFTgiIuIRejTc+5TzH2iISkRERIKOChwREREJOipwREREJOiowBEREZGgowJHRERE\ngo4KHBEREQk6KnBEREQk6KjAERERv2AWFuD4eAlmYYGvQ/FrBUXHWZpfQkHRcV+H4te00J+IiPic\nNud0TUHRcaau/B6b3cQaYpA17NzeMfxs1IMjIiI+p40iXZN/qAqb3cQB2Bwm+YeqfB2S31KBIyIi\nPqeNIl0T2TYEwwADsFoMUhPCfR2S3/K7IarKykpmz55NUVER8fHxTJ48mYiIiDptvvvuO1577TWO\nHz+OxWJh1KhRXH755QDMnz+frVu3Eh5e80WfOHEi3bp18/ZtiIhIM2ijyKYVFB3nT/85hMMEiwH/\nd1mChqfOwu8KnOzsbNLS0hg5ciTZ2dlkZ2czfvz4Om3atGnDfffdR6dOnSgtLeWxxx7jxz/+Me3a\ntQPg1ltvZeDAgb4IX0REWkgbRZ5d7fCUCZhAxUm7r0Pya343RJWXl0dGRgYAGRkZ5OXl1WuTlJRE\np06dAIiNjSU6OpqjR496NU4RERFvSk0IxxpiYDE0POUKv+vBKS8vJyYmBoD27dtTXl5+1vY7duzA\nZrORkJDgfO2dd95h6dKlpKamMm7cOEJDQxs8Nycnh5ycHABmzJhBXFycm+6ihtVqdfs1pT7l2TuU\nZ+9Qnr0jEPM8KA7mto9mw95yLu0cTWqnKF+H1CRf5tknBU5WVhZHjhyp9/rYsWPrHBuGgWEYjV6n\nrKyMuXPnMnHiRCyWms6oW265hfbt22Oz2ViwYAEffPABo0ePbvD8zMxMMjMzncfFxcUtuZ1GxcXF\nuf2aUp/y7B3Ks3coz94RqHlODIVrLwgDTgVE/J7Ic1JSkkvtfFLgTJ06tdH3oqOjKSsrIyYmhrKy\nMqKiGq5Qq6qqmDFjBjfffDM9e/Z0vl7b+xMaGsqQIUP48MMP3Ru8iIiI+D2/m4OTnp5Obm4uALm5\nufTr169eG5vNxgsvvMAVV1xRbzJxWVkZAKZpkpeXR5cuXTwftIiIiJtopWL38Ls5OCNHjmT27Nms\nWrXK+Zg4QGFhIStWrGDChAmsXbuW//73v1RUVLB69Wrgh8fB58yZ45xwfP7553PPPff46lZERESa\nRSsVu49hmqbp6yD8xf79+916vUAd4w00yrN3KM/eoTx7h7/meWl+CYs3FeGgZq2bcZfEMzq1g6/D\najFfzsHxuyEqERGRc5UeBXcfvxuiEhEROVelxIeRNawr+YeqSE0I1/BUK6jAERER8YGCouMNFjIp\n8WEqbNxABY6IiIiXaTKx52kOjoiIiAec7XHv2n2lHIDNYZJ/qMr7AQY59eCIiIi4WVM9NLWTiW0O\nU5OJPUQFjoiIBB2zsABz22aMXmk+2aG8oR6aM+fZaDKxZ6nAERGRoGIWFuB48Umw2TCtViwPP+uR\nIudsRZQrPTSaTOxZKnBERCSomNs2g80GpgPstpoixM0FTlNFlHpofE8FjoiIBBWjVxqm1Qp2G4RY\nMXqluf0zXCmi1EPjWypwREQkqBjJKVgeftajc3C8UURJ66jAERGRoGMkp3h0cnFzi6jGFvVr7HVp\nPRU4IiIiLeBqEdXYI+Na7M+ztNCfiIiIBzW2qJ8W+/MsFTgiIiIe1NgO4do53LM0RCUiIuJBjT0y\nrkfJPUsFjoiIiIc19si4HiX3HA1RiYiInMYsLMDx8RLMwgK/vqacnXpwREQkaLT2sWtPbPPgra0j\npC6/LHAqKyuZPXs2RUVFxMfHM3nyZCIiIuq0KSoq4oUXXsDhcGC327nmmmu46qqrANi5cyfz58/n\n1KlT9O3blzvvvBPDMHxxKyIi4iXueOzaE9s8eGPrCKnPL4eosrOzSUtLY86cOaSlpZGdnV2vTUxM\nDM8++ywzZ87kueee44MPPqC0tBSA1157jV/96lfMmTOHgwcPsnHjRm/fgoiIeJlbHruOiAKLAYbh\nthWKjV5pYLWCxaJVj73ILwucvLw8MjIyAMjIyCAvL69eG6vVSmhoKADV1dU4HA4AysrKOH78OD17\n9sQwDK644ooGzxcRkeDS2seuzcICzHdfA7sDDAvG2F+6paeldtVj44ZxGp7yIr8coiovLycmJgaA\n9u3bU15e3mC74uJiZsyYwcGDBxk/fjyxsbEUFhbSoUMHZ5sOHTo4e3bOlJOTQ05ODgAzZswgLi7O\nrfdhtVrdfk2pT3n2DuXZO5TnlhsUB3PbR7NhbzmXdo4mtVNUo20byvOx3J1U2m2ACZi0M+20c9fX\nIm4QDBjknmsFEF9+P/uswMnKyuLIkSP1Xh87dmydY8MwGp0/ExcXxwsvvEBpaSkzZ85k4MCBzYoh\nMzOTzMxM53FxcXGzzm9KXFyc268p9SnP3qE8e4fy3DqJoXDtBWHAqbPmsaE8m527Q4gVqNlAs6pz\nd47ra9Eqnvh+TkpKcqmdzwqcqVOnNvpedHQ0ZWVlxMTEUFZWRlRU41U4QGxsLF26dKGgoIBevXpR\nUlLifK+kpITY2Fi3xS0iIsHJG7uQi/f45Ryc9PR0cnNzAcjNzaVfv3712pSUlHDq1Cmg5qmrbdu2\nkZSURExMDGFhYXz77beYpskXX3xBenq6V+MXEZHAZCSnYLl2DIDWrQlwfjkHZ+TIkcyePZtVq1Y5\nHxMHKCwsZMWKFUyYMIF9+/axaNEiDMPANE2uv/56unbtCsD//d//8fLLL3Pq1Cn69OlD3759fXk7\nIiISQLRuTXAwTNM0fR2Ev9i/f79br6exdO9Qnr1DefYO5dk7zpZnx8dLMLMX16xbY7HUPP30v14d\naR5fzsHxyyEqERERX9G6NcHBL4eoREREfEWTjYODChwREZEzGMkpKmwCnIaoREREJOiowBEREZGg\nowJHREREgo4KHBEREQk6KnBEREQk6KjAERERkaCjlYxFREQk6KgHx4Mee+wxX4dwTlCevUN59g7l\n2TuUZ+/wZZ5V4IiIiEjQUYEjIiIiQSdk2rRp03wdRDDr3r27r0M4JyjP3qE8e4fy7B3Ks3f4Ks+a\nZCwiIiJBR0NUIiIiEnRU4IiIiEjQsfo6gGCwceNG3njjDRwOB8OGDWPkyJF13q+urmbevHns3LmT\nyMhIJk2aRMeOHX0UbeBqKs/Lli1j5cqVhISEEBUVxa9//Wvi4+N9FG3gairPtdatW8esWbN4/vnn\nSU5O9nKUgc+VPK9du5YlS5ZgGAbnn38+Dz74oA8iDWxN5bm4uJj58+dz7NgxHA4Ht9xyC5deeqmP\nog1ML7/8Mhs2bCA6OpoXX3yx3vumafLGG2/w9ddf07ZtW+69917vzMsxpVXsdrt53333mQcPHjSr\nq6vNRx55xNyzZ0+dNp9++qm5YMEC0zRNc82aNeasWbN8EWpAcyXPmzdvNk+cOGGapmkuX75ceW4B\nV/JsmqZZVVVlPvXUU+bjjz9u7tixwweRBjZX8rx//37zN7/5jVlRUWGapmkeOXLEF6EGNFfy/Oqr\nr5rLly83TdM09+zZY957772+CDWgbdmyxSwsLDQfeuihBt//z3/+Y06fPt10OBzmtm3bzClTpngl\nLg1RtdKOHTtITEwkISEBq9XK5ZdfTl5eXp0269evZ/DgwQAMHDiQ/Px8TM3tbhZX8pyamkrbtm0B\n6NGjB6Wlpb4INaC5kmeA9957jxtuuIHQ0FAfRBn4XMnzypUrufrqq4mIiAAgOjraF6EGNFfybBgG\nVVVVAFRVVRETE+OLUAPaxRdf7Pw+bcj69eu54oorMAyDnj17cuzYMcrKyjwelwqcViotLaVDhw7O\n4w4dOtT7xXp6m5CQEMLDw6moqPBqnIHOlTyfbtWqVfTp08cboQUVV/K8c+dOiouL1Y3fCq7kef/+\n/Rw4cICpU6fyxBNPsHHjRm+HGfBcyfOYMWP45z//yYQJE3j++ee56667vB1m0CstLSUuLs553NTP\nb3dRgSNB54svvmDnzp2MGDHC16EEHYfDwaJFi7jtttt8HUrQczgcHDhwgKeffpoHH3yQBQsWcOzY\nMV+HFXS+/PJLBg8ezKuvvsqUKVOYO3cuDofD12GJG6jAaaXY2FhKSkqcxyUlJcTGxjbaxm63U1VV\nRWRkpFfjDHSu5Bngm2++4f333+fRRx/V8EkLNJXnEydOsGfPHn73u98xceJEtm/fzh/+8AcKCwt9\nEW7AcvXnRnp6OlarlY4dO9KpUycOHDjg7VADmit5XrVqFT/5yU8A6NmzJ9XV1ephd7PY2FiKi4ud\nx439/HY3FTitlJyczIEDBzh8+DA2m421a9eSnp5ep81ll13G6tWrgZonT3r37o1hGD6INnC5kudd\nu3bx2muv8eijj2q+Qgs1lefw8HAWLlzI/PnzmT9/Pj169ODRRx/VU1TN5Mr3c//+/dmyZQsAR48e\n5cCBAyQkJPgi3IDlSp7j4uLIz88HYO/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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(8, 4))\n",
- "ax = fig.add_subplot(111)\n",
- "ax.plot(inputs_train[:, 0], targets_train[:, 0], '.', label='training data')\n",
- "ax.plot(inputs_valid[:, 0], targets_valid[:, 0], '.', label='validation data')\n",
- "ax.set_xlabel('Inputs $x$', fontsize=14)\n",
- "ax.set_ylabel('Ouputs $y$', fontsize=14)\n",
- "ax.legend(loc='best')\n",
- "fig.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Model\n",
- "\n",
- "We will fit models with a varying number of parameters to the training data. As multi-layer logistic sigmoid models do not tend to perform well in regressions tasks like this we will instead use a [radial basis function (RBF) network](https://en.wikipedia.org/wiki/Radial_basis_function_network).\n",
- "\n",
- "This model predicts the output as the weighted sum of basis functions (here Gaussian like bumps) tiled across the input space. The cell below generates a random set of weights and bias for a RBF network and plots the modelled input-output function across inputs $[0, 1]$. Run the cell below for several different number of weight parameters (specified with `num_weights` variable) to get a feel for the sort of predictions the RBF network models produce."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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MTqxgxdqElYjgXXo9iIc/4WXXcaKeLpkHhw4g7bu5jpJvkpaBtL4Q/fBddMOP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\nAwAAAA6hXAMAAAAOoVwDAAAADvk/MW3Utrx4VB8AAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "num_weights = 15\n",
- "weights_scale = 1.\n",
- "bias_scale = 1.\n",
- "\n",
- "def basis_function(x, centre, scale):\n",
- " return np.exp(-(x - centre)**2 / scale**2)\n",
- "\n",
- "weights = rng.normal(size=num_weights) * weights_scale\n",
- "bias = rng.normal() * bias_scale\n",
- "\n",
- "centres = np.linspace(0, 1, weights.shape[0])\n",
- "scale = 1. / weights.shape[0]\n",
- "\n",
- "xs = np.linspace(0, 1, 200)\n",
- "ys = np.zeros(xs.shape[0])\n",
- "\n",
- "fig = plt.figure(figsize=(12, 4))\n",
- "ax = fig.add_subplot(1, 1, 1)\n",
- "for weight, centre in zip(weights, centres):\n",
- " ys += weight * basis_function(xs, centre, scale)\n",
- "ax.plot(xs, ys)\n",
- "ax.set_xlabel('Input', fontsize=14)\n",
- "ax.set_ylabel('Output', fontsize=14)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You do not need to read in to the details of how to implement this model. All of the additional code you need to fit RBF networks is provided in the `RadialBasisFunctionLayer` in the `mlp.layers` module. The `RadialBasisFunctionLayer` class has the same interface as the layer classes we encountered in the previous lab, defining both `fprop` and `bprop` methods, and we can therefore include it as a layer in a `MultipleLayerModel` as with any other layer. \n",
- "\n",
- "Here we will use the `RadialBasisFunctionLayer` as the first layer in a two layer model. This first layer calculates the basis function terms which are then be weighted and summed together in an `AffineLayer`, the second and final layer. This illustrates the advantage of using a modular modelling framework - we can reuse the code we previously implemented to train a quite different model architecture just by defining a new layer class. \n",
- "\n",
- "Run the cell below to run some necessary setup code."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "from mlp.models import MultipleLayerModel\n",
- "from mlp.layers import AffineLayer, RadialBasisFunctionLayer\n",
- "from mlp.errors import SumOfSquaredDiffsError\n",
- "from mlp.initialisers import ConstantInit, UniformInit\n",
- "from mlp.learning_rules import GradientDescentLearningRule\n",
- "from mlp.optimisers import Optimiser\n",
- "\n",
- "# Regression problem therefore use sum of squared differences error\n",
- "error = SumOfSquaredDiffsError()\n",
- "# Use basic gradient descent learning rule with fixed learning rate\n",
- "learning_rule = GradientDescentLearningRule(0.1)\n",
- "# Initialise weights from uniform distribution and zero bias\n",
- "weights_init = UniformInit(-0.1, 0.1)\n",
- "biases_init = ConstantInit(0.)\n",
- "# Train all models for 2000 epochs\n",
- "num_epoch = 2000"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The cell below defines RBF network models with varying number of weight parameters (equal to the number of basis functions) and fits each to the training set, recording the final training and validation set errors for the fitted models. Run it now to fit the models and calculate the error values."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--------------------------------------------------------------------------------\n",
- "Training model with 2 weights\n",
- "--------------------------------------------------------------------------------\n",
- " Final training set error: 2.0e-03\n",
- " Final validation set error: 1.1e-03\n",
- "--------------------------------------------------------------------------------\n",
- "Training model with 5 weights\n",
- "--------------------------------------------------------------------------------\n",
- " Final training set error: 4.5e-04\n",
- " Final validation set error: 3.0e-04\n",
- "--------------------------------------------------------------------------------\n",
- "Training model with 10 weights\n",
- "--------------------------------------------------------------------------------\n",
- " Final training set error: 5.1e-05\n",
- " Final validation set error: 8.3e-05\n",
- "--------------------------------------------------------------------------------\n",
- "Training model with 25 weights\n",
- "--------------------------------------------------------------------------------\n",
- " Final training set error: 3.9e-05\n",
- " Final validation set error: 9.5e-05\n",
- "--------------------------------------------------------------------------------\n",
- "Training model with 50 weights\n",
- "--------------------------------------------------------------------------------\n",
- " Final training set error: 1.5e-05\n",
- " Final validation set error: 1.6e-03\n",
- "--------------------------------------------------------------------------------\n",
- "Training model with 100 weights\n",
- "--------------------------------------------------------------------------------\n",
- " Final training set error: 1.0e-05\n",
- " Final validation set error: 4.2e-03\n"
- ]
- }
- ],
- "source": [
- "num_weight_list = [2, 5, 10, 25, 50, 100]\n",
- "models = []\n",
- "train_errors = []\n",
- "valid_errors = []\n",
- "for num_weight in num_weight_list:\n",
- " model = MultipleLayerModel([\n",
- " RadialBasisFunctionLayer(num_weight),\n",
- " AffineLayer(input_dim * num_weight, output_dim, \n",
- " weights_init, biases_init)\n",
- " ])\n",
- " optimiser = Optimiser(model, error, learning_rule, \n",
- " train_data, valid_data)\n",
- " print('-' * 80)\n",
- " print('Training model with {0} weights'.format(num_weight))\n",
- " print('-' * 80)\n",
- " _ = optimiser.train(num_epoch, -1)\n",
- " outputs_train = model.fprop(inputs_train)[-1]\n",
- " outputs_valid = model.fprop(inputs_valid)[-1]\n",
- " models.append(model)\n",
- " train_errors.append(error(outputs_train, targets_train))\n",
- " valid_errors.append(error(outputs_valid, targets_valid))\n",
- " print(' Final training set error: {0:.1e}'.format(train_errors[-1]))\n",
- " print(' Final validation set error: {0:.1e}'.format(valid_errors[-1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the cell below write code to [plot bar charts](http://matplotlib.org/examples/api/barchart_demo.html) of the training and validation set errors for the different fitted models.\n",
- "\n",
- "Some questions to think about from the plots:\n",
- "\n",
- " * Do the models with more free parameters fit the training data better or worse?\n",
- " * What does the validation set error value tell us about the models?\n",
- " * Of the models fitted here which would you say seems like it is most likely to generalise well to unseen data? \n",
- " * Do any of the models seem to be overfitting?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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JZFJhYaFmzpwpSTpw4ICqq6v16aefasKECZo/f77i4uJkGIZWr16tLVu26Ctf+YrmzZun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\nxVKrmReXxqluzODgYEmSr6+vBgwYoK1bt1YbF1P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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12, 6))\n",
- "ax1 = fig.add_subplot(1, 2, 1)\n",
- "ax1.bar(0.1 + np.arange(len(num_weight_list)), train_errors)\n",
- "ax1.set_xticks(0.5 + np.arange(len(num_weight_list)))\n",
- "ax1.set_xticklabels(num_weight_list)\n",
- "ax1.set_xlabel('Number of weight parameters')\n",
- "ax1.set_ylabel('Error')\n",
- "ax1.set_title('Training set')\n",
- "ax2 = fig.add_subplot(1, 2, 2)\n",
- "ax2.bar(0.1 + np.arange(len(num_weight_list)), valid_errors)\n",
- "ax2.set_xticks(0.5 + np.arange(len(num_weight_list)))\n",
- "ax2.set_xticklabels(num_weight_list)\n",
- "ax2.set_xlabel('Number of weight parameters')\n",
- "ax2.set_ylabel('Error')\n",
- "ax2.set_title('Validation set')\n",
- "fig.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's visualise what the fitted model's predictions look like across the whole input space compared to the 'true' function we were trying to fit. \n",
- "\n",
- "In the cell below, for each of the fitted models stored in the `models` list above:\n",
- " * Compute output predictions for the model across a linearly spaced series of 500 input points between 0 and 1 in the input space.\n",
- " * Plot the computed predicted outputs and true function values at the corresponding inputs as line plots on the same axis (use a new axis for each model).\n",
- " * On the same axis plot the training data sets input-target pairs as points."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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Iim0dJnG2eKn+LnGoLHNzYJeDAzgIMCqER2oJi9Rgi9Cg0w3OJY71GhWPZsTy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\nC0OGDCEgIKDGx05ISCjXVOpm8+bNY+/evQwfPpw5c+bUyXs5ffo0ycn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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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AZwVWn1zbL4ceGhoaCA8P774dHh5OUVFRn2XUajVGo5HW1lYaGhpITU3tLhcW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\n6lhfp3JK+SIhl1/PFxPoZmBeTAeGdvRogujF1SRREEII0eT0OoVx4d7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z515oZew4A2XH3Gx6p5XD+1y43SM3YVDSMkCngzPUagghOLzPxZH9HRyng9dd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\nhDB69GiOHj1Kjx49ePrpp/nnP/9Jjx49KCoqws3Njb/97W8cP36cefP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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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P5Ubk0IPVau324VBXV4fVau21jMfjwW63Y7FYjntufX39cc8dbDq1iofPT0aj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\ncOECTz75JGPGjEGv13P48GFiYmKqtHKOiYnh0KFDTJkyBWdnZ7755ht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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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a8SGEoGp/gLqaIGlZhlOrTZBXiGdnOyHRypMX/IRwu8LXSl1cMz25z9LI0uiz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- "output_type": "display_data"
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6XqJHwdSfNJhL+CHgVzBbks8hFbb+iT8YHH4foaBACFKGHkbCapNTGkcaJ09y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\nEMKm/wNpaqMOyNfePAAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "inputs_grid = np.linspace(0., 1., 500)\n",
- "true_func_grid = polynomial_function(inputs_grid, coefficients)\n",
- "for num_weight, model in zip(num_weight_list, models):\n",
- " fig = plt.figure(figsize=(8, 4))\n",
- " ax = fig.add_subplot(111)\n",
- " outputs_grid = model.fprop(inputs_grid)[-1]\n",
- " ax.plot(inputs_train, targets_train, '.', label='training data')\n",
- " ax.plot(inputs_grid, true_func_grid, label='true function')\n",
- " ax.plot(inputs_grid, outputs_grid, label='fitted function')\n",
- " ax.set_xlabel('Inputs $x$', fontsize=14)\n",
- " ax.set_ylabel('Ouputs $y$', fontsize=14)\n",
- " ax.set_title('{0} weight parameters'.format(num_weight))\n",
- " ax.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You should be able to relate your answers to the questions above to what you see in these plots - ask a demonstrator if you are unsure what is going on. In particular for the models which appeared to be overfitting and generalising poorly you should now have an idea how this looks in terms of the model's predictions and how these relate to the training data points and true function values."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/05_Non-linearities_and_regularisation.ipynb b/notebooks/05_Non-linearities_and_regularisation.ipynb
deleted file mode 100644
index 83e4b82..0000000
--- a/notebooks/05_Non-linearities_and_regularisation.ipynb
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Regularisation\n",
- "\n",
- "In this lab we will explore different methods for regularising networks to reduce overfitting and improve generalisation. This uses the material covered in the [fifth lecture slides](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp05-hid.pdf)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Exercise 1: L1 and L2 penalties\n",
- "\n",
- "In the previous lab notebook we explored the issue of overfitting. There we saw that this arises when the model is 'too complex' ($\\sim$ has too many degrees of freedom / parameters) for the amount of data we have available.\n",
- "\n",
- "One method for trying to reduce overfitting is therefore to try to decrease the flexibility of the model. We can do this by simply reducing the number of free parameters in the model (e.g. by using a shallower model with fewer layers or layers with smaller dimensionality). More generally however we might want some way of more continuously varying the effective flexibility of a model with a fixed architecture.\n",
- "\n",
- "A common method for doing this is to add an additional term to the objective function being minimised during training which penalises some measure of the complexity of a model as a function of the model parameters. The aim of training is then to minimise with respect to the model parameters the sum $E^\\star$ of the data-driven error function term $\\bar{E}$ and a model complexity term $C$.\n",
- "\n",
- "\\begin{equation}\n",
- " E^\\star =\n",
- " \\underbrace{\\bar{E}}_{\\textrm{data term}} + \\underbrace{C}_{\\textrm{complexity term}}\n",
- "\\end{equation}\n",
- "\n",
- "We need the complexity term $C$ to be easy to compute and differentiable with respect to the model parameters. A common choice is to use terms involving the *norms* ($\\sim$ a measure of size) of the parameters. This penalises models with large parameter values. Two commonly used norms are the L1 and L2 norms. \n",
- "\n",
- "For a $D$ dimensional vector $\\mathbf{v}$ the L1 norm is defined as\n",
- "\n",
- "\\begin{equation}\n",
- "\\| \\boldsymbol{v} \\|_1 = \\sum_{d=1}^D \\left| v_d \\right|,\n",
- "\\end{equation}\n",
- "\n",
- "and the L2 norm is defined as\n",
- "\n",
- "\\begin{equation}\n",
- "\\| \\boldsymbol{v} \\|_2 = \\left[ \\sum_{d=1}^D \\left( v_d^2 \\right) \\right]^{\\frac{1}{2}}.\n",
- "\\end{equation}\n",
- "\n",
- "For a $K \\times D$ matrix $\\mathbf{M}$, we will define norms by collapsing the matrix to a vector $\\boldsymbol{m} = \\mathrm{vec}\\left[\\mathbf{M}\\right] = \\left[ M_{1,1} \\dots M_{1,D} ~ M_{2,1} \\dots M_{K,D} \\right]^{\\rm T}$ and then taking the norm as defined above of this resulting vector (practically this just results in summing over two sets of indices rather than one).\n",
- "\n",
- "The overall complexity penalty term $C$ is defined as a sum over individual complexity terms for each of the $P$ parameters of the model \n",
- "\n",
- "\\begin{equation}\n",
- " C = \\sum_{i=1}^P \\left[ C^{(i)} \\right]\n",
- "\\end{equation}\n",
- "\n",
- "Some of these per-parameter penalty terms $C^{(i)}$ may be set to zero if we do not wish to penalise the size of the corresponding parameter.\n",
- "\n",
- "To enable us to tradeoff between the model complexity and data error terms, it is typical to introduce positive scalar coefficients $\\beta_i$ to scale the penalty term on the $i$th parameter. A *L1 penalty* on the $i$th vector parameter $\\boldsymbol{p}^{(i)}$ (or matrix parameter collapsed to a vector) is then commonly defined as\n",
- "\n",
- "\\begin{equation}\n",
- " C^{(i)}_{\\textrm{L1}} = \n",
- " \\beta_i \\left\\| \\boldsymbol{p}^{(i)} \\right\\|_1 = \n",
- " \\beta_i \\sum_{d=1}^D \\left| p^{(i)}_d \\right|.\n",
- "\\end{equation}\n",
- "\n",
- "This has a gradient with respect to the parameter vector\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial C^{(i)}_{\\textrm{L1}}}{\\partial p^{(i)}_d} = \\beta_i \\, \\textrm{sgn}\\left( p^{(i)}_d \\right)\n",
- "\\end{equation}\n",
- "\n",
- "where $\\textrm{sgn}(u) = +1$ if $u > 0$, $\\textrm{sgn}(u) = -1$ if $u < 0$ (and is not well defined for $u=0$ though a common convention is to have $\\textrm{sgn}(0) = 0$).\n",
- "\n",
- "Similarly a *L2 penalty* on the $i$th vector parameter $\\boldsymbol{p}^{(i)}$ (or matrix parameter collapsed to a vector) is commonly defined as\n",
- "\n",
- "\\begin{equation}\n",
- " C^{(i)}_{\\textrm{L2}} = \n",
- " \\frac{1}{2} \\beta_i \\left\\| \\boldsymbol{p}^{(i)} \\right\\|_2^2 =\n",
- " \\frac{1}{2} \\beta_i \\sum_{d=1}^D \\left[ \\left( p^{(i)}_d \\right)^2 \\right].\n",
- "\\end{equation}\n",
- "\n",
- "Somewhat confusingly this is proportional to the square of the L2 norm rather than the L2 norm itself, however it is an almost universal convention to call this an L2 penalty so we will stick with this nomenclature here. The $\\frac{1}{2}$ term is less universal and is sometimes not included; we include it here for consistency with how we defined the sum of squared errors cost. Similarly to that case, the $\\frac{1}{2}$ cancels when calculating the gradient with respect to the parameter\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial C^{(i)}_{\\textrm{L2}}}{\\partial p^{(i)}_d} = \\beta_i p^{(i)}_d\n",
- "\\end{equation}\n",
- "\n",
- "Use the above definitions for the L1 and L2 penalties for a parameter and corresponding gradients to implement the `__call__` and `grad` methods respectively for the skeleton `L1Penalty` and `L2Penalty` class definitions below. The `coefficient` propert of these classes should be used as the $\\beta_i$ value in the equations above. The parameter the penalty term (or gradient) is being evaluated for will be either a one or two-dimensional NumPy array (corresponding to a vector or matrix parameter respectively) and your implementations should be able to cope with both cases."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "seed = 22102017 \n",
- "rng = np.random.RandomState(seed)\n",
- "class L1Penalty(object):\n",
- " \"\"\"L1 parameter penalty.\n",
- " \n",
- " Term to add to the objective function penalising parameters\n",
- " based on their L1 norm.\n",
- " \"\"\"\n",
- " \n",
- " def __init__(self, coefficient):\n",
- " \"\"\"Create a new L1 penalty object.\n",
- " \n",
- " Args:\n",
- " coefficient: Positive constant to scale penalty term by.\n",
- " \"\"\"\n",
- " assert coefficient > 0., 'Penalty coefficient must be positive.'\n",
- " self.coefficient = coefficient\n",
- " \n",
- " def __call__(self, parameter):\n",
- " \"\"\"Calculate L1 penalty value for a parameter.\n",
- " \n",
- " Args:\n",
- " parameter: Array corresponding to a model parameter.\n",
- " \n",
- " Returns:\n",
- " Value of penalty term.\n",
- " \"\"\"\n",
- " return self.coefficient * abs(parameter).sum()\n",
- " \n",
- " def grad(self, parameter):\n",
- " \"\"\"Calculate the penalty gradient with respect to the parameter.\n",
- " \n",
- " Args:\n",
- " parameter: Array corresponding to a model parameter.\n",
- " \n",
- " Returns:\n",
- " Value of penalty gradient with respect to parameter. This\n",
- " should be an array of the same shape as the parameter.\n",
- " \"\"\"\n",
- " return self.coefficient * np.sign(parameter)\n",
- " \n",
- " def __repr__(self):\n",
- " return 'L1Penalty({0})'.format(self.coefficient)\n",
- " \n",
- "\n",
- "class L2Penalty(object):\n",
- " \"\"\"L1 parameter penalty.\n",
- " \n",
- " Term to add to the objective function penalising parameters\n",
- " based on their L2 norm.\n",
- " \"\"\"\n",
- "\n",
- " def __init__(self, coefficient):\n",
- " \"\"\"Create a new L2 penalty object.\n",
- " \n",
- " Args:\n",
- " coefficient: Positive constant to scale penalty term by.\n",
- " \"\"\"\n",
- " assert coefficient > 0., 'Penalty coefficient must be positive.'\n",
- " self.coefficient = coefficient\n",
- " \n",
- " def __call__(self, parameter):\n",
- " \"\"\"Calculate L2 penalty value for a parameter.\n",
- " \n",
- " Args:\n",
- " parameter: Array corresponding to a model parameter.\n",
- " \n",
- " Returns:\n",
- " Value of penalty term.\n",
- " \"\"\"\n",
- " return 0.5 * self.coefficient * (parameter**2).sum()\n",
- " \n",
- " def grad(self, parameter):\n",
- " \"\"\"Calculate the penalty gradient with respect to the parameter.\n",
- " \n",
- " Args:\n",
- " parameter: Array corresponding to a model parameter.\n",
- " \n",
- " Returns:\n",
- " Value of penalty gradient with respect to parameter. This\n",
- " should be an array of the same shape as the parameter.\n",
- " \"\"\"\n",
- " return self.coefficient * parameter\n",
- " \n",
- " def __repr__(self):\n",
- " return 'L2Penalty({0})'.format(self.coefficient)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Test your implementations by running the cells below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "All test values calculated correctly for L1Penalty\n"
- ]
- }
- ],
- "source": [
- "test_params_1 = np.array([[0.5, 0.3, -1.2, 5.8], [0.2, -3.1, 4.9, -5.0]])\n",
- "test_params_2 = np.array([0.8, -0.6, -0.3, 1.5, 2.8])\n",
- "true_l1_cost_1 = 10.5\n",
- "true_l1_grad_1 = np.array([[0.5, 0.5, -0.5, 0.5], [0.5, -0.5, 0.5, -0.5]])\n",
- "true_l1_cost_2 = 3.\n",
- "true_l1_grad_2 = np.array([0.5, -0.5, -0.5, 0.5, 0.5])\n",
- "l1 = L1Penalty(0.5)\n",
- "if (not np.allclose(l1(test_params_1), true_l1_cost_1) or\n",
- " not np.allclose(l1(test_params_2), true_l1_cost_2)):\n",
- " print('L1Penalty.__call__ giving incorrect value(s).')\n",
- "elif (not np.allclose(l1.grad(test_params_1), true_l1_grad_1) or \n",
- " not np.allclose(l1.grad(test_params_2), true_l1_grad_2)):\n",
- " print('L1Penalty.grad giving incorrect value(s).')\n",
- "else:\n",
- " print('All test values calculated correctly for L1Penalty')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "All test values calculated correctly for L2Penalty\n"
- ]
- }
- ],
- "source": [
- "test_params_1 = np.array([[0.5, 0.3, -1.2, 5.8], [0.2, -3.1, 4.9, -5.0]])\n",
- "test_params_2 = np.array([0.8, -0.6, -0.3, 1.5, 2.8])\n",
- "true_l2_cost_1 = 23.52\n",
- "true_l2_grad_1 = np.array([[0.25, 0.15, -0.6, 2.9], [0.1, -1.55, 2.45, -2.5]])\n",
- "true_l2_cost_2 = 2.795\n",
- "true_l2_grad_2 = np.array([0.4, -0.3, -0.15, 0.75, 1.4])\n",
- "l2 = L2Penalty(0.5)\n",
- "if (not np.allclose(l2(test_params_1), true_l2_cost_1) or\n",
- " not np.allclose(l2(test_params_2), true_l2_cost_2)):\n",
- " print('L2Penalty.__call__ giving incorrect value(s).')\n",
- "elif (not np.allclose(l2.grad(test_params_1), true_l2_grad_1) or \n",
- " not np.allclose(l2.grad(test_params_2), true_l2_grad_2)):\n",
- " print('L2Penalty.grad giving incorrect value(s).')\n",
- "else:\n",
- " print('All test values calculated correctly for L2Penalty')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 2: Training with regularisation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Previously in the second laboratory you implemented a function `grads_wrt_params` to calculate the gradient of an error function with respect to the parameters of an affine model (layer), given gradients of the error function with respect to the model (layer) outputs.\n",
- "\n",
- "If we are training a model using a regularised objective function, we need to additionally calculate the gradients of the regularisation penalty terms with respect to the parameters and add these to the error function gradient terms. Following from the definition of the regularised objective $E^\\star$ above we have that the gradient of the overall objective with respect to the $d$th element of the $i$th model parameter is\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial E^\\star}{\\partial p^{(i)}_d} =\n",
- " \\frac{\\partial \\bar{E}}{\\partial p^{(i)}_d} + \n",
- " \\frac{\\partial C}{\\partial p^{(i)}_d}\n",
- "\\end{equation}\n",
- "\n",
- "We have already discussed in the second lab notebook how to calculate the error function gradient term $\\frac{\\partial \\bar{E}}{\\partial p^{(i)}_d}$. As the model complexity term is composed of a sum of per parameter terms and only one of these will depend on the $i$th parameter we can write\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial C}{\\partial p^{(i)}_d} = \\frac{\\partial C^{(i)}}{\\partial p^{(i)}_d}\n",
- "\\end{equation}\n",
- "\n",
- "which corresponds to the penalty term gradients you implemented above. To enable us to use the same `Optimiser` implementation that we have previously used to train models without regularisation, we have altered the implementation of the `AffineLayer` class (this being the only layer we currently have defined with parameters) to allow us to specify penalty terms on the weight matrix and bias vector when creating an instance of the class and to add the corresponding penalty gradients to the returned value from the `grads_wrt_params` method. \n",
- "\n",
- "The penalty terms need to be specified as a class matching the interface of the `L1Penalty` and `L2Penalty` classes you implemented above, defining both a `__call__` method to calculate the penalty value for a parameter and a `grad` method to calculate the gradient of the penalty with respect to the parameter. Separate penalties can be specified for the weight and bias parameters, with it common to only regularise the weight parameters. \n",
- "\n",
- "The penalty terms for a layer are specifed using the `weights_penalty` and `biases_penalty` arguments to the `__init__` method of the `AffineLayer` class. If either (or both) ofthese are set to `None` (the default) no regularisation is applied to the corresponding parameter.\n",
- "\n",
- "Using the `L1Penalty` and `L2Penalty` classes you implemented in the previous exercise, train models to classify MNIST digit images with\n",
- "\n",
- " * no regularisation\n",
- " * an L1 penalty with coefficient $10^{-5}$ on the all of the weight matrix parameters\n",
- " * an L1 penalty with coefficient $10^{-3}$ on the all of the weight matrix parameters\n",
- " * an L2 penalty with coefficient $10^{-4}$ on the all of the weight matrix parameters\n",
- " * an L2 penalty with coefficient $10^{-2}$ on the all of the weight matrix parameters\n",
- " \n",
- "The models should all have three affine layers interspersed with rectified linear layers (as implemented in the first exercise) and intermediate layers between the input and output should have dimensionalities of 100. The final output layer should be an `AffineLayer` (the model outputting the logarithms of the non-normalised class probabilties) and you should use the `CrossEntropySoftmaxError` as the error function (which calculates the softmax of the model outputs to convert to normalised class probabilities before calculating the corresponding multi-class cross entropy error). \n",
- "\n",
- "Use the `GlorotInit` class introduced in the first coursework to initialise the weights in all layers, using a gain of 0.5 (this adjusts for the fact that the rectified linear sets zeros all negative inputs), and initialises the biases to zero with a `ConstantInit` object. \n",
- "\n",
- "As an example the necessary parameter initialisers, model and error can be defined using\n",
- "\n",
- "```python\n",
- "weights_init = GlorotUniformInit(0.5, rng)\n",
- "biases_init = ConstantInit(0.)\n",
- "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, weights_init, \n",
- " biases_init, weights_penalty=weights_penalty),\n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, weights_init, \n",
- " biases_init, weights_penalty=weights_penalty),\n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, weights_init, \n",
- " biases_init, weights_penalty=weights_penalty)\n",
- "])\n",
- "error = CrossEntropySoftmaxError()\n",
- "```\n",
- "\n",
- "This assumes all the relevant classes have been imported from their modules, a penalty object has been assigned to `weights_penalty` and a seeded random number generator assigned to `rng`.\n",
- "\n",
- "For each regularisation scheme, train the model for 100 epochs with a batch size of 50 and using a gradient descent with momentum learning rule with learning rate 0.01 and momentum coefficient 0.9. For each regularisation scheme you should store the run statistics (output of `Optimiser.train`) and the final values of the first layer weights for each of the trained models."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from collections import OrderedDict\n",
- "import logging\n",
- "from mlp.layers import AffineLayer, ReluLayer\n",
- "from mlp.errors import CrossEntropySoftmaxError\n",
- "from mlp.models import MultipleLayerModel\n",
- "from mlp.initialisers import GlorotUniformInit, ConstantInit\n",
- "from mlp.learning_rules import MomentumLearningRule\n",
- "from mlp.data_providers import MNISTDataProvider\n",
- "from mlp.optimisers import Optimiser\n",
- "\n",
- "# Seed a random number generator\n",
- "seed = 24102016 \n",
- "rng = np.random.RandomState(seed)\n",
- "\n",
- "num_epochs = 100\n",
- "stats_interval = 5\n",
- "batch_size = 50\n",
- "learning_rate = 0.01\n",
- "mom_coeff = 0.9\n",
- "weights_init_gain = 0.5\n",
- "biases_init = 0.\n",
- "\n",
- "# Set up a logger object to print info about the training run to stdout\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
- "logger.handlers = [logging.StreamHandler()]\n",
- "\n",
- "# Create data provider objects for the MNIST data set\n",
- "train_data = MNISTDataProvider('train', batch_size, rng=rng)\n",
- "valid_data = MNISTDataProvider('valid', batch_size, rng=rng)\n",
- "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
- "\n",
- "weights_init = GlorotUniformInit(weights_init_gain, rng)\n",
- "biases_init = ConstantInit(biases_init)\n",
- "error = CrossEntropySoftmaxError()\n",
- "learning_rule = MomentumLearningRule(learning_rate, mom_coeff)\n",
- "data_monitors = {'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: None\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 10.9s to complete\n",
- " error(train)=6.34e-02, acc(train)=9.82e-01, error(valid)=9.67e-02, acc(valid)=9.72e-01\n",
- "Epoch 10: 10.1s to complete\n",
- " error(train)=2.36e-02, acc(train)=9.94e-01, error(valid)=8.34e-02, acc(valid)=9.77e-01\n",
- "Epoch 15: 11.7s to complete\n",
- " error(train)=8.49e-03, acc(train)=9.98e-01, error(valid)=8.34e-02, acc(valid)=9.78e-01\n",
- "Epoch 20: 10.5s to complete\n",
- " error(train)=3.42e-03, acc(train)=1.00e+00, error(valid)=9.58e-02, acc(valid)=9.78e-01\n",
- "Epoch 25: 10.7s to complete\n",
- " error(train)=1.42e-03, acc(train)=1.00e+00, error(valid)=9.48e-02, acc(valid)=9.79e-01\n",
- "Epoch 30: 11.4s to complete\n",
- " error(train)=9.06e-04, acc(train)=1.00e+00, error(valid)=1.01e-01, acc(valid)=9.79e-01\n",
- "Epoch 35: 12.0s to complete\n",
- " error(train)=6.40e-04, acc(train)=1.00e+00, error(valid)=1.04e-01, acc(valid)=9.79e-01\n",
- "Epoch 40: 10.9s to complete\n",
- " error(train)=5.06e-04, acc(train)=1.00e+00, error(valid)=1.06e-01, acc(valid)=9.79e-01\n",
- "Epoch 45: 15.1s to complete\n",
- " error(train)=4.23e-04, acc(train)=1.00e+00, error(valid)=1.08e-01, acc(valid)=9.79e-01\n",
- "Epoch 50: 13.2s to complete\n",
- " error(train)=3.59e-04, acc(train)=1.00e+00, error(valid)=1.10e-01, acc(valid)=9.79e-01\n",
- "Epoch 55: 10.1s to complete\n",
- " error(train)=3.07e-04, acc(train)=1.00e+00, error(valid)=1.11e-01, acc(valid)=9.79e-01\n",
- "Epoch 60: 10.3s to complete\n",
- " error(train)=2.71e-04, acc(train)=1.00e+00, error(valid)=1.13e-01, acc(valid)=9.79e-01\n",
- "Epoch 65: 11.4s to complete\n",
- " error(train)=2.42e-04, acc(train)=1.00e+00, error(valid)=1.15e-01, acc(valid)=9.79e-01\n",
- "Epoch 70: 10.8s to complete\n",
- " error(train)=2.19e-04, acc(train)=1.00e+00, error(valid)=1.16e-01, acc(valid)=9.79e-01\n",
- "Epoch 75: 13.8s to complete\n",
- " error(train)=1.97e-04, acc(train)=1.00e+00, error(valid)=1.17e-01, acc(valid)=9.79e-01\n",
- "Epoch 80: 11.4s to complete\n",
- " error(train)=1.82e-04, acc(train)=1.00e+00, error(valid)=1.18e-01, acc(valid)=9.79e-01\n",
- "Epoch 85: 15.5s to complete\n",
- " error(train)=1.67e-04, acc(train)=1.00e+00, error(valid)=1.19e-01, acc(valid)=9.79e-01\n",
- "Epoch 90: 15.0s to complete\n",
- " error(train)=1.54e-04, acc(train)=1.00e+00, error(valid)=1.20e-01, acc(valid)=9.79e-01\n",
- "Epoch 95: 11.7s to complete\n",
- " error(train)=1.44e-04, acc(train)=1.00e+00, error(valid)=1.22e-01, acc(valid)=9.79e-01\n",
- "Epoch 100: 12.6s to complete\n",
- " error(train)=1.34e-04, acc(train)=1.00e+00, error(valid)=1.22e-01, acc(valid)=9.79e-01\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L1Penalty(1e-05)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 25.2s to complete\n",
- " error(train)=6.46e-02, acc(train)=9.82e-01, error(valid)=9.39e-02, acc(valid)=9.73e-01\n",
- "Epoch 10: 15.4s to complete\n",
- " error(train)=2.68e-02, acc(train)=9.93e-01, error(valid)=8.04e-02, acc(valid)=9.78e-01\n",
- "Epoch 15: 17.1s to complete\n",
- " error(train)=1.42e-02, acc(train)=9.97e-01, error(valid)=8.12e-02, acc(valid)=9.78e-01\n",
- "Epoch 20: 15.4s to complete\n",
- " error(train)=6.73e-03, acc(train)=9.99e-01, error(valid)=8.81e-02, acc(valid)=9.77e-01\n",
- "Epoch 25: 43.1s to complete\n",
- " error(train)=3.55e-03, acc(train)=1.00e+00, error(valid)=8.79e-02, acc(valid)=9.78e-01\n",
- "Epoch 30: 18.9s to complete\n",
- " error(train)=2.77e-03, acc(train)=1.00e+00, error(valid)=9.02e-02, acc(valid)=9.78e-01\n",
- "Epoch 35: 23.9s to complete\n",
- " error(train)=2.19e-03, acc(train)=1.00e+00, error(valid)=8.69e-02, acc(valid)=9.79e-01\n",
- "Epoch 40: 22.3s to complete\n",
- " error(train)=2.16e-03, acc(train)=1.00e+00, error(valid)=8.94e-02, acc(valid)=9.79e-01\n",
- "Epoch 45: 17.4s to complete\n",
- " error(train)=1.91e-03, acc(train)=1.00e+00, error(valid)=8.66e-02, acc(valid)=9.78e-01\n",
- "Epoch 50: 22.0s to complete\n",
- " error(train)=1.76e-03, acc(train)=1.00e+00, error(valid)=8.73e-02, acc(valid)=9.79e-01\n",
- "Epoch 55: 16.9s to complete\n",
- " error(train)=2.14e-03, acc(train)=1.00e+00, error(valid)=9.00e-02, acc(valid)=9.79e-01\n",
- "Epoch 60: 21.0s to complete\n",
- " error(train)=1.98e-03, acc(train)=1.00e+00, error(valid)=8.77e-02, acc(valid)=9.80e-01\n",
- "Epoch 65: 15.8s to complete\n",
- " error(train)=1.09e-02, acc(train)=9.96e-01, error(valid)=1.07e-01, acc(valid)=9.75e-01\n",
- "Epoch 70: 14.3s to complete\n",
- " error(train)=9.57e-04, acc(train)=1.00e+00, error(valid)=8.86e-02, acc(valid)=9.80e-01\n",
- "Epoch 75: 18.5s to complete\n",
- " error(train)=9.22e-04, acc(train)=1.00e+00, error(valid)=8.57e-02, acc(valid)=9.80e-01\n",
- "Epoch 80: 20.6s to complete\n",
- " error(train)=1.03e-03, acc(train)=1.00e+00, error(valid)=8.58e-02, acc(valid)=9.81e-01\n",
- "Epoch 85: 15.3s to complete\n",
- " error(train)=1.20e-03, acc(train)=1.00e+00, error(valid)=8.28e-02, acc(valid)=9.80e-01\n",
- "Epoch 90: 14.0s to complete\n",
- " error(train)=1.24e-03, acc(train)=1.00e+00, error(valid)=8.32e-02, acc(valid)=9.81e-01\n",
- "Epoch 95: 18.4s to complete\n",
- " error(train)=1.33e-03, acc(train)=1.00e+00, error(valid)=8.41e-02, acc(valid)=9.80e-01\n",
- "Epoch 100: 14.4s to complete\n",
- " error(train)=1.70e-03, acc(train)=1.00e+00, error(valid)=8.21e-02, acc(valid)=9.80e-01\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L1Penalty(0.001)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 18.9s to complete\n",
- " error(train)=2.53e-01, acc(train)=9.31e-01, error(valid)=2.40e-01, acc(valid)=9.35e-01\n",
- "Epoch 10: 17.4s to complete\n",
- " error(train)=2.20e-01, acc(train)=9.36e-01, error(valid)=2.13e-01, acc(valid)=9.38e-01\n",
- "Epoch 15: 22.7s to complete\n",
- " error(train)=2.26e-01, acc(train)=9.34e-01, error(valid)=2.14e-01, acc(valid)=9.37e-01\n",
- "Epoch 20: 14.2s to complete\n",
- " error(train)=2.75e-01, acc(train)=9.15e-01, error(valid)=2.58e-01, acc(valid)=9.22e-01\n",
- "Epoch 25: 17.6s to complete\n",
- " error(train)=2.31e-01, acc(train)=9.30e-01, error(valid)=2.29e-01, acc(valid)=9.30e-01\n",
- "Epoch 30: 21.7s to complete\n",
- " error(train)=2.20e-01, acc(train)=9.36e-01, error(valid)=2.12e-01, acc(valid)=9.40e-01\n",
- "Epoch 35: 14.0s to complete\n",
- " error(train)=2.11e-01, acc(train)=9.37e-01, error(valid)=2.03e-01, acc(valid)=9.40e-01\n",
- "Epoch 40: 15.9s to complete\n",
- " error(train)=2.06e-01, acc(train)=9.37e-01, error(valid)=2.03e-01, acc(valid)=9.39e-01\n",
- "Epoch 45: 17.8s to complete\n",
- " error(train)=2.02e-01, acc(train)=9.40e-01, error(valid)=1.99e-01, acc(valid)=9.42e-01\n",
- "Epoch 50: 15.7s to complete\n",
- " error(train)=2.01e-01, acc(train)=9.40e-01, error(valid)=1.95e-01, acc(valid)=9.45e-01\n",
- "Epoch 55: 14.7s to complete\n",
- " error(train)=2.07e-01, acc(train)=9.36e-01, error(valid)=2.00e-01, acc(valid)=9.39e-01\n",
- "Epoch 60: 21.3s to complete\n",
- " error(train)=2.38e-01, acc(train)=9.28e-01, error(valid)=2.34e-01, acc(valid)=9.31e-01\n",
- "Epoch 65: 17.6s to complete\n",
- " error(train)=2.07e-01, acc(train)=9.38e-01, error(valid)=2.02e-01, acc(valid)=9.41e-01\n",
- "Epoch 70: 21.5s to complete\n",
- " error(train)=1.98e-01, acc(train)=9.40e-01, error(valid)=1.92e-01, acc(valid)=9.44e-01\n",
- "Epoch 75: 14.2s to complete\n",
- " error(train)=2.09e-01, acc(train)=9.37e-01, error(valid)=2.08e-01, acc(valid)=9.39e-01\n",
- "Epoch 80: 16.7s to complete\n",
- " error(train)=2.03e-01, acc(train)=9.40e-01, error(valid)=1.98e-01, acc(valid)=9.42e-01\n",
- "Epoch 85: 16.8s to complete\n",
- " error(train)=1.98e-01, acc(train)=9.42e-01, error(valid)=1.93e-01, acc(valid)=9.43e-01\n",
- "Epoch 90: 23.6s to complete\n",
- " error(train)=2.00e-01, acc(train)=9.38e-01, error(valid)=1.97e-01, acc(valid)=9.40e-01\n",
- "Epoch 95: 15.5s to complete\n",
- " error(train)=1.88e-01, acc(train)=9.44e-01, error(valid)=1.86e-01, acc(valid)=9.46e-01\n",
- "Epoch 100: 17.7s to complete\n",
- " error(train)=1.97e-01, acc(train)=9.42e-01, error(valid)=1.93e-01, acc(valid)=9.46e-01\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L2Penalty(0.0001)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 10.8s to complete\n",
- " error(train)=6.49e-02, acc(train)=9.81e-01, error(valid)=9.48e-02, acc(valid)=9.72e-01\n",
- "Epoch 10: 15.5s to complete\n",
- " error(train)=2.65e-02, acc(train)=9.93e-01, error(valid)=8.14e-02, acc(valid)=9.78e-01\n",
- "Epoch 15: 13.5s to complete\n",
- " error(train)=1.41e-02, acc(train)=9.97e-01, error(valid)=8.20e-02, acc(valid)=9.78e-01\n",
- "Epoch 20: 14.2s to complete\n",
- " error(train)=6.66e-03, acc(train)=9.99e-01, error(valid)=8.53e-02, acc(valid)=9.78e-01\n",
- "Epoch 25: 11.3s to complete\n",
- " error(train)=3.56e-03, acc(train)=1.00e+00, error(valid)=8.34e-02, acc(valid)=9.79e-01\n",
- "Epoch 30: 12.6s to complete\n",
- " error(train)=2.80e-03, acc(train)=1.00e+00, error(valid)=8.27e-02, acc(valid)=9.79e-01\n",
- "Epoch 35: 10.2s to complete\n",
- " error(train)=2.53e-03, acc(train)=1.00e+00, error(valid)=8.15e-02, acc(valid)=9.80e-01\n",
- "Epoch 40: 13.2s to complete\n",
- " error(train)=2.44e-03, acc(train)=1.00e+00, error(valid)=8.21e-02, acc(valid)=9.79e-01\n",
- "Epoch 45: 17.8s to complete\n",
- " error(train)=2.29e-03, acc(train)=1.00e+00, error(valid)=7.91e-02, acc(valid)=9.80e-01\n",
- "Epoch 50: 13.7s to complete\n",
- " error(train)=2.24e-03, acc(train)=1.00e+00, error(valid)=8.01e-02, acc(valid)=9.79e-01\n",
- "Epoch 55: 12.4s to complete\n",
- " error(train)=2.20e-03, acc(train)=1.00e+00, error(valid)=7.90e-02, acc(valid)=9.80e-01\n",
- "Epoch 60: 13.7s to complete\n",
- " error(train)=2.39e-03, acc(train)=1.00e+00, error(valid)=7.88e-02, acc(valid)=9.80e-01\n",
- "Epoch 65: 11.1s to complete\n",
- " error(train)=2.12e-03, acc(train)=1.00e+00, error(valid)=7.79e-02, acc(valid)=9.80e-01\n",
- "Epoch 70: 11.4s to complete\n",
- " error(train)=2.22e-03, acc(train)=1.00e+00, error(valid)=7.74e-02, acc(valid)=9.80e-01\n",
- "Epoch 75: 14.5s to complete\n",
- " error(train)=2.16e-03, acc(train)=1.00e+00, error(valid)=7.53e-02, acc(valid)=9.80e-01\n",
- "Epoch 80: 12.6s to complete\n",
- " error(train)=2.19e-03, acc(train)=1.00e+00, error(valid)=7.74e-02, acc(valid)=9.81e-01\n",
- "Epoch 85: 11.5s to complete\n",
- " error(train)=2.21e-03, acc(train)=1.00e+00, error(valid)=7.49e-02, acc(valid)=9.81e-01\n",
- "Epoch 90: 11.4s to complete\n",
- " error(train)=2.11e-03, acc(train)=1.00e+00, error(valid)=7.49e-02, acc(valid)=9.81e-01\n",
- "Epoch 95: 11.2s to complete\n",
- " error(train)=2.12e-03, acc(train)=1.00e+00, error(valid)=7.54e-02, acc(valid)=9.80e-01\n",
- "Epoch 100: 15.5s to complete\n",
- " error(train)=2.32e-03, acc(train)=1.00e+00, error(valid)=7.57e-02, acc(valid)=9.81e-01\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L2Penalty(0.01)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 10.6s to complete\n",
- " error(train)=2.36e-01, acc(train)=9.41e-01, error(valid)=2.21e-01, acc(valid)=9.45e-01\n",
- "Epoch 10: 10.9s to complete\n",
- " error(train)=2.13e-01, acc(train)=9.45e-01, error(valid)=1.99e-01, acc(valid)=9.52e-01\n",
- "Epoch 15: 11.8s to complete\n",
- " error(train)=2.22e-01, acc(train)=9.44e-01, error(valid)=2.08e-01, acc(valid)=9.48e-01\n",
- "Epoch 20: 11.0s to complete\n",
- " error(train)=2.15e-01, acc(train)=9.46e-01, error(valid)=2.03e-01, acc(valid)=9.49e-01\n",
- "Epoch 25: 15.5s to complete\n",
- " error(train)=2.14e-01, acc(train)=9.47e-01, error(valid)=2.03e-01, acc(valid)=9.50e-01\n",
- "Epoch 30: 11.8s to complete\n",
- " error(train)=2.02e-01, acc(train)=9.51e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
- "Epoch 35: 14.7s to complete\n",
- " error(train)=2.11e-01, acc(train)=9.47e-01, error(valid)=2.00e-01, acc(valid)=9.50e-01\n",
- "Epoch 40: 12.3s to complete\n",
- " error(train)=2.01e-01, acc(train)=9.50e-01, error(valid)=1.92e-01, acc(valid)=9.53e-01\n",
- "Epoch 45: 11.9s to complete\n",
- " error(train)=2.01e-01, acc(train)=9.52e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
- "Epoch 50: 12.1s to complete\n",
- " error(train)=2.05e-01, acc(train)=9.50e-01, error(valid)=1.93e-01, acc(valid)=9.55e-01\n",
- "Epoch 55: 11.4s to complete\n",
- " error(train)=2.03e-01, acc(train)=9.50e-01, error(valid)=1.92e-01, acc(valid)=9.54e-01\n",
- "Epoch 60: 12.8s to complete\n",
- " error(train)=2.07e-01, acc(train)=9.49e-01, error(valid)=1.95e-01, acc(valid)=9.54e-01\n",
- "Epoch 65: 11.7s to complete\n",
- " error(train)=2.10e-01, acc(train)=9.46e-01, error(valid)=1.98e-01, acc(valid)=9.49e-01\n",
- "Epoch 70: 12.8s to complete\n",
- " error(train)=2.03e-01, acc(train)=9.50e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
- "Epoch 75: 15.1s to complete\n",
- " error(train)=2.06e-01, acc(train)=9.50e-01, error(valid)=1.95e-01, acc(valid)=9.54e-01\n",
- "Epoch 80: 11.1s to complete\n",
- " error(train)=1.98e-01, acc(train)=9.52e-01, error(valid)=1.86e-01, acc(valid)=9.56e-01\n",
- "Epoch 85: 12.4s to complete\n",
- " error(train)=2.02e-01, acc(train)=9.51e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
- "Epoch 90: 13.2s to complete\n",
- " error(train)=2.02e-01, acc(train)=9.51e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
- "Epoch 95: 10.6s to complete\n",
- " error(train)=2.00e-01, acc(train)=9.52e-01, error(valid)=1.87e-01, acc(valid)=9.57e-01\n",
- "Epoch 100: 11.3s to complete\n",
- " error(train)=1.98e-01, acc(train)=9.53e-01, error(valid)=1.86e-01, acc(valid)=9.58e-01\n"
- ]
- }
- ],
- "source": [
- "weights_penalties = [\n",
- " None,\n",
- " L1Penalty(1e-5),\n",
- " L1Penalty(1e-3),\n",
- " L2Penalty(1e-4),\n",
- " L2Penalty(1e-2)\n",
- "]\n",
- "run_info = OrderedDict()\n",
- "models = OrderedDict()\n",
- "for weights_penalty in weights_penalties:\n",
- " # Reset random number generator and data provider states on each run\n",
- " # to ensure reproducibility of results\n",
- " rng.seed(seed)\n",
- " train_data.reset()\n",
- " valid_data.reset()\n",
- " print('Regularisation: {0}'.format(weights_penalty))\n",
- " model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, weights_init, \n",
- " biases_init, weights_penalty),\n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, weights_init, \n",
- " biases_init, weights_penalty),\n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, weights_init, \n",
- " biases_init, weights_penalty)\n",
- " ])\n",
- " optimiser = Optimiser(model, error, learning_rule, train_data, valid_data, data_monitors)\n",
- " run_info[weights_penalty] = optimiser.train(num_epochs, stats_interval)\n",
- " models[weights_penalty] = model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot the training set error against epoch number for all different regularisation schemes on the same axis. On a second axis plot the validation set error against epoch number for all the different regularisation schemes. Interpret and comment on what you see."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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+f8JKH4MFntwopWhfE2Lb/THQmp3PpTh2g7eknOx28L3iDb68UEOLhWUruo7M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\nQoheJAm4EEIIIYQQvej/AeKNgoTC9s87AAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "plt.style.use('ggplot')\n",
- "\n",
- "fig = plt.figure(figsize=(12, 6))\n",
- "ax1 = fig.add_subplot(1, 2, 1)\n",
- "ax2 = fig.add_subplot(1, 2, 2)\n",
- "for weight_penalty, run in run_info.items():\n",
- " stats, keys, run_time = run\n",
- " ax1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys['error(train)']], label=str(weight_penalty))\n",
- " ax2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys['error(valid)']], label=str(weight_penalty))\n",
- "ax1.legend(loc=0)\n",
- "ax1.set_xlabel('Epoch number')\n",
- "ax1.set_ylabel('Training set error')\n",
- "ax1.set_yscale('log')\n",
- "ax2.legend(loc=0)\n",
- "ax2.set_xlabel('Epoch number')\n",
- "ax2.set_ylabel('Validation set error')\n",
- "ax2.set_yscale('log')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The cell below defines two functions for visualising the first layer weights of the models trained above. The first plots a histogram of the weight values and the second plots the first layer weights as feature maps, i.e. each row of the first layer weight matrix (corresponding to the weights going from the input MNIST image to a particular first layer output) is visualised as a $28\\times 28$ image. In these feature maps white corresponds to negative weights, black to positive weights and grey to weights close to zero. \n",
- "\n",
- "Use these functions to plot a histogram and feature map visualisation for the first layer weights of each model trained above. You should try to interpret the plots in the context of what you were told in the lecture about the behaviour of L1 versus L2 regularisation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_param_histogram(param, fig_size=(6, 3), interval=[-1.5, 1.5]):\n",
- " \"\"\"Plots a normalised histogram of an array of parameter values.\"\"\"\n",
- " fig = plt.figure(figsize=fig_size)\n",
- " ax = fig.add_subplot(111)\n",
- " ax.hist(param.flatten(), 50, interval, normed=True)\n",
- " ax.set_xlabel('Parameter value')\n",
- " ax.set_ylabel('Normalised frequency density')\n",
- " return fig, ax\n",
- "\n",
- "def visualise_first_layer_weights(weights, fig_size=(5, 5)):\n",
- " \"\"\"Plots a grid of first layer weights as feature maps.\"\"\"\n",
- " fig = plt.figure(figsize=fig_size)\n",
- " num_feature_maps = weights.shape[0]\n",
- " grid_size = int(num_feature_maps**0.5)\n",
- " max_abs = np.abs(model.params[0]).max()\n",
- " tiled = -np.ones((30 * grid_size, \n",
- " 30 * num_feature_maps // grid_size)) * max_abs\n",
- " for i, fm in enumerate(model.params[0]):\n",
- " r, c = i % grid_size, i // grid_size\n",
- " tiled[1 + r * 30:(r + 1) * 30 - 1, \n",
- " 1 + c * 30:(c + 1) * 30 - 1] = fm.reshape((28, 28))\n",
- " ax = fig.add_subplot(111)\n",
- " max_abs = np.abs(tiled).max()\n",
- " ax.imshow(tiled, cmap='Greys', vmin=-max_abs, vmax=max_abs)\n",
- " ax.axis('off')\n",
- " fig.tight_layout()\n",
- " plt.show()\n",
- " return fig, ax"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: None\n"
- ]
- },
- {
- "data": {
- "image/png": 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NxWLK5/OSpPPzcyPhTyYTCz40AAk8ThoYDSRnlPZ4PIYVw99sNBpWAn766afq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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L1Penalty(1e-05)\n"
- ]
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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BwUNEINsYSWOy6gWAY8+qbG9Uhwzie/L4RCIRZLNZV8QumhOCdruNYDDosovR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X6WF4cmWGVzSO653+/j+vqas22Q+En9sax5kJUPP/yQkYdJ1bLKvMtbcQmkflLfXVxc4Pr6GtPp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KCUGp08vlMiqVCuLxOB4+fAjLshzJDGkiqGmUKN0MBY/c3NwwA54kCdPvTCbDPprASBJPpVJI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\nSP8bceV+ZgtIZjIAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L1Penalty(0.001)\n"
- ]
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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/9NFH1u7KB0qmsjWva3Z//PHHr63B/agbsxllKb16fK7IXXq7s8g2NZ9nu5mt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xtu1X3FXjs+oT9YY8xGBYTKD7znwKnar8iDArhO7eH7OaxXnZUiQAr4VB+zWDEGhjuMgKjznht3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\n3lpa4/98LroAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L2Penalty(0.0001)\n"
- ]
- },
- {
- "data": {
- "image/png": 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gJycH5eXlGDZsGAICAizuaiuFQgGFQgEASExMhLe3dxsnMpw1ZjbK4QttnYBa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\nQQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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kEkqn03r8+LE5HzTn2WxWpVJJg8HACsAgsHkeiNnvMt4bI+wWUEDwnJ2d6cmT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kyDG0ziAMoRXjQhHoYJGhCJSwS1CWGh+fC8GEcSB908YYwB4RlzhsfmLx6gHF/hBaMArx3Phvlm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\nExhxdlrXXFxcmBQqRpgE66NHjzQ8PGyJIRKrXLocYm+E4e5C1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nHQCLLGRCs4MORZ8PpR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+LNzqKNZobGzM9owk84jb7bYJGxFl7e7uKp/PKxaLWagbDAat0hJ46fMMIfsZgj8RGZEUxpFK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\nyYnBAsVi0XB3V0AJQ+e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uhRBmFhCmO48ulZISe6h+wBzYF3eff5Hx0njCGCn34EP8dpMO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wcHQxrfTIMBiqOeMJr5EGHyDqRrjLxSqejo6EjxeFypVEoLCwuqVqv2XtBSuXfvnv2+eDxu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\nv7+3Cpug5QaCo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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Regularisation: L2Penalty(0.01)\n"
- ]
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
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- "output_type": "display_data"
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- {
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FECzCc4Q19rKoyaSiwT1fFmqZsTvvmEMWBIskhZvH/HXssiy5ocWjJGsO5ks4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\nMuBWq6Ver5dRZmazmS4vLyVJV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DRtSB8jpjhwxaawnQhNxPWJArSI2TBGZ4B52hHkmih7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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "for weight_penalty, model in models.items():\n",
- " print('Regularisation: {0}'.format(weight_penalty))\n",
- " _ = plot_param_histogram(model.params[0], interval=[-0.5, 0.5])\n",
- " _ = visualise_first_layer_weights(model.params[0])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 4: Random data augmentation\n",
- "\n",
- "Another technique mentioned in the lectures for trying to reduce overfitting is to artificially augment the training data set by performing random transformations to the original training data inputs. The idea is to produce further artificial inputs corresponding to the same target class as the original input. The closer the artificially generated inputs are to the appearing like the true inputs the better as they provide more realistic additional examples for the model to learn from.\n",
- "\n",
- "For the handwritten image inputs in the MNIST dataset, an obvious way to considering augmenting the dataset is to apply small rotations to the original images. Providing the rotations are small we would generally expect that what we would identify as the class of a digit image will remain the same.\n",
- "\n",
- "Implement a function which given a batch of MNIST images as 784-dimensional vectors, i.e. an array of shape `(batch_size, 784)`\n",
- "\n",
- " * chooses 25% of the images in the batch at random\n",
- " * for each image in the 25% chosen, rotates the image by a random angle in $\\left[-30^\\circ,\\,30^\\circ\\right]$\n",
- " * returns a new array of size `(batch_size, 784)` in which the rows corresponding to the 25% chosen images are the vectors corresponding to the new randomly rotated images, while the remaining rows correspond to the original images.\n",
- " \n",
- "You will need to make use of the [`scipy.ndimage.interpolation.rotate`](https://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.ndimage.interpolation.rotate.html#scipy.ndimage.interpolation.rotate) function which is imported below for you. For computational efficiency you should use bilinear interpolation by setting `order=1` as a keyword argument to this function rather than using the default of bicubic interpolation. Additionally you should make sure the original shape of the images is maintained by passing a `reshape=False` keyword argument."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "from scipy.ndimage.interpolation import rotate\n",
- "\n",
- "def random_rotation(inputs, rng):\n",
- " \"\"\"Randomly rotates a subset of images in a batch.\n",
- " \n",
- " Args:\n",
- " inputs: Input image batch, an array of shape (batch_size, 784).\n",
- " rng: A seeded random number generator.\n",
- " \n",
- " Returns:\n",
- " An array of shape (batch_size, 784) corresponding to a copy\n",
- " of the original `inputs` array with the randomly selected\n",
- " images rotated by a random angle. The original `inputs`\n",
- " array should not be modified.\n",
- " \"\"\"\n",
- " orig_ims = inputs.reshape((-1, 28, 28))\n",
- " new_ims = orig_ims.copy()\n",
- " indices = rng.choice(orig_ims.shape[0], orig_ims.shape[0] // 4, False)\n",
- " angles = rng.uniform(-1., 1., size=indices.shape[0]) * 30.\n",
- " for i, j in enumerate(indices):\n",
- " new_ims[j] = rotate(orig_ims[j], angles[i], order=1, reshape=False)\n",
- " return new_ims.reshape((-1, 784))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Use the cell below to test your implementation. This uses the `show_batch_of_images` function we implemented in the first lab notebook to visualise the images in a batch before and after application of the random rotation transformation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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DRaW1oopKRcXOqKL6g3Lp0iWBUioQQoTc3NyWbk6LUVJSIiQmJgqUUoFSKrx48cLmOluF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\nAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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r1k1QmfYCMj8UCoVoW7fGxkYkJyeDZVl8+eWXSE5ORv/+/UU5jTY1NeHChQu4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\nkXEwsqhkZByMLCoZGQfzfyHNI6b77aCeAAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from mlp.data_providers import MNISTDataProvider\n",
- "import matplotlib.pyplot as plt\n",
- "def show_batch_of_images(img_batch, fig_size=(3, 3)):\n",
- " fig = plt.figure(figsize=fig_size)\n",
- " batch_size, im_height, im_width = img_batch.shape\n",
- " # calculate no. columns per grid row to give square grid\n",
- " grid_size = int(batch_size**0.5)\n",
- " # intialise empty array to tile image grid into\n",
- " tiled = np.empty((im_height * grid_size, \n",
- " im_width * batch_size // grid_size))\n",
- " # iterate over images in batch + indexes within batch\n",
- " for i, img in enumerate(img_batch):\n",
- " # calculate grid row and column indices\n",
- " r, c = i % grid_size, i // grid_size\n",
- " tiled[r * im_height:(r + 1) * im_height, \n",
- " c * im_height:(c + 1) * im_height] = img\n",
- " ax = fig.add_subplot(111)\n",
- " ax.imshow(tiled, cmap='Greys') #, vmin=0., vmax=1.)\n",
- " ax.axis('off')\n",
- " fig.tight_layout()\n",
- " plt.show()\n",
- " return fig, ax\n",
- "\n",
- "test_data = MNISTDataProvider('test', 100, rng=rng)\n",
- "inputs, targets = test_data.next()\n",
- "_ = show_batch_of_images(inputs.reshape((-1, 28, 28)))\n",
- "transformed_inputs = random_rotation(inputs, rng)\n",
- "_ = show_batch_of_images(transformed_inputs.reshape((-1, 28, 28)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 5: Training with data augmentation\n",
- "\n",
- "One simple way to use data augmentation is to just statically augment the training data set - for example we could iterate through the training dataset applying a transformation function like that implemented above to generate new artificial inputs, and use both the original and newly generated data in a new data provider object. We are quite limited however in how far we can augment the dataset by with a static method like this however - if we wanted to apply 9 random rotations to each image in the original datase, we would end up with a dataset with 10 times the memory requirements and that would take 10 times as long to run through each epoch.\n",
- "\n",
- "An alternative is to randomly augment the data on the fly as we iterate through the data provider in each epoch. In this method a new data provider class can be defined that inherits from the original data provider to be augmented, and provides a new `next` method which applies a random transformation function like that implemented in the previous exercise to each input batch before returning it. This method means that on every epoch a different set of training examples are provided to the model and so in some ways corresponds to an 'infinite' data set (although the amount of variability in the dataset will still be significantly less than the variability in all possible digit images). Compared to static augmentation, this dynamic augmentation scheme comes at the computational cost of having to apply the random transformation each time a new batch is provided. We can vary this overhead by changing the proportion of images in a batch randomly transformed.\n",
- "\n",
- "An implementation of this scheme has been provided for the MNIST data set in the `AugmentedMNISTDataProvider` object in the `mlp.data_providers` module. In addition to the arguments of the original `MNISTDataProvider.__init__` method, this additional takes a `transformer` argument, which should be a function which takes as arguments an inputs batch array and a random number generator object, and returns an array corresponding to a random transformation of the inputs. \n",
- "\n",
- "Train a model with the same architecture as in exercise 3 and with no L1 / L2 regularisation using a training data provider which randomly augments the training images using your `random_rotation` transformer function. Plot the training and validation set errors over the training epochs and compare this plot to your previous results from exercise 3. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "from mlp.data_providers import AugmentedMNISTDataProvider\n",
- "\n",
- "aug_train_data = AugmentedMNISTDataProvider('train', rng=rng, transformer=random_rotation)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 18.6s to complete\n",
- " error(train)=1.45e-01, acc(train)=9.57e-01, error(valid)=1.23e-01, acc(valid)=9.65e-01\n",
- "Epoch 10: 19.2s to complete\n",
- " error(train)=8.53e-02, acc(train)=9.74e-01, error(valid)=8.48e-02, acc(valid)=9.74e-01\n",
- "Epoch 15: 19.3s to complete\n",
- " error(train)=6.26e-02, acc(train)=9.81e-01, error(valid)=7.69e-02, acc(valid)=9.77e-01\n",
- "Epoch 20: 22.9s to complete\n",
- " error(train)=5.26e-02, acc(train)=9.85e-01, error(valid)=7.79e-02, acc(valid)=9.76e-01\n",
- "Epoch 25: 16.4s to complete\n",
- " error(train)=4.33e-02, acc(train)=9.88e-01, error(valid)=7.29e-02, acc(valid)=9.78e-01\n",
- "Epoch 30: 19.1s to complete\n",
- " error(train)=3.72e-02, acc(train)=9.89e-01, error(valid)=7.23e-02, acc(valid)=9.78e-01\n",
- "Epoch 35: 19.4s to complete\n",
- " error(train)=3.56e-02, acc(train)=9.90e-01, error(valid)=7.50e-02, acc(valid)=9.79e-01\n",
- "Epoch 40: 18.6s to complete\n",
- " error(train)=2.90e-02, acc(train)=9.93e-01, error(valid)=7.00e-02, acc(valid)=9.79e-01\n",
- "Epoch 45: 18.8s to complete\n",
- " error(train)=2.77e-02, acc(train)=9.93e-01, error(valid)=6.72e-02, acc(valid)=9.80e-01\n",
- "Epoch 50: 19.0s to complete\n",
- " error(train)=3.12e-02, acc(train)=9.91e-01, error(valid)=7.63e-02, acc(valid)=9.80e-01\n",
- "Epoch 55: 20.1s to complete\n",
- " error(train)=2.23e-02, acc(train)=9.94e-01, error(valid)=6.63e-02, acc(valid)=9.83e-01\n",
- "Epoch 60: 25.8s to complete\n",
- " error(train)=2.10e-02, acc(train)=9.94e-01, error(valid)=6.99e-02, acc(valid)=9.81e-01\n",
- "Epoch 65: 20.6s to complete\n",
- " error(train)=1.95e-02, acc(train)=9.95e-01, error(valid)=6.53e-02, acc(valid)=9.83e-01\n",
- "Epoch 70: 17.1s to complete\n",
- " error(train)=2.11e-02, acc(train)=9.94e-01, error(valid)=6.88e-02, acc(valid)=9.81e-01\n",
- "Epoch 75: 24.2s to complete\n",
- " error(train)=1.93e-02, acc(train)=9.95e-01, error(valid)=6.44e-02, acc(valid)=9.83e-01\n",
- "Epoch 80: 20.2s to complete\n",
- " error(train)=1.78e-02, acc(train)=9.95e-01, error(valid)=6.52e-02, acc(valid)=9.83e-01\n",
- "Epoch 85: 20.1s to complete\n",
- " error(train)=1.68e-02, acc(train)=9.96e-01, error(valid)=6.30e-02, acc(valid)=9.84e-01\n",
- "Epoch 90: 19.9s to complete\n",
- " error(train)=2.17e-02, acc(train)=9.94e-01, error(valid)=7.03e-02, acc(valid)=9.82e-01\n",
- "Epoch 95: 18.5s to complete\n",
- " error(train)=1.72e-02, acc(train)=9.96e-01, error(valid)=6.59e-02, acc(valid)=9.82e-01\n",
- "Epoch 100: 20.6s to complete\n",
- " error(train)=2.21e-02, acc(train)=9.93e-01, error(valid)=8.22e-02, acc(valid)=9.80e-01\n"
- ]
- }
- ],
- "source": [
- "batch_size = 100\n",
- "num_epochs = 100\n",
- "learning_rate = 0.01\n",
- "mom_coeff = 0.9\n",
- "stats_interval = 5\n",
- "\n",
- "rng.seed(seed)\n",
- "aug_train_data.reset()\n",
- "valid_data.reset()\n",
- "aug_train_data.batch_size = batch_size \n",
- "valid_data.batch_size = batch_size\n",
- "\n",
- "weights_init = GlorotUniformInit(0.5, rng=rng)\n",
- "biases_init = ConstantInit(0.)\n",
- "\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, weights_init, biases_init),\n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init),\n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
- "])\n",
- "\n",
- "error = CrossEntropySoftmaxError()\n",
- "learning_rule = MomentumLearningRule(learning_rate=learning_rate, mom_coeff=mom_coeff)\n",
- "data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
- "optimiser = Optimiser(\n",
- " model, error, learning_rule, aug_train_data, valid_data, data_monitors)\n",
- "\n",
- "aug_stats, aug_keys, aug_run_time = optimiser.train(\n",
- " num_epochs=num_epochs, stats_interval=stats_interval)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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xiyeAgKeC0Z5jGB09BnDHsI8nt/VprV/LyqZndumGUxWYRMw/hphvDAGrEqVU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\nMmRI+vLZ7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DNvUYCPNkbYvT1GbXWSKTN9eLz981ytpSnF+nfCeLwq0+f60PXMDvDtBbI1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33P\nscj7dvQ60dlJ6+tC/c4Xoa8L6a/vRrfu7VN6/PNtBAgEgtnLcX+Mr2/tYr7XwodXVc/aUK5Xz3PQ\nH0nypIghLxAUDGf8pnbs2DHe+9738thjj3HXXXcJAT+DDEZlXoxUMFx3BT09PTz99NNEFixB97H/\nhGgE9YF/Q2s+dE7HXlS2kZgcpC346hTnOv/QDu5F/eK/wlAA3Uf+nakW8AKBoHAJxGS+uKUDm1HP\np9bUYjbMbjeV2y7w8PYFLn590MfvD/tznR2BQHCenLHGue+++3jb296GySR6tM8077zQy4dWVrEt\n6qavZiWBQICnnnqKoLcK3b1fBasN9eufSY3IOUkqSy7CZann8OCzaJo6DbnPD9TNf0B96PPg8qD7\n9INIiy/JdZYEAkGekFRUvvJSJ4GYwn1ra/MihKMkSXxweSVX1JbyvZ29bOuY2dDJAoFgapndZoMi\n5/r5Lj5xVQ374w5aK1YTjyd46qmn6JcMKSFfMxf1kS+jvviHSR1XkiQWeTcyFO+iO7x3mnI/e9Fk\nGfXHj6D99FFYshzdvV9FKq/KdbYEAkGeoGka/72tlwP9Uf55VTULvNZcZ2nC6HUS/3pVDY1uC197\npYujg9FcZ0kgEJwjQsTPcq6sd/C59XM4oZSwz7sSSafn6aefpiMYQvevX4Qll6P95FHUXz2Jpk7c\nql7vXInV4OHQwOQaAPmOFhpC/cbn0Lb8Eentf4nunk8hWW25zpZAIMgjfnPIzwstQW6/2MvV8xy5\nzs6ksRh0fHbdHJwWA194sYPecCLXWRIIBOeAEPF5wKVVJfzntXUEsPG6fTkWWyn/93//R3N7B7p7\n7kNac2MqBOUPvoEmJyd0TJ1kYKH3BvojhxiMtkzzFcwOtM421C99HFoOI/39x9C982+QdCJmskAg\nmDg7OsP8cHcfq+vsvO+Sslxn55xxWQ18fv0cFFXj3zd3EIoruc6SQCCYJELE5wkLvFYeuK4e1Whj\nk/ky7O4ynn32WfYfPIh0x91It96B9sYW1G/+eyqG+QRocq/HqLNyuAis8dqeN1C//G+QTKL7ty+j\nW7Uu11kSCAR5xolgnAdf7WKuy8xHrqzO+zCNmRjyveEkX9rSQUIp3j5SAkE+IkR8HjHHaeYr18/F\nUWLjD7pLcFbUsmnTJrZv34608d1If/sROLof9av3puKMnwWj3kqTez0dQ9sIJ/pm4ApmHk3TUJ/9\nJeojX4Kq2lQH1oaFuc6WQCDIM4biCl98sQOTXuLTa+dgmeWRaCZKJob8gf4o33ytG3Vyg7gLBIIc\nMqkRWwW5p7zEyJevq+ffN3fwjG8xt9SaeP3114lGo6xZsx7J5Ub97wfSseQ/j1Q794zHW+C9gcOD\nf+LI4B9ZVv3/ZugqZgYtEUd78jto27YgrViD9DcfRjKZc52tWYWiagxEknQOJegKJegKJelKLw/F\nFBrcZhaVWVlYZmFRmTUvInAI8pdQXKEtEOd4IEZXKInHamCOw0Stw0RVqQmjPjeWb1nV+MrLnQxE\nZL54bT3lJYX1HIyKIV/Sz99cJmLICwT5gBDxZ2DHjh3s3LmTu+66K9dZGYXDYuAL19bx5S2d/Kqn\nidvmmti7dy/RaJTrrrsO3Se+jPqt/0D9yr2pjptnCJ1oM3qY61xNi38LF5Xfhtkwe4YLPx+0wCDq\nw1+C40eRbns/0tvfNWsHYZluNE0jGFPoDCWyAj0j2rtDSWT1pOXNatBR4zCxyGulxKSj2Rfjt4f9\nyAdTabxWAwtHiPr5Hsusj409WRRVo284SUc8SIkq47aKanKqkVWNrqEExwNxjvtjqXkgzmBEzqax\nGHTE5JPuHToJqkqN1DpM1DrM1DpMWYHvMOun7fnWNI3Ht/fyVm+Ej6yuZnF5/kSimQy3XeChL5zk\nVwd8lJcY2bjQffadBAJBTpE0TXw7mwhdXV25zsJpJBWVB1/t4rUTIW5y9hFp3Ut9fT0bN27EGAqi\nfvN+6OtG+tt/Qbdy7bjHCcTa+VPzfSypeBcXlt8ycxcwTWitR1Ef+SJEo+g+8FGkpavO6ThlZWUM\nDJzdLWm2EEkqdA0lUxb1ocQo0R5JnhRDBp1Etd1Ijd2UmhwmatNzl+V0MZRUVFr8cY4MRDkyEOPI\nYJSecKoDtU6Cea6MtT4l7mvsprzwFVZUje5wgvZggvZgPDvvHEqQUE5Wiy6LnnluCw0uMw1uMw1u\nC7UOE3rd7L/G2UAgKqdFeozj/pRYbw8mso1Hgw7mOMzMc5uZ5zIzz21hnsuMy6InKqt0DiWyU0d6\n3jWUIDmi8Wk36ahxmLOiPiPwq+wmDOdRTmVlZTz56lEe39HLOy/0FLyFWlE1vvxSJzu7wnxqTS0r\n5hSGUedcybd3gGBqyVX519TUTDitEPETZDaKeEhVuo9u7+G5Y0Guc/jQju+koqKCm2++GYuqpMTs\nkf1I77oT6frbxrVWbWn7KoHYCd6x4Ovodfk7uJf6xha0H34LnG50//QZpDnzzvlYs7ECTyoq3eG0\ny8spQj0QOxldQiLlepUS6Kl5jT0lbspsxvMWoIGYnBX1hwejHB2IEU1bTUtMOhZ609Z6r5UFZVYc\n5txFAUoqKl2hZFqonxTrXaEEIwy9lNsM1DnN1DlN1DnNzKvycLBjgFZ/ylp8YoTwNOok6tOifp7L\nTKPbwly3mVJT8UY7Sioq7cGUdb1thIV95H3psRrSQt3MXFfqv6t1mCftJpNxA+sIpp6BzLxzKIE/\netKan7Lem0ZZ7bPWe8vZv7C0Rgx87Jm3uLymlE+tqS2KhltMVvn08ydoD8b5i0Vu5nssNHksVJYa\ni+5r5mx8BwhmDiHiC4jZKuIh9bn3x3sH+OX+Qa52hLCe2IbD4eDWW2+l1GJBe+IhtO0vI63/C6T3\nfmDMsIq94f282PYAy2v+nib3upm/iPNEU1W0Z36M9uwvYeFF6P7xXiS785yOpWoar7WHOOBTiMVi\n6CUJnUR6kk6f607+1o+1XUoNsDL2/qPXnUwHSUWjO5QcJdT7h5OMMEDisuiz1vSRVvUquxGTfubc\nXBRVo3MowZHBKIfT4v5EMJ7Na43dmLLUe60sKrMyz20+LwvpWMTTVtsTwTgdwQTtQynB3h1KZPMh\nAZWlxlFivc6ZEnU24+jn4tQKXFY1OoIpS3KrP06rP0arP87QiNB8FSUGGtwW5qUt9g0uMxWlxrz4\nMjFRNE1jMCpnrept/pSVvWPo5P9s0kuphpBrhIXdZZ6QcD5fhhMKXRlhn7beZxq88inW+1Pdcmqd\nKd97g06icyjBJ59rw2Mx8MAN9afdH4VMICrzX690cmggmm3olpp0NHosWVHf5LFQVeDCXoj44kaI\n+AJiNov4DM8cHOSJXf0st0co69qG2WTilltuweN2oz39JNpzv4bLVqH7wMdP6+CpaRrPtXwWRU3w\n9vkPIEn54+esxSKo3/s67N2GtOZGpPd9EMkw+Y5niqrxStsQT+0fpD2YwGkxYJBSol7VUnNFG/07\nNZ+GixpBxk895fJiHCXaS2ax5TeaVDnmi3J4IJa22kfxp62yJr1Ek8fCQq8l64pTZjNMSBBEkgqd\nQyPdYFJivTecJFMUOglq7CbqnCbmOE4K9lqHacI+/BOpwDVNw5cWtK3+OK1pl5GuEQ0Hq0GXsthn\nhL3bTL3TnBd9CeKyyolgPCvYU6I9Rihx8hNGRYmBuS7LKMFebZ997kaKqtE/nBzlltM5lHKf8o/4\nWqCXoLLURFRW0YCvXl9PZWn+fp08H5KKSlsgwTFflGZfjGZfjLZAPCvsS0w6mtwW5nsLU9gLEV/c\nCBFfQOSDiAd4oTnAd97oYXFJnMb+7Wiqyk033UR1dTXqC79F+/n3oHERug99Bsk+eqTBtsBWXu/8\nb66q/yi19mU5uoLJofX3oD78RehuR3rvB5HWbZz0C0RWNba0Bvnl/kG6QknmOs28+2IvNy9rwO8b\nPHseRoj50wV+aq5oGqp6lu2nrNdLEtX2sf3U8xFN0+gflkdZ65t9saxvs9tqyIr6RWVWahwmesfw\nWR8Y0fnRoJOyltT6Edb1avv5RzI5nwo8LqvpKCtxWnzpjpv+eNblKNPISIn7tL+9x4J7Cspa1TTi\nskZMVonJKtGkenJZVoklVWIjt2fXZX5rxJIq4YQyqmFkMUhpFxhLVqzXuwrDhWg4oZwi7hMMxWX+\nae0CakxiNNORZIR9RtQf88VoC8ROE/YZUT/fm7/CXoj44kaI+AIiX0Q8wBvtIf7rlS7mmBNcMrST\nWCTCxo0bmTdvHtrOrajfexC8FakQlOVV2f1UTeb3R/+VEmMZGxo+k8MrmBja4bdQH/0yqBq6f/wk\n0gWXTmr/pKKyqWWIX+4fpG84SaPbzO0Xl7GyrhSdJIkKfAZIKhrHA7FUh9mBKIcHo3SHTh912KSX\nmOMwneIGY6aq9Pz9+8djqstf1TR6w8mU1T4Qy/ra9w2fbJQ4zfqsxb7OaUJROU1oR2WV+AixfaoY\njysTr9J1UioKjNmgw2qQsBh0WAw6rMbUVOcwMzct2CsLzC1oIog6YGIkFY0TwTjHBkcK+3jWfanE\nmHLFyQj7BXki7EX5FzdCxBcQ+STiAd7qjfCfL3bg0idZHdtDKODn2muvZfHixWjHDqB+54ug06H7\n588hzVuQ3e/wwLPs6f0p1zZ8Hq9tfu4u4CyoW/6I9r+PQXk1ug9/Bqli4jd9QlF5/liQpw8MMhiR\nWeC18J6Ly1heWzLqpSIq8NwwFJM5MhijO5SgOu0SU14y8wJypso/HFfSfvZpYR+IcSIwOvoKpBoy\nWZFt0GExSmnxrRslvi2niPHRaaR0mtRk0kuzXkjlElEHnDsZYd/si2XF/fEzCPv5HgtV9tnVUBTl\nX9wIEV9A5JuIB2jxxbh/Uzt6Nck12n58vd2sWbOGpUuXonV3pEJQhoIpK/aS5QAklSi/PfIRKksv\n4m11/5zbCxgDTZbRfvE9tM1/gCXLU/79tpIJ7RuXVf54NMCvD/rwR2UuLLdy+5IyllbZxhQyogIv\nbnJZ/rKqMTCcxDhCuM82H/NiQNQBU0tS0WgPxjk2jrC3GU92nm10m7EZ9WhoaICmkZ6nfpN2YcwI\nmMxIs6em005Jk1E8mTayhjZin5O/ARbWeKkyJcUgd0WKEPEFRD6KeIDOoQT3bzpBOJbkJuMRBjrb\nWL58OatXr4ahAOq3/gM6WpHuuAfd1dcDsLf35xwe+D0bF/wXpabKHF/BSbThEOqjX4FDb6bCZf7l\n/xsz0s6pRJIKzx4J8H8HfQTjCksqbbxniZeLK8YW79FolK6uLhRFQafT4XQ6cTqdmEzF2bmtWBEC\nTiDugelnpLDP+Nkf98dP+xKVSzxWAwu8lvRkZb7XUhB9QQRnRoj4AiJfRTzAYCTJ5ze10z2U4F2l\nx+k/foSLLrqI9evXIyViqI99Fd7ahfSO9yLd/D5icoDfHf0oje71XF79N7nOPgBadzvqt78A/gGk\n938I3ZXXnHWfcELh94f9/PaQj1BC5bLqEt5zsZcLKmyj0oVCITo7O+nq6qKzsxO/3z/m8axWa1bQ\nnzrZbGM3CAT5ixBwAnEP5IZMONeEoiFJICGl54yYS6N+I4HulHRA1j1n5H66dPpRx8mkSZ9P1TRC\nkpUdLT0cHYhxZDBGV+hkJ+dah4kFXgsL06K+wW2e0bC+gulHiPgCIp9FPEAorvCFF9s5OhDl3e4e\nBo7to7GxkRtvvBE9oP34EbRX/4x05TVI7/8Q23ufoDXwEvNcV3NJxe1Yja6c5V3btwP18f8Ckxnd\nPfchNS0+Y/qhuMJvD/n43WE/kaTKFbWl3H6xl4VlVjRNw+/3ZwV7V1cXoVAIAJPJRHV1NbW1tdTU\n1NDU1ERbWxvBYPC0KbNPBqPRiMPhGFPg2+129Hphtck3hIATiHuguDm1/MNxhWO+1KjVxwZjo8Lm\nGnQwz5Wy1i8ss7LAmxrZeTb5+AsmhxDxBUS+i3hI+KQ9PQAAIABJREFURbl44KVOdncP807vIMEj\nO6mtreUd73gHJpMJ7bc/Q/vt/8KFl6He9REOhJ7jyOCz6CQjF5XfygLPDeh10z9YSwZN09Ceewbt\n6R9CXSO6D92H5CkfN30gJvN/B3384UiAmKyyus7Ouy50Y1dOWtq7urqIxWIA2Gw2ampqsqLd6/Wi\n0520pJzpAZZlmVAoNKbADwaDKMqI0VMlCbvdPq4VX7jpzE6EgBOIe6C4OVv5ZwY+S1nqU8L+6ODJ\n0autBh3z0244C71WFpRZ8FonNh5GIZNUVMIJleGEMmKuMJwObTucXldi0lNVaqTabqLabpyS0cYn\ngxDxBUQhiHhI+R8+9FoXr7SFeEfZEPGj2/B4PNxyyy2UlJSgvvI82o8ehjnz0H34c4StCXb3/ITu\n8B5KTZUsLX8fNSWXcLJXkZruDZSZ1HQPofNfr/35N2ivbUZafhXSnf+CZDaPeU2DkSS/PujjT0cD\nqIrM2zxJLi6JMDzYS09PD8lkKmSh0+mkpqYmK9ydTucZK9NzfYA1TWN4eHhcgZ9pRGQYz03Hbrdj\nMpkwmUxFX+nnAiHgBOIeKG7OpfxVLTV69dG0pf7oYIzjI+Lou0f41y/0WpnvsVBqzq8vtaqmEUmm\nhPZw4qTwTgnxMdZlt6X2SZwlDK5JL1Fi0p+W1qBLDcRWnRX2KXFfbU9FMJvqUcCFiC8gCkXEQ2rk\nwu/u6OXZowGu8UYxtLyGzWbj1ltvxeVyob21M9WBNB5LOQhqGj1zDexdayHk0VPVmuTSl2LY/erZ\nT3aeSLf8NdJf3D6miO0fTvLLvd3sPnICe9JHvT6ELuJHU1P5Kisry4r2mpoaSktLJ3Xu6XqA4/H4\nuAI/HA4z1iNpNBqzgn7k8li/x1o/clk0CCaGEHACcQ8UN1NV/klFpdUfTwn7wZSw7xw66V9fYzex\n0JsaGGthmXXS/vWqpiGrGklFI5mey2pqWVZGz0duSypqats4+0aTp1rLVYaTCpGEypmEo05KhRAt\nMenTk45Sk54SY3qe+W3SU2rSnUxjTM2N6WtX0yNid4cSdIeS2XlPOEF3KEFM1kads6LEOErYZ6z4\nlaXGc+qvIER8AVFIIh5S1uL/3TfAz/cNcqUngevEa+j1em655RbKy8vROo6j7dyaSiwBkg5V0jjm\nPM5+1xEUSWFBqIkLQxdgxDSiR5Aum/7kuvSk06UOJkmpJ47R6yVd+jfp7Z5ypPqmUfkeHh7mQHMb\nrx9sJTTQS4kSRgJ0Oh0VFRVZ15jq6mosFst5/Ue5eIAVRWFoaCgr6BOJxKgpmUyO+1tVJ9aomkyD\nwGq1YrPZsnOLxVI0jQAh4ATiHihuprP8wwmFZl8s64pzdDCGL5oa+E0vwbx0iM2MuJazAls9RYhr\nTGJ8twlh0EkYdakxJU4T4OaxxHgmjZ5Scyok7nT3BdA0jUBMSQv7tMgPp0V+KMFw8uT7UALKbIas\n9b4qLfIzFn2zYWyBL0R8AVFoIj7Dbw/5+N7OPpa6FOp73iCZTPCOd7yDOXPmjLtPTA7yZu8vaA28\njMXgYEnF7TS4rkKSprZnvqZpBIPBrD/7iY5OhkNDACjoMTjLuLipngXz6qisrMRonNpYvvn2Apdl\n+axCfyKNgTM1CCRJwmq1ZqeRAv/UdVarNW8t/5qm4fF4xo1UJCgO8q0OEEwtM13+g5EkR9N+9ccG\noyQUDaNeSolqfUpYj1w26nVZwW04ZXtmfXY//cnfI7eN3l+HQUde1tkj0TSNUFyhO5wcLfJDCbrD\nSUJxZVR6j9WQtd5Xl6Ys+WbDW1wy7xJMycl9wZ8KhIifBgpVxAO82Brkm691M79U4+LAdsKhEDfe\neCNNTU1n3M8XbWFX948YjB7DY23ksqr347U2oSgKyWQyO2XEpSzLo9afOp2aLhQKEYlEAND0Jgb0\nTkImDxc01vHO5Y2Ul56fpf1sFPMLPFMW0WiUSCRCNBodtXzqPJFIjHkcvV4/ptgfT/QbDBPrOK1p\n2pj32Vjz8baPtyzLMrIsI0kSHo+Hqqoqqqurqaqqwu125/0LTjBxirkOEIjyL1TCceWk1X6E9b47\nlMAfU3BZerl+4Q+R1cX8zaWfnPH8CRE/DRSyiAfY0RnmKy93UmnWWB3bg2+gn+XLl2M2m88iuuOo\njg70tUfQmRJEu72Ej81BTUws4ookSRiNxuxkMBgwmUwYDAZUg5mWZCm7hqwo5lI2LvJwy2IPLuvM\nRMgRFfjEkWX5NKF/JtE/MnrPSEa68JjN5jMK78mi1+tH3Wcj52MtW61Wjh8/Tk9PD/F4HACz2UxV\nVVVW2FdWVmIep8O1IP8RdUBxI8q/+AjGwmw+/nmSapxl9V+hqXRiI8JPJULETwOFLuIBDvRF+M8X\nO7DpVK6TDtLb2Z7dNlIAnSq6jUYjBhPEHfsZtu5DQo9HWYlDuwJNsqBIepLokNET1/QkND1xTSKq\n6ojIEE1qRJIKkaQ6Ykp1orEZdbxjkZubFntwzHAPflGBTw+appFMJk8T9qcux+NxDAbDhAT3RLZP\n1oKeKf/M2ALd3d309PTQ09PD4OBgNp3H48la6quqqvB4PMJaXyCIOqC4EeVfXGiaxmsdD9MxtI11\n8+7jwrlXCZ/4QqEYRDzAcX+M+ze1Iysqdy/zUGo1Eld0RBXtNJE96nci9VtigIXlz1HrPEIo7mZX\n57V0Di0gPabeKIw6CZtRh82kS82N+vQ8NVWUGrm2yZWz4a1FBV7cnKn84/E4vb2pEKYZcT/SWl9Z\nWTlK2AtrfX4i6oDiRpR/cXHU92d2dT/JJRW3c0H5TaJjayFRLCIeoDuU4P5N7fSEx3ZZyISPsqbD\nQVkNqR7s1hEi3Go8AtIzaPRiMVxIbentuK21o0S6cZYPUS0q8OJmMuWfsdZnLPXd3d2nWetHuuHM\nNmu9pmnZPhCxWIxYLEYikcBkMp1Tn4VCQdQBxY0o/+LBF23hhdYvUFlyEVfXfwxJ0uWFiC+uGlkw\nIartJr524zz290WwZAW6jpK0SDfppQkIkApUbRVHfX9mf9+vaAl8gQX666lz3opJb5uR6xAIZopM\nJ1iPx8OFF14IpKz1fX19WUt9S0sLBw4cAFK+/xlRn5nONyRqBlVVicfjWTE+Uphnlk9dF4/HJxSi\n1Gg0YrFYRkUoOtOUr9GJBAJB8ZBQhtna/m0sBicra++a8kh704kQ8Wdgx44d7Ny5k7vuuivXWZlx\n7GY9q+rs53UMnWRgkfdG5jpXs6/3lxwZ/CNtgVe5pPLdNLjW5NWDIhBMFrPZTF1dHXV1dUA6rnEg\nMMoFZ/v27dnBvdxud9YFJ2OtV1X1jGL81OXMNB46nQ6LxZKdMufMCPPM+owAj8fj2T4KmSlzzmg0\nis/nIxqNIsvyuOfLHHciot9isaDTiXpBIBDMDJqmsa3zcSJJPxsaPoPZcH66Z6YR7jQTpJjcaaYL\nX7Q1HZLyKG5LA8uq76DMtjDX2RoX8Sm1uJmJ8k8kEqf51mdEuE6nO6N13GAwjBLdYy2f+nu6LOPJ\nZPI0sX8m8Z/pPzAWI/Os1+vR6XTjTnq9HkmSsstnSjvRaeRxqqurGR4eLjo3IkEK8Q4ofA4PPMue\n3p+ytPKvWFT29lHbhDuNQDACj7WBaxo+y4nga+zt/RkvtH6Buc4ruaTyPdiMnlxnTyCYcUwm02nW\n+mAwmPWpN5lM4wr12SQsM5GAHA7HhNIrijJK1I8n+jNjAaiqOuFpOuxSBoMBs9mMxWLJzieyLNyJ\nBILZy0DkCHt7f06t/XIWem/MdXbOidnzFhAUBZIkMdd1JTX2ZRwc+C2HB/9AZ2gnF5TdzCLvjeh1\nE4svP52omkpSGSaWzH1eBMWFJEm4XC5cLleuszKt6PV6SkpKKCmZ+hjMmqaNEvWKomTFfWZ5IpOi\nKJhMJgYGBojFYqP6GQSDQXp7e4nH4+O6EkGqPM1m82kNgLOJf7PZPKsaaQJBoRGXQ2xtfxib0cOK\n2g/mbWNb1BKCnGDUW7ik8t00uteyp+en7Ot7ihb/Fi6r+itq7Mum7IHSNA1ZjZNQQsSVEHH5lLkS\nIi6HiStDxOUwCSVEQgmjoQES1aWX0OTeQLX9UnRSbkJdCgSCiSNJEnq9Hr3+/J/XiXxOl2U5K/BP\nnY+1HAwGicfjxOPxM341yIyRkBnnQK/Xj1o31rbxls+2TfRDEBQTmqbyeuejxJUhrmn4HCb9zA/o\nNFUIn/gJInzip5ee8Fvs7vkxQ/FOKksuZln1HTjMtaelUzWZhDJMTB4ioYTTYnwoLcRPF+oJJYSi\njR0qU0KH2WDHpC/FondgMpRi1tsxGxyY9aXoTEn2d/2JmBzAanDT6F5Lo3sdNqN3uv8OwSxA+MMK\npvMeyIT1HE/0x+Px7AjFE53O9XWu0+nGbBw4HI5s1CWPx4PL5ZqSxtFsJJFI4PP58Pv9+Hw+AoEA\nbrcbu91OeXk5Xq8Xo9GY62wKpoAD/b9hX99TXF79N8z3XDtuunzwiRcifoIIET/9qJrMMd8LvNX3\nK2Q1Rq3jclRNTovyMHF5iKQaGXd/o86G2VCKWZ8S4SaDHYvejslgT4vz9Dy9bNTZzmjxLysro6+/\nh67QHpr9m+gJv4UEVJdeSqNnPdWlS9GJCDsFixDxgny7BxRFmZToP9OUSCQIBoMMDQ1lj6/T6XA6\nnaOEvcfjwe125437TyaqUkawDw4O4vf7CYfD2TSZ68z0zYDUFx632015eTnl5eWUlZVRXl6O1WrN\n1aUIzoG+4YO8ePzL1DlWsmrOPWfVAELEFwhCxM8cMXmIt/qepju8F5O+FLO+dJQIP02c61PWdL1u\nal8ipz7A4UQfLf4ttAa2EJODWA2eEdZ50TG30Mg3ATfVxOVQ3oVbm2qK/R6AVOShjHV65BQMBrOW\nf0mSTrPaZ8S9yTTzfYs0TSMcDo+Z75EhWA0GQzafI/PtcDjQ6/V4vV5aWloYGBigv78/O40U/KWl\npVlhn5nsdnve+lgXMjE5yJ+aP41RZ+W6xv/AqD9zA0yI+AJCiPjiY7wHWNVkukK7afZtpmd4HxIS\n1falNLk3UFV6ibDOFwjFKuAUNcHunp/Q7N/EAs91LK3666LtD1Ks98BEkGWZQCBwmkgOBAKjQqPa\n7fbTxL3H48FsNp93HlRVZWhoaJQbTGZKJk+6UZrN5lENi8zy2cT2eOUfjUbp7+8fJe79fn+2UWM2\nm7OW+szkdrsL1hUpH1A1lS1tX2EwcpRrG/8dl6XurPsIEV9ACBFffEzkAU5Z51+kNfASMTmIzeil\n0bWWBvdaYZ3Pc4pRwIUTvbza/m0CsTYqbBfQFzlIVekSVs/5p6IcabkY74HzRVEUgsHgKGGdcVlR\nFCWbrqSkZExxP5Z7iqIooxoMmeOOdcyRIt3tduP1erFaredkGZ9M+SeTSQYHB7Pivq+vj8HBwWz0\nIp1Oh9frHSXsy8rKcvKlohDIRKFSFGXUJMvyaesURaE9/gI96kvUshGHctGYaU7dd8GCBSxYsGDG\nr02I+GlAiPjiYzIVuKLKdIV20ezfTO/wW2nr/GU0udcL63yeUmwCrmNoB9s6v4skSayo/Qdq7cto\n9m1mZ/eT2M2VXF3/MUpNlbnO5oxSbPfAdDLSan6q5Xyk1dxqtWY70UajUfx+P4FAYFSn3Yzrzqlu\nMFNh3R/J+Za/qqoEAoFRrjj9/f2jXHpcLtcoH/vy8vJpCb06E2iahizL2Q7ZIztmn7purG2Z5ZG/\nxxLbmWmimDxBXEuPEOsuY+hgw2nbM4O8nTpddNFFLFu2bCr/ogkhRPw0IER88XGuFXg40Uuz/0Va\n/S8RV4ZS1nn3Ohpda7Ea3dOQ08JA0zTCiR4Go80MRpuJJn3McVxBnWNFTsYPKBYBp6gyb/b9nCOD\nf8RjbeTKOf9Eiak8u71v+CCvtn8LgLfV/QsVJYtzldUZp1jugVyS8V8fHBw8zcqeEfQjBbvb7Z6x\nKDHTUf6Z6z3Vz35kB2KbzUZ5eTkWiyW7TpKk7NeEseYjvzScLe1EjzGyo/R4IvzUCEqTJRMZyWg0\njppnprHEdWY623a9Xo8shdk99E1Megeryj6OyWg9bb/xvtIId5oCQoj44uN8H+CUdX5n2jq/Hwkd\nNfbLaHJvoLL04qK3zsflEIPRZnxp0e6LtpBQhgEw6CyY9CVEkoOY9KU0uK6myb0Bu7lqxvJXDAIu\nkhxka/t3GIweY4HnOi6tfB963ekCKRTv5eUTX2c42cvl1X9Lo3ttDnI78xTDPSAYn5ks/3g8fpqf\nfTKZzH6BmMh8pJwb+Xuy8wySJI0S1acK7anYNp39BFRNZvPxLxOItXFd43/gME9cHIMQ8QWFEPHF\nx1Q+wKF4Ly3+zbQGXiKuhLAZy2hyr6PBtRarsbBH54RUgyYQaxsl2sOJXgAkJBzmOXitTXhsTXit\nTTjMtUhI9A0f4Jj/BTqHdqGhUFlyEfM911BjXzbtnS0LXcB1h/byeuejqJrMFTUfoN658ozpE8ow\nr7U/TM/wPhZ5N3JJ5XsKviFa6PeA4MwUa/mPlIX5HGVnb8/PODT4e1bV3s1c15WT3l+I+AJCiPji\nYzoeYEVN0pm2zvcNH0hb55fR5FlPVcnFSAUgijRNYzjZn3KLiRxjMNpMINaGqqU+tVoMLrzW+Xit\nTXhtTbgtDRj1ljMeM5oM0OJ/kWb/ZqKyLz341rppDe9ZqC9wVVPY3/crDgz8Bqe5jrfVfRi7uXrC\n++7u+QnHfM9TU7qUVXPuOWuYtnymUO8BwcQQ5Z+/dIZ28cqJb9Dk3sDymr89p2MIEV9ACBFffEz3\nAxyKd9Psf5HjgZeJKyFKjGU0utfT4FqTV9b5hDKML9rCYKQ5a2mPKyEA9JIJj7UBj7UpK9qtBs85\nW3dUTaE7tJdm/wt0h1PhPWvsl9Hk2TDljaBCfIFHkwFe73iEvshBGlxrWVb9/zCcQ3+Do74/s7v7\nRzjMNVxd/7FRPvSFRCHeA4KJI8o/PxlODPBcy2ewGcu4tuFz59ynSoj4AkKI+OJjph7glHV+R9o6\nfxAJPbWOZcx1rsasd2DQmTHoLNm5XmfOmRuDqskEYh34osfSlvZmQonu9FYJh7km5RaTFu1Oy5xp\nc3sJJ/po9m+m1b+FuBKi1FRBk3sDDa41UzJIUaG9wPuGD/Jax8MklSiX19xJg+vq8zpeT3gfW9u/\ng07S87b6j1BuWzhFOZ09FNo9IJgcovzzD0WV2XT8C4Ti3VzX+AXs5nOPqCVEfAEhRHzxkYsHOGWd\n30xr4GUSSnjcdHrJiF5nPkXgmzFIJ4X+qPWnpU2lMerM6KWT23SSIWsl1zSNSHIwGy3GF23GH21F\n0VLh4Mx6B960D7vH2oTH2piTWOKKmqQjtINm3wv0Rw6jkwzUOVbQ5N5AmW3hOVv9C+UFrmkqBwd+\nx1t9v6TUVMWVdR+e0EAnE2Eo3sXLJ75OJDnIFTV/zzzXVVNy3NlCodwDgnNDlH/+sav7xxz1/Ykr\n6/6ZOscV53UsIeILCCHii49cVuCKmsAfO4GsRpHVeHZS1DiyGkv/jiFro7cl1Vg6TTybRkM9+wnT\nSOiyQl/V5BFuMUZclnlZ0e61NmEzls26Tk/BWAfH/JtoC7xCUo3iNM+hyXMN85xvm7TvdiG8wONy\niDc6H6U7/Cb1jlUsr/m7Kfdhj8thtnZ8i77hg1xQdhNLKt5VEH07oDDuAcG5I8o/v2gf2s7W9m+x\nwHMDy6rvOO/jCRFfQAgRX3wUQgWuaRqqJmcFvaLFSWYbA6c0CE5pLGhouC1z8drm47LUoZMMub6c\nCSOrMdqCr9PsewF/7DgGnZl655XMd2/AbZ03oWPke/kPRI7xWsd3iMlBLqv6a5rc10xbo0vVZHZ2\nP0mL/0Vq7ctZNecuDLozd1bOB/L9HhCcH6L884dQvJfnWz6L3VzNhnmfRa87//dVPoj4/HkrCwSC\nSSNJUtr1xoiZ0lxnZ8Yw6Cw0udfR5F7HYLSFZt8LtAVepcW/GY+1ifmea6hzrDynTp2zHU3TOOL7\nE3t7fobN6Oaahs/isTZO6zl1koHl1X+H0zyHPT0/4YXW/+Tq+o9iM3qn9bwCgUCgqAm2dnwbSdJx\n5ZwPT4mAzxeK50oFAkFR4rU24q1tZGnVX3E88ArHfJvY1vk4e3p+wrz0IFKOCYZYnO0klAjbO79L\nR2gHtfZlrKj9B0z6mRnCXZIkFnpvwG6qYmvHd3i+5fNcVfdRvLamGTm/QCAoTnb3/IRArC0dKass\n19mZUYSIFwgERYFJX8JC7w0s8FxPf+QQx3wvcHTweY4M/pGKkguZ776GWseyvHIbGok/epytHd9m\nODHApZXvY5H37Tnps1Btv5RrGz7Pyye+zubjX2RF7Qepd66e8XwIBILC53jgVZr9m1hc9g5q7Jfl\nOjszTn6+rQQCgeAckSSJipILqCi5gGgyQGvgJZr9m9ja8W0sBieNrtQgUmXkh0VH0zRa/JvZ1fNj\nzPpS1jd8OufhHp2WOVzbeD+vtn+T1zoeYSjezUXlt826jtACgeB0VE2hLbgVo85KjX3prDVsDMU7\n2dn9BOW2RSypeFeus5MTRMfWCSI6thYfolNT8aBqKj3hNznme4Hu8F4koN5zBV7TYspLFuE0z5mV\nEVeSSoyd3T+kLfgqVSVLWDnnH7EYHLnOVhZFTbKj+wmOB16mzrGSFbX/kBf9EOJyiMHoMRbUXsHw\nkJzr7AhyRDG+AwKxE2zr/B7+WCuQCiXc4LqKBve6WeV2KKsxnm+5n7g8xPVN/zktI3eLjq15zo4d\nO9i5cyd33XVXrrMiEAimEZ2ko8a+lBr7UoYTAzT7N9Meep023zYg5YpTZltEhW0x5SWLcVnqp20Q\nq4kSjHWyteNbDMW7ubj8L7mg/OacDQI2HnqdkRU1H8RprmVv788ZTvZzVd1HsBrduc7aaQwn+ukM\n7aJzaCf9kUNoaLzabmCO4wqa3Bsoty0SXxIEBYuiyhwc+A0HB36DUWdj9ZwPYdCZafFv4fDgHzk0\n+AfKbAtpdK+jznFFzqNP7ex+kqF4F2vnfmJaBHy+ICzxE0RY4ouPYrTCCE5SVlZGW9dB+iKH6B8+\nRH/kEOFEH5CKflNuW0i57QLKSxbhsTbM6Cfn44FX2dH1Aww6C6vn3ENl6UUzdu5zpXNoF693PoJR\nZ+Oq+o/hmWCoz+lC0zQCsRN0hnbSGdpJIHYCAKd5DrX2yykvWYRfPszB7udJqhEc5hqa3BuY57pq\nxjoLC3JLsbwDfNHjbOt8nGC8nXrnai6rumPUF71oMsDx4Cu0+LcQTvRg0FmY67ySBvdaPJaGGW/c\ntvhfYnvXd7mo/FYurvjLaTtPPljihYifIELEFx/FUoELxmas8o8kffRHDmdF/VA8VS/oJRNltgWU\npy31Xmsj+mlwG1HUBLt6fkyLfzPltkWsnvOhWWnVHo9A7AQvn/g6cTnEyjn/eN4jKk4WVVMYiByh\nYygl3CPJAUCizLaAWvvl1NovHzVMe1lZGT19nbQH3+CYfxO+aDN6yUidcyVN7mvwWpsK0jofTfrp\nGz6I29owq1woZppCfwcoapL9/c9waOB3mA12llf/LbWOy8dNr2ka/ZHDtPq30D60DUVL4DTX0ehe\ny1zn2zAbpj+McSB2gj+33I/XtoC1cz85rV8fhYgvIISILz4KvQIXnJmJlH9MDtIfOUL/8EH6hg8R\njHcAGjrJiNfamBX1Zbb55/35ORTvZWvHtwnE2lhc9g6WVLwr5y4950JMDvLKiYcYjB5jScW7uKDs\n5mkVwrIapye8j87QTrpCe0goYXSSkaqSi6h1XE6N/TIsBueY+556D/ijbTT7N9EW3IqsxnCa65jv\n2cDccxgReLYxFO+ic2gnHaGd+KLN2fUVJRfQ5N5ArX15UcXfhsJ+BwxGjrGt67sMxbuY57qKy6ru\nmNQXpoQS4UTwNVr8W/DHWtFJRubYL6fRvY6KkgumpQ9RUonyfMvnSKpRbmj64rjP7VQhRHwBIUR8\n8VHIFbjg7JxL+cflMAORI1kXnEDsOBoaEno81gbKSxZTbltEuW3RpERf+9B2tnd+F0nSsbL2rrwP\npaaoCbZ3fZ+24FbmOq/kipq/n9IvF3E5RFdoN52hnfSE30LREhh1Nmrsl1HruJyqkiUY9WdvVI13\nDySVKCeCr3HMv4lArC09IvBqmtwb8Fgbpuw6phNNUxmMtqTciYZ2Ekp0A+CxNlJrv5zKkgvpHT5A\ni38zw8kBzHo7Da41NHnWU2qqPMvRC4NCfAfIaoK3+p7myOCzWAxurqj5O6rtl57XMf2xNlr9W2gL\nbiWhDFNiLKfBvYYG15op81fXNI3XOx6hfegN1s37FBUlF0zJcc+EEPEFhBDxxUchVuCCiTMV5Z9U\nogxEjtIfSVnqfdFWNBQkJFyWeZSXpDrLltkWjfkpWlFl3uz9GUd8f8JjbeTKOf9Eian8vPI0W9A0\njYMDv2Vf31N4rfO5qv4j52VZG6tjqs3opda+LOvjPtl+C2e7BzRNwxdrpdm3iRPB11C0BG5LA02e\nDcx1rsp5579TUdQkfcMHs/0AYnIQCT0VJRdQ67icWvuy00SXpqn0hN+i2b+JrtBuNFQqSy5KWefz\neFyFiVBo74D+4cNs6/oe4UQPje51XFr5Pkx625QdX1ETdIR20uJ/kb7hA0hIVJVeQqN7LTX2y87r\nXjnm+zM7u59kScW7ubD85inL85kQIr6AECK++Ci0ClwwOaaj/GU1zmDkGP2RQ/QNH2Iw2oyqJQFw\nmusoL1lMhW0R5SWLUdQkr3U8nAp16LmOSyv/qiDdGdqHtvNGx6OYDXaurv8YLkv9hPbTNI1A/ASd\nQzvpDO0iEGsDUv9jSpBejtsy97xcdSZzDySUCG3pgWeC8Q6MOitznVfS5Nkw4WuaDhJKhJ7wXjqG\ndtId3ousxjDoLFSXXkKt/XKq7ZdO2I0ikvQUIzN0AAAWoklEQVTR6n+JlsCLRJKDWAxOGlxraXKv\nK5jG5UgK5R0gqzHe7H2Ko77nKTF6WV7z91SVXjyt5wwn+mj1b6E18DJR2Y9Z72Ce6yoa3WtxmCcu\nUgF80VZeaP0PKksu5Or6j89YuF8h4gsIIeKLj0KpwAXnxkyUv6Im8EVb6YscpH/4MAORIyhaAgCd\nZEAnGVhR8wHqnCunNR+5xhc9zisnvk5SjbCq9h5qHcvGTDeyY2pXaCfD6Y6p5baFqY6pjmVT6upx\nLveApmkMRI/S7NtE+9A2VC2J1zqfJs811DlWzEic/GjSn/oqEdpJ3/ABVE3BrHdQ61iWdZU5H/el\nzLgKzf5NdIf2oAFVpUtocq9PW1zzr6/GWBTCO6B3+ADbO7/HcLKf+Z7ruKTi9gm5kk0VqqbQE95H\ni39L+kuOkgpV6VpLnXPFWb9WJZRhnmv+LBoq1zd+AbPBPkM5FyK+oBAivvgohApccO7kovxVTcYX\nPU5/5BDDiX4WeW/EXiTRQaJJPy+f+Ab+2HEurXwPi7wbkSRp/I6ppRdTa7+cGvvSaevgdr73QFwO\ncTzwCs3+TYQSPZj0JcxzXkWTZ8OkrZFnY6yOqaWmCmrty6l1XI7XOn9aInlEkoO0+F+kxb+FqOzH\nanDT4F5Lo2stJab8GPV4PPL5HZBUouzt/RnN/k2Umiq5ouYDVJQszmmeYnKQ44FXaPG/SCgdqrLe\nuZpG11o81sbTvpppmsar7d+kK7SHDQ2fpsy2YEbzK0R8ASFEfPGRzxW44PwR5T/zyGqcbZ3fpX3o\nDeocK7JWvJEdU+c4Lqdygh1Tz5epugdSofkOccz3Ap2hHaiaQrltMfM9magvxnM45tgdU92WBuak\n3Ykc5toZC4GpagrdoT0p63x4HxJQVXopTZ71VJcunXUDkU2EfK0DusNvsqPrB0SSPhZ6b2RJxV9i\n0Jlzna0smqYxEDlCS2AL7cE3TglVeWXW2n544Fn29P6UpZV/xaKyt894PoWILyCEiC8+8rUCF0wN\novxzg6Zp7O//Nfv7f33eHVPPl+m4B2JykFb/yzT7NzGc7J9U1BdFlemLHMj2A4jJgXTH1MVZdyKb\n0Tul+T0XhhP9Ket8YAsxOYjN6KXRtZYG99q8Gl0z3+qAhDLMnp6f0hp4CbuphhW1H6TMNj/X2Toj\nmUhPLYEt+KIt6CQDtfbLqSi5gF3dP6LGvpS31f1LTsZjECK+gBAivvjItwpcMLWI8s8tSSWKQWfJ\n6WBK03kPaJpK7/B+jvk20RXalY76cjFNng3UjojkkVSidIf30pnumJpUoxh0ZqrSHVNr7Etn7Qiy\nqibTGdpNs28TvcNvIaGjxr6UJvcGKkuXzHrrfD7VAV2h3ezoeoKYHGRx2UYuKr9tWgacm04CsRO0\nZENVhikxlnN90xdydn8LEV9ACBFffORTBS6YekT5C2bqHogm/bT4t4yK+lLvWMVQopu+4f0nO6ba\nl1HrOP+OqbkgnOil2f8irf4txJUQNmMZTe51NLjWYjW6cp29McmHOiAuh9jd82PagltxmutYUfsB\nPNbGXGfrvFDUBN3hN3FZ6nI6JoEQ8QWEEPHFRz5U4ILpQ5S/YKbvgVTUl70c822iO7yXUlP5tHdM\nnWkUVaYztJNm/6Z0LHE9tY5lNLnXU1ly0bSFD1TUJAklTFwJEVfCJOT0XAkTzy6n5nE5TFIdxm6p\nwGGsw2NtxGNtwGmuO6f+C9NFx9B2dnY/SVwOc2H5zVxQdnNBhqHNFfkg4kVpCwQCgUAwC9BJOmrs\nl1FjvwxZTaCXjDl1J5oO9DoD9c6V1DtXEop3p6zzgZfoGNpOibGCJvd6GtxXnzHikKzGTxffckqg\np4R6mIScmmfWyWps3OMZdBbM+lJMejtmQymlpkpMOhsJAnQO7aI18BIAOkmP01yPx9qA29qQFva1\nM95XIyYH2dX9P7QPbcNlmcuauZ/AbZk7o3kQzA6EJX6CCEt88SEsscWNKH+BuAdmBkVN0DG0g2b/\nJvojh9FJemrsy7DoHSet5WnRnlDC2bEUxsKos2HSl2I2lJ4U5vpSzAZ7ar3ejsmQmqe2l45rXS8r\nK6O/v59IcgBftBVfrBVftAV/9DhJNQKAXjLisszNinqPpQG7uWZavppomsaJodfZ1f0/yGqMi8pv\nY3HZxoIeNTeXCEu8QCAQCAQCwRnQ60zMdV3JXNeVDMU7afZtpi24FQ0tLbxLsRncuCz1KUGuTwvy\nrDDPLJdMuaCVJIkSUzklpnLqnCuAVKfkcKIvLepb8UdbOB54iWO+5wEw6My4LfNSwt6SEvelpsrz\nchWKJgPs7H6CztAuPNZGVtR8EKdlzpRcoyB/ESJeIBAIBALBrMBhruWy6ju4rPqOXGdlXCRJh91c\nhd1cxVznaiDVnyGc6GYwLep90VaafS9wREsCYNRZcVvnpUV9I25rAyXG8rO6S2maxvHgK+zu/jGq\nluTSyvey0Pv2gugfITh/hIgXCAQCgUAgOA90kg6HuRaHuZYG11VAagCsoXhnyhUn2oo/1soR33Oo\nmgyASV+Kx3LSv95jbcRqcGeFfSQ5yI6uH9AdfpMy20JW1HygaEZwFkwMIeIFAoFAIBAIphidpMdl\nqcdlqafRvRZIRckJxjvSbjgpP/tDA79DQwXAYnDitqTcb1oDW9A0lcuq3s8Cz7XTFrlHkL8IES8Q\nCAQCgUAwA+h1xrTVvSG7TlYTBGMn0p1nU644PeG9lJdcwBU1f0+pqSKHORbMZoSIFwgEAoFAIMgR\nBp0Jr20+Xtv87DpVk0XUGcFZEd9mBAKBQCAQCGYRQsALJoIQ8QKBQCAQCAQCQZ4hRLxAIBAIBAKB\nQJBnCBEvEAgEAoFAIBDkGUXrdLVt2zZ27dpFNBplw4YNXHrppbnOkkAgEAgEAoFAMCFmTMQPDw/z\n6KOP0t7ejiRJ3H333SxcuHDSx3nkkUfYtWsXTqeTBx98cNS2PXv28MQTT6CqKtdccw233nrruMdZ\nsWIFK1asIBwO86Mf/UiIeIFAIBAIBAJB3jBjIv6JJ55g6dKlfPzjH0eWZeLx+KjtwWAQk8mE1WrN\nruvp6aGqqmpUunXr1nHjjTfy8MMPj1qvqirf//73+cxnPoPX6+VTn/oUy5cvR1VVfvrTn45Ke/fd\nd+N0OgH41a9+xQ033DCVlyoQCAQCgUAgEEwrMyLiI5EIBw8e5EMf+lDqpAYDBsPoUx84cIDnn3+e\nT33qUxiNRv785z+zbds27rvvvlHpLrzwQvr6+k47x7Fjx6iqqqKyshKAK6+8ku3bt3Pbbbdx7733\nnpZe0zR+8pOfsHTpUhobG6fqUgUCgUAgEAgEgmlnRkR8X18fDoeDRx55hLa2NhobG7nzzjuxWCzZ\nNKtXr6avr49vfOMbrF69ms2bN/PZz352wufw+Xx4vd7sb6/Xy9GjR8dN/+yzz7Jv3z4ikQg9PT1c\nf/31p6XZsWMHO3fu5K677ppwPgQCgUAgEAgEgulmRkS8oii0trbyd3/3dyxYsIAnnniCZ555hve+\n972j0t1yyy089NBDfO973+Pb3/72KJE/1WzcuJGNGzeeMc3y5ctZvnz5tOVBIBAIBAKBQCA4F2Yk\nxKTX68Xr9bJgwQIAVq1aRWtr62npDh48SHt7O1dccQVPPfXUpM7h8XgYHBzM/h4cHMTj8ZxfxgUC\ngUAgEAgEglnIjIh4l8uF1+ulq6sLgH379jFnzpxRaVpbW3n88cf5xCc+wT333EMoFOJnP/vZhM/R\n1NREd3c3fX19yLLM1q1bhRVdIBAIBAKBQFCQSJqmaTNxouPHj/Poo48iyzIVFRXcc889lJaWZrcf\nOnQIm81GfX09ALIs8+KLL3LttdeOOs5DDz3EgQMHCIVCOJ1Obr/9djZs2ADArl27ePLJJ1FVlfXr\n1/POd75zJi5NIBAIBAKBQCCYUWZMxAsE+ca9997LAw88kOtsCHKEKH+BuAeKG1H+xU0+lP+MuNMI\nBAKBQCAQCASCqUOIeIFAIBAIBAKBIM8QIl4gGIdT+2MIigtR/gJxDxQ3ovyLm3wof+ETLxAIBAKB\nQCAQ5BnCEi8QCAQCgUAgEOQZMzJiq0Aw2xkYGODhhx8mEAggSRLXXnstGzduJBwO841vfIP+/n7K\ny8v56Ec/Oio0qqCwUFWVe++9F4/Hw7333ktfXx8PPfQQoVCIxsZGPvzhD2MwiGqzEBkeHubRRx+l\nvb0dSZK4++67qampEc9/kfC73/2OTZs2IUkSdXV13HPPPQQCAfH8FzCPPPIIu3btwul08uCDDwKM\n+87XNI0nnniC3bt3Yzabueeee2hsbMzxFYD+/vvvvz/XmRAIck08HmfhwoW8733vY82aNTz22GMs\nWbKEP/7xj9TV1fHRj34Uv9/Pm2++ySWXXJLr7Aqmid///vfIsowsy1x11VU89thjrF+/nrvuuot9\n+/bh9/tpamrKdTYF08Djjz/OkiVLuOeee7j22mux2Ww888wz4vkvAnw+H48//jhf+9rX2LhxI1u3\nbkWWZf70pz+J57+AKSkpYf369Wzfvp0bbrgBgF/84hdjPvO7d+9mz549fOlLX6KhoYEf/OAHXHPN\nNTm+AuFOIxAA4Ha7s61qq9VKbW0tPp+P7du3s3btWgDWrl3L9u3bc5lNwTQyODjIrl27shWzpmns\n37+fVatWAbBu3TpR/gVKJBLh4MGD2YEDDQYDJSUl4vkvIlRVJZFIoCgKiUQCl8slnv8C58ILLzzt\ny9p4z/yOHTtYs2YNkiSxcOFChoeH8fv9M57nUxHfhQSCU+jr66O1tZX58+cTDAZxu90AuFwugsFg\njnMnmC5++MMfcscddxCNRgEIhULYbDb0ej0AHo8Hn8+XyywKpom+vj4cDgePPPIIbW1tNDY2cued\nd4rnv0jweDzcdNNN3H333ZhMJi699FIaGxvF81+EjPfM+3w+ysrKsum8Xi8+ny+bNlcIS7xAMIJY\nLMaDDz7InXfeic1mG7VNkiQkScpRzgTTyc6dO3E6nbPCx1Ew8yiKQmtrK9dffz1f/epXMZvNPPPM\nM6PSiOe/cAmHw2zfvp2HH36Yxx57jFgsxp49e3KdLUGOyYdnXljiBYI0sizz4IMPcvXVV7Ny5UoA\nnE4nfr8ft9uN3+/H4XDkOJeC6eDw4cPs2LGD3bt3k0gkiEaj/PCHPyQSiaAoCnq9Hp/Ph8fjyXVW\nBdOA1+vF6/WyYMECAFatWsUzzzwjnv8iYd++fVRUVGTLd+XK/9/evYZEsYdhAH/aXdcsxctu3pMp\nsguWUOx2TCuC7UtqFFKbFYSwQal0IRPrix8qKlHQjIUV0bIPRUIgGEaQeKm0Ik0jzTJDu5mxu15W\nKN1153yI5tQ5x4NQnm30+YEw7sz85x3WP/swvjvzB168eMH5PwtNNueDgoJgtVql7Ww222/x98Ar\n8UT42v9ssVgQERGB5ORk6XWdToeGhgYAQENDA/R6vadKpGm0Z88eWCwWmM1mHD16FCtXrsThw4cR\nExODBw8eAADq6+uh0+k8XClNh4CAAGg0Gnz48AHA11AXGRnJ+T9LaLVadHd3Y2xsDKIoSu8/5//s\nM9mc1+l0aGxshCiKePnyJebNm+fxVhqAD3siAgB0dXUhNzcXUVFR0r/Pdu/ejejoaBQWFsJqtfIW\nc7NER0cHqqurceLECQwMDKCoqAijo6NYtGgRDh06BC8vL0+XSNOgt7cXFosFLpcLwcHByMjIgCiK\nnP+zRGVlJZqamqBUKiEIAg4ePAi73c75P4MVFRWhs7MTDocD/v7+MBqN0Ov1/zrnRVFEWVkZ2tvb\noVarkZGR8VvcqYghnoiIiIhIZthOQ0REREQkMwzxREREREQywxBPRERERCQzDPFERERERDLDEE9E\nREREJDMM8URE9J+MRiM+fvzo6TL+obKyEsXFxZ4ug4jII/jEViIiGcnMzMTQ0BAUir+uwWzatAkm\nk8mDVRER0f+NIZ6ISGZycnIQGxvr6TJmlImJCSiVSk+XQUQ0ZQzxREQzRH19PWprayEIAhobGxEY\nGAiTyYRVq1YBAOx2O0pLS9HV1QVfX19s27YNmzdvBgC43W5UVVWhrq4Ow8PDCAsLQ3Z2NrRaLQDg\n6dOnOHv2LEZGRrB+/XqYTCbp6cbfq6ysxLt376BWq/Ho0SNotVpkZmZKTzc0Go0oLi5GaGgoAMBs\nNkOj0SA1NRUdHR24ePEitmzZgurqaigUCuzfvx8qlQoVFRUYGRnB1q1bkZKSIh3P6XSisLAQT548\nQVhYGNLT0yEIgnS+5eXleP78OebOnYukpCQkJiZKdb59+xZeXl5oaWnBvn37YDAYpueNISKaBuyJ\nJyKaQbq7uxESEoKysjIYjUYUFBRgdHQUAHDhwgVoNBqUlJQgKysL165dw7NnzwAAN2/exP3793Hy\n5ElUVFQgPT0d3t7e0ritra04d+4cCgoK0NzcjPb29klraGlpQXx8PC5fvgydTofy8vIp1z80NASn\n0wmLxQKj0YiSkhLcvXsX58+fx6lTp3Djxg18+vRJ2v7x48dYt24dysvLkZCQgPz8fLhcLrjdbuTl\n5UEQBJSUlCA3Nxc1NTVoa2v7Yd+4uDhcunQJGzZsmHKNRES/A4Z4IiKZyc/PR1pamvRz584daZ2/\nvz+SkpKgUqkQHx+P8PBwtLa2wmq1oqurC3v37oVarYYgCDAYDGhoaAAA1NbWIjU1FeHh4ZgzZw4E\nQYCfn5807vbt2zF//nxotVrExMSgt7d30vqWL1+ONWvWQKFQYOPGjf+57d8plUqkpKRApVIhISEB\nDocDiYmJ8PHxwcKFCxEZGfnDeIsXL0ZcXBxUKhWSk5PhdDrR3d2Nnp4ejIyMYMeOHVCpVAgJCYHB\nYEBTU5O079KlS7F27VooFAqo1eop10hE9DtgOw0RkcxkZ2dP2hMfFBT0Q5vLggULYLfbMTg4CF9f\nX/j4+EjrtFotenp6AAA2mw0hISGTHjMgIEBa9vb2xpcvXybd1t/fX1pWq9VwOp1T7jn38/OTvrT7\nLVj/fbzvj63RaKRlhUIBjUaDwcFBAMDg4CDS0tKk9W63GytWrPjXfYmI5IYhnohoBrHb7RBFUQry\nVqsVOp0OgYGBGB0dxefPn6Ugb7VaERQUBOBroB0YGEBUVNS01uft7Y2xsTHp96GhoZ8K0zabTVp2\nu92w2WwIDAyEUqlEcHAwb0FJRDMW22mIiGaQ4eFh3Lp1Cy6XC83NzXj//j1Wr14NrVaLZcuW4erV\nqxgfH0dfXx/q6uqkXnCDwYDr16+jv78foiiir68PDofjl9cnCALu3bsHt9uNtrY2dHZ2/tR4r1+/\nxsOHDzExMYGamhp4eXkhOjoaS5YsgY+PD6qqqjA+Pg632403b97g1atXv+hMiIg8i1fiiYhkJi8v\n74f7xMfGxiI7OxsAEB0djf7+fphMJgQEBODYsWNSb/uRI0dQWlqKAwcOwNfXFzt37pTacr71k585\ncwYOhwMRERE4fvz4L689LS0NZrMZt2/fhl6vh16v/6nxdDodmpqaYDabERoaiqysLKhUXz/acnJy\ncOXKFWRmZsLlciE8PBy7du36FadBRORxc0RRFD1dBBER/bxvt5g8ffq0p0shIqJpxnYaIiIiIiKZ\nYYgnIiIiIpIZttMQEREREckMr8QTEREREckMQzwRERERkcwwxBMRERERyQxDPBERERGRzDDEExER\nERHJDEM8EREREZHM/Al4ZxDqWa1dNQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(12, 12))\n",
- "ax1 = fig.add_subplot(2, 1, 1)\n",
- "ax2 = fig.add_subplot(2, 1, 2)\n",
- "for weight_penalty, run in run_info.items():\n",
- " stats, keys, run_time = run\n",
- " ax1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys['error(train)']], label=str(weight_penalty))\n",
- " ax2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys['error(valid)']], label=str(weight_penalty))\n",
- "ax1.plot(np.arange(1, aug_stats.shape[0]) * stats_interval, \n",
- " aug_stats[1:, aug_keys['error(train)']], label='Data augmentation')\n",
- "ax2.plot(np.arange(1, aug_stats.shape[0]) * stats_interval, \n",
- " aug_stats[1:, aug_keys['error(valid)']], label='Data augmentation')\n",
- "ax1.legend(loc=0)\n",
- "ax1.set_xlabel('Epoch number')\n",
- "ax1.set_ylabel('Training set error')\n",
- "ax1.set_yscale('log')\n",
- "ax2.legend(loc=0)\n",
- "ax2.set_xlabel('Epoch number')\n",
- "ax2.set_ylabel('Validation set error')\n",
- "ax2.set_yscale('log')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
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diff --git a/notebooks/06_Dropout_and_maxout.ipynb b/notebooks/06_Dropout_and_maxout.ipynb
deleted file mode 100644
index 05c2a1f..0000000
--- a/notebooks/06_Dropout_and_maxout.ipynb
+++ /dev/null
@@ -1,3774 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dropout and maxout\n",
- "In this lab we will explore the methods of [dropout](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf), a regularisation method which stochastically drops out activations from the model during training, and [maxout](http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf), another non-linear transformation that can be used in multiple layer models. This is based on material covered in the [fifth lecture slides](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp05-hid.pdf)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 1: Implementing a dropout layer\n",
- "\n",
- "During training the forward propagation through a dropout layer produces outputs where a subset of the input dimensions are set to zero ('dropped out'). The dimensions to be dropped out are randomly sampled for each new batch, with each dimension having a probability $p$ of being included and the inclusion (or not) of each dimension independent of all the others. If the inputs to a dropout layer are $D$ dimensional vectors then we can represent the dropout operation by an elementwise multiplication by a $D$ dimensional *binary mask* vector $\\boldsymbol{m} = \\left[m_1 ~ m_2 ~\\dots~ m_D\\right]^{\\rm T}$ where $m_d \\sim \\text{Bernoulli}(p) ~~\\forall d \\in \\lbrace 1 \\dots D\\rbrace$. \n",
- "\n",
- "As a first step implement a `random_binary_mask` function in the cell below to generate a binary mask array of a specified shape, where each value in the outputted array is either a one with probablity `prob_1` or zero with probability `1 - prob_1` and all values are sampled independently."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "def random_binary_mask(prob_1, shape, rng):\n",
- " \"\"\"Generates a random binary mask array of a given shape.\n",
- " \n",
- " Each value in the outputted array should be an indepedently sampled\n",
- " binary value i.e in {0, 1} with the probability of each value\n",
- " being 1 being equal to `prob_1`.\n",
- " \n",
- " Args:\n",
- " prob_1: Scalar value in [0, 1] specifying probability each\n",
- " entry in output array is equal to one.\n",
- " shape: Shape of returned mask array.\n",
- " rng (RandomState): Seeded random number generator object.\n",
- " \n",
- " Returns:\n",
- " Random binary mask array of specified shape.\n",
- " \"\"\"\n",
- " return rng.uniform(size=shape) < prob_1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Test your `random_binary_mask` function using the cell below (if your implementation is incorrect you will get an `AssertionError` - look at what the assert statement is checking for a clue as to what is wrong)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "test_shapes = [(1, 1000), (10, 10, 10)]\n",
- "test_probs = [0.1, 0.5, 0.7]\n",
- "for i in range(10):\n",
- " for shape in test_shapes:\n",
- " for prob in test_probs:\n",
- " output = random_binary_mask(prob, shape, np.random)\n",
- " # Check generating correct shape output\n",
- " assert output.shape == shape\n",
- " # Check all outputs are binary values\n",
- " assert np.all((output == 1.) | (output == 0.))\n",
- " # Check proportion equal to one plausible\n",
- " # This will be noisy so there is a chance this will error\n",
- " # even for a correct implementation\n",
- " assert np.abs(output.mean() - prob) < 0.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Given a randomly sampled binary mask $\\boldsymbol{m}$, the outputs $\\lbrace \\boldsymbol{y}^{(b)} \\rbrace_{b=1}^B$ of the stochastic forward propagation through a dropout layer given a batch of inputs $\\lbrace \\boldsymbol{x}^{(b)} \\rbrace_{b=1}^B$ can be calculated by simply performing an elementwise multiplication of the inputs with the mask\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_d = m_k x^{(b)}_d \\qquad \\forall d \\in \\lbrace 1 \\dots D \\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "The corresponding partial derivatives required for implementing back-propagation through a dropout layer are\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = \n",
- " \\begin{cases}\n",
- " m_k & \\quad k = d \\\\\n",
- " 0 & \\quad k \\neq d\n",
- " \\end{cases}\n",
- " \\qquad \\forall k,\\,d \\in \\lbrace 1 \\dots D \\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "As discussed in the lecture slides, when using a model trained with dropout at test time dimensions are no longer stochastically dropped out and instead all activations are deterministically fed forward through the model. So that the expected (mean) outputs of each layer are the same at test and training we scale the forward propagated inputs during testing by $p$ the probability of each dimension being included in the output. If we denote the deterministically forward-propagated batch of outputs of a dropout layer at test time as $\\lbrace \\boldsymbol{z}^{(b)} \\rbrace_{b=1}^B$ then we have\n",
- "\n",
- "\\begin{equation}\n",
- " z^{(b)}_d =\n",
- " \\mathbb{E}\\left[ y^{(b)}_d \\right] = \n",
- " \\sum_{m_d \\in \\lbrace 0,1 \\rbrace} \\left( \\mathbb{P}\\left[\\mathrm{m}_d = m_d\\right] m_d x^{(b)}_d \\right) =\n",
- " (p) (1) x^{(b)}_d + (1-p) (0) x^{(b)}_d =\n",
- " p x^{(b)}_d \\qquad \\forall d \\in \\lbrace 1 \\dots D \\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "To allow switching between this stochastic training time behaviour and deterministic test time behaviour, a new abstract `StochasticLayer` class has been defined in the `mlp.layers` module. This acts similarly to the layer objects we have already encountered other than adding an extra boolean argument `stochastic` to the `fprop` method interface. When `stochastic = True` (the default) a stochastic forward propagation should be caculated, for dropout this corresponding to $\\boldsymbol{x}^{(b)} \\to \\boldsymbol{y}^{(b)}$ above. When `stochastic = False` a deterministic forward-propagation corresponding to the expected output of the stochastic forward-propagation should be calculated, for dropout this corresponding to $\\boldsymbol{x}^{(b)} \\to \\boldsymbol{z}^{(b)}$ above.\n",
- "\n",
- "Using the skeleton `DropoutLayer` class definition below, implement the `fprop` and `bprop` methods. You may wish to store the binary mask used in the forward propagation as an attribute of the class for use in back-propagation - it is fine to assume that the `fprop` and `bprop` will always be called in sync."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "from mlp.layers import StochasticLayer\n",
- "\n",
- "class DropoutLayer(StochasticLayer):\n",
- " \"\"\"Layer which stochastically drops input dimensions in its output.\"\"\"\n",
- " \n",
- " def __init__(self, rng=None, incl_prob=0.5, share_across_batch=True):\n",
- " \"\"\"Construct a new dropout layer.\n",
- " \n",
- " Args:\n",
- " rng (RandomState): Seeded random number generator.\n",
- " incl_prob: Scalar value in (0, 1] specifying the probability of\n",
- " each input dimension being included in the output.\n",
- " share_across_batch: Whether to use same dropout mask across\n",
- " all inputs in a batch or use per input masks.\n",
- " \"\"\"\n",
- " super(DropoutLayer, self).__init__(rng)\n",
- " assert incl_prob > 0. and incl_prob <= 1.\n",
- " self.incl_prob = incl_prob\n",
- " self.share_across_batch = share_across_batch\n",
- " \n",
- " def fprop(self, inputs, stochastic=True):\n",
- " \"\"\"Forward propagates activations through the layer transformation.\n",
- "\n",
- " Args:\n",
- " inputs: Array of layer inputs of shape (batch_size, input_dim).\n",
- " stochastic: Flag allowing different deterministic\n",
- " forward-propagation mode in addition to default stochastic\n",
- " forward-propagation e.g. for use at test time. If False\n",
- " a deterministic forward-propagation transformation\n",
- " corresponding to the expected output of the stochastic\n",
- " forward-propagation is applied.\n",
- "\n",
- " Returns:\n",
- " outputs: Array of layer outputs of shape (batch_size, output_dim).\n",
- " \"\"\"\n",
- " if stochastic:\n",
- " mask_shape = (1,) + inputs.shape[1:] if self.share_across_batch else inputs.shape\n",
- " self._mask = (rng.uniform(size=mask_shape) < self.incl_prob)\n",
- " return inputs * self._mask\n",
- " else:\n",
- " return inputs * self.incl_prob\n",
- " \n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " \"\"\"Back propagates gradients through a layer.\n",
- "\n",
- " Given gradients with respect to the outputs of the layer calculates the\n",
- " gradients with respect to the layer inputs. This should correspond to\n",
- " default stochastic forward-propagation.\n",
- "\n",
- " Args:\n",
- " inputs: Array of layer inputs of shape (batch_size, input_dim).\n",
- " outputs: Array of layer outputs calculated in forward pass of\n",
- " shape (batch_size, output_dim).\n",
- " grads_wrt_outputs: Array of gradients with respect to the layer\n",
- " outputs of shape (batch_size, output_dim).\n",
- "\n",
- " Returns:\n",
- " Array of gradients with respect to the layer inputs of shape\n",
- " (batch_size, input_dim).\n",
- " \"\"\"\n",
- " return grads_wrt_outputs * self._mask \n",
- "\n",
- " def __repr__(self):\n",
- " return 'DropoutLayer(incl_prob={0:.1f})'.format(self.incl_prob)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Test your implementation by running the cell below (if your implementation is incorrect you will get an `AssertionError` - look at what the assert statement is checking for a clue as to what is wrong)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "seed = 31102016 \n",
- "rng = np.random.RandomState(seed)\n",
- "test_incl_probs = [0.1, 0.5, 0.7]\n",
- "input_shape = (5, 10)\n",
- "for incl_prob in test_incl_probs:\n",
- " layer = DropoutLayer(rng, incl_prob)\n",
- " inputs = rng.normal(size=input_shape)\n",
- " grads_wrt_outputs = rng.normal(size=input_shape)\n",
- " for t in range(100):\n",
- " outputs = layer.fprop(inputs, stochastic=True)\n",
- " # Check outputted array correct shape\n",
- " assert outputs.shape == inputs.shape\n",
- " # Check all outputs are either equal to inputs or zero\n",
- " assert np.all((outputs == inputs) | (outputs == 0))\n",
- " grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs)\n",
- " # Check back-propagated gradients only non-zero for non-zero outputs\n",
- " assert np.all((outputs != 0) == (grads_wrt_inputs != 0))\n",
- " assert np.all(grads_wrt_outputs[outputs != 0] == grads_wrt_inputs[outputs != 0])\n",
- " det_outputs = layer.fprop(inputs, stochastic=False)\n",
- " # Check deterministic fprop outputs are correct shape\n",
- " assert det_outputs.shape == inputs.shape\n",
- " # Check deterministic fprop outputs scaled correctly\n",
- " assert np.allclose(det_outputs, incl_prob * inputs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Optional extension\n",
- "\n",
- "Above we assumed the same dropout mask was applied to each input in a batch, as specified in the lecture slides. In practice sometimes a different mask is sampled for each input. As an extension you could try implementing this per-input form of dropout either by defining a new layer or adding an extra argument to the constructor of the above layer which allows you to switch between the two forms."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 2: Training with dropout"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Experiment with training models with dropout layers to classify MNIST digits. Code has been provided below as a starting point for setting up the model objects though feel free to use any additional adaptive learning rules or learning rule schedulers you wrote during the coursework instead. You may also wish to change the model architecture to use a larger model with more parameters in which the regularisation provided by dropout is likely to have a more pronounced effect. You will probably also find that models with dropout generally need to be trained over more epochs than those without (can you suggest why this might be?).\n",
- "\n",
- "You should training with a few different `incl_prob` settings for the dropout layers and try to establish how the values chosen affect the training performance. You may wish to experiment with using a different dropout probability at the input than for the intermediate layers (why?).\n",
- "\n",
- "You may wish to start reading through and implementing exercise 3 while waiting for training runs to complete."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import logging\n",
- "from mlp.data_providers import MNISTDataProvider\n",
- "from mlp.models import MultipleLayerModel\n",
- "from mlp.layers import ReluLayer, AffineLayer\n",
- "from mlp.errors import CrossEntropySoftmaxError\n",
- "from mlp.initialisers import GlorotUniformInit, ConstantInit\n",
- "from mlp.learning_rules import MomentumLearningRule\n",
- "from mlp.optimisers import Optimiser\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# Seed a random number generator\n",
- "seed = 31102016 \n",
- "rng = np.random.RandomState(seed)\n",
- "\n",
- "# Set up a logger object to print info about the training run to stdout\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
- "logger.handlers = [logging.StreamHandler()]\n",
- "\n",
- "# Create data provider objects for the MNIST data set\n",
- "train_data = MNISTDataProvider('train', batch_size=50, rng=rng)\n",
- "valid_data = MNISTDataProvider('valid', batch_size=50, rng=rng)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
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- "
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- " it may mean that your frontend doesn't currently support widgets.\n",
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- "
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- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n",
- " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the Jupyter\n",
- " Widgets Documentation for setup instructions.\n",
- "
\n",
- "
\n",
- " If you're reading this message in another notebook frontend (for example, a static\n",
- " rendering on GitHub or NBViewer),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "
\n"
- ],
- "text/plain": [
- "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 100: 5.3s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.25e-01, error(valid)=2.30e-01, acc(valid)=9.31e-01\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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NtCIiIi2GOdeyJsdIT093GRkZ/tmZc/DPwyE8Gq74st6bX/bCfFZszWfmTePxnfoXERHx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\nnLsI+BI4x7eaPtN6cs5tBTaaWX/fogn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- "output_type": "display_data"
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Vxd2F0ckxjOgZ7dUpHpVSvsndNm0NbdV6airhPz+wl25dcB9c+lvP9s7O323b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\nnZZLau9Y4iKCvV0UpZRSqsk6TWhnFR0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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Probability of input being included in output in dropout layer\n",
- "incl_prob = 0.5\n",
- "\n",
- "input_dim, output_dim, hidden_dim = 784, 10, 125\n",
- "\n",
- "# Use Glorot initialisation scheme for weights and zero biases\n",
- "weights_init = GlorotUniformInit(rng=rng, gain=2.**0.5)\n",
- "biases_init = ConstantInit(0.)\n",
- "\n",
- "# Create three affine layer model with rectified linear non-linearities\n",
- "# and dropout layers before every affine layer\n",
- "model = MultipleLayerModel([\n",
- " DropoutLayer(rng, incl_prob),\n",
- " AffineLayer(input_dim, hidden_dim, weights_init, biases_init), \n",
- " ReluLayer(),\n",
- " DropoutLayer(rng, incl_prob),\n",
- " AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init), \n",
- " ReluLayer(),\n",
- " DropoutLayer(rng, incl_prob),\n",
- " AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
- "])\n",
- "\n",
- "# Multiclass classification therefore use cross-entropy + softmax error\n",
- "error = CrossEntropySoftmaxError()\n",
- "\n",
- "# Use a momentum learning rule - you could use an adaptive learning rule\n",
- "# implemented for the coursework here instead\n",
- "learning_rule = MomentumLearningRule(0.02, 0.9)\n",
- "\n",
- "# Monitor classification accuracy during training\n",
- "data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
- "\n",
- "optimiser = Optimiser(\n",
- " model, error, learning_rule, train_data, valid_data, data_monitors, notebook=True)\n",
- "\n",
- "num_epochs = 100\n",
- "stats_interval = 5\n",
- "\n",
- "stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
- "\n",
- "# Plot the change in the validation and training set error over training.\n",
- "fig_1 = plt.figure(figsize=(8, 4))\n",
- "ax_1 = fig_1.add_subplot(111)\n",
- "for k in ['error(train)', 'error(valid)']:\n",
- " ax_1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- "ax_1.legend(loc=0)\n",
- "ax_1.set_xlabel('Epoch number')\n",
- "\n",
- "# Plot the change in the validation and training set accuracy over training.\n",
- "fig_2 = plt.figure(figsize=(8, 4))\n",
- "ax_2 = fig_2.add_subplot(111)\n",
- "for k in ['acc(train)', 'acc(valid)']:\n",
- " ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- "ax_2.legend(loc=0)\n",
- "ax_2.set_xlabel('Epoch number')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 3: Implementing maxout\n",
- "\n",
- "[Maxout](http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf) can be considered a generalisation of the rectified linear transformation implemented in the previous lab. \n",
- "\n",
- "For a rectified linear (`Relu`) layer the forward propagation corresponds to\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_k = \n",
- " \\max\\left\\lbrace 0,\\,x^{(b)}_k \\right\\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "i.e. each output corresponds to an pairwise maximum of a constant (0) and the input.\n",
- "\n",
- "Instead of taking the maximum of the input and a constant, we could instead consider taking the maximum over sets of inputs of a fixed size $s$.\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_k = \n",
- " \\max\\left\\lbrace x^{(b)}_{(k-1)s + 1},\\, x^{(b)}_{(k-1)s + 2},\\, \\dots ,\\, x^{(b)}_{ks} \\right\\rbrace\n",
- "\\end{equation}\n",
- "\n",
- "If these inputs $x^{(b)}_d$ are themselves the outputs of an affine layer, then this corresponds to taking the maximum of a series of affine functions of the previous layer outputs. Like a rectified linear layer this leads to piecewise linear input-output relationships (which have well-behaved gradients which do not suffer from the saturation problems of logistic sigmoid / hyperbolic tangent transformations) but unlike the rectified linear case we do not end force a portion of the outputs to be zero. \n",
- "\n",
- "Experimentally this form of transformation has been found to give good performance, with the name *maxout* chosen because the *out*put is the *max*imum of a set of inputs. Maxout is also commonly used with dropout layers however note they are not directly related - maxout defines a deterministic non-linear transformation which can help improve the representational capacity and trainability of models; dropout defines a stochastic transformation which is mainly aimed at regularising a model to reduce overfitting.\n",
- "\n",
- "Using layers which take the maximum of fixed sized sets of inputs is also a common technique in models with convolutional layers which we will cover later in the course, with here the layer commonly being termed a *max-pooling* layer (with there being natural generalisation to other choices of reduction functions over pools such as the mean). We will adopt this terminology here for a layer implementing the transformation described above and we will be able to reuse our code implementing this maximum operation when experimenting with convolutional models.\n",
- "\n",
- "The partial derivatives of this max-pooling transformation are sparse (lots of values are zero), with only the partial derivative of the output of a pool with respect to the maximum input in the pool non-zero. This can be expressed as\n",
- "\n",
- "\\begin{equation}\n",
- " \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = \n",
- " \\begin{cases} \n",
- " 1 & \\quad (k-1)s + 1 \\leq d \\leq ks \\quad\\textrm{and} &x^{(b)}_d = \\max\\left\\lbrace x^{(b)}_{(k-1)s + 1},\\, x^{(b)}_{(k-1)s + 2},\\, \\dots ,\\, x^{(b)}_{ks} \\right\\rbrace \\\\\n",
- " 0 & \\quad \\textrm{otherwise}\n",
- " \\end{cases}.\n",
- "\\end{equation}\n",
- "\n",
- "Using these definitions implement the `fprop` and `bprop` methods of the skeleton `MaxPoolingLayer` class below.\n",
- "\n",
- "Some hints\n",
- "\n",
- " * One way of organising the inputs into non-overlapping pools is using the `numpy.reshape` function.\n",
- " * The `numpy.max` function has an `axis` argument which allows you specify the axis (dimension) of the input array to take the maximum over.\n",
- " * It may help to construct a binary mask corresponding to the definitions of the partial derivatives above to allow you to implement the `bprop` method. \n",
- " * As with the `DropoutLayer` it is fine to temporarily store values calculated in the `fprop` method as attributes of the object (e.g. `self.val = val`) to use in the `bprop` method (although you don't necessarily need to do this)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "from mlp.layers import Layer\n",
- "\n",
- "class MaxPoolingLayer(Layer):\n",
- " \n",
- " def __init__(self, pool_size=2):\n",
- " \"\"\"Construct a new max-pooling layer.\n",
- " \n",
- " Args:\n",
- " pool_size: Positive integer specifying size of pools over\n",
- " which to take maximum value. The outputs of the layer\n",
- " feeding in to this layer must have a dimension which\n",
- " is a multiple of this pool size such that the outputs\n",
- " can be split in to pools with no dimensions left over.\n",
- " \"\"\"\n",
- " self.pool_size = pool_size\n",
- " \n",
- " def fprop(self, inputs):\n",
- " \"\"\"Forward propagates activations through the layer transformation.\n",
- " \n",
- " This corresponds to taking the maximum over non-overlapping pools of\n",
- " inputs of a fixed size `pool_size`.\n",
- "\n",
- " Args:\n",
- " inputs: Array of layer inputs of shape (batch_size, input_dim).\n",
- "\n",
- " Returns:\n",
- " outputs: Array of layer outputs of shape (batch_size, output_dim).\n",
- " \"\"\"\n",
- " assert inputs.shape[-1] % self.pool_size == 0, (\n",
- " 'Last dimension of inputs must be multiple of pool size')\n",
- " pooled_inputs = inputs.reshape(\n",
- " inputs.shape[:-1] + \n",
- " (inputs.shape[-1] // self.pool_size, self.pool_size))\n",
- " pool_maxes = pooled_inputs.max(-1)\n",
- " self._mask = pooled_inputs == pool_maxes[..., None]\n",
- " return pool_maxes\n",
- "\n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " \"\"\"Back propagates gradients through a layer.\n",
- "\n",
- " Given gradients with respect to the outputs of the layer calculates the\n",
- " gradients with respect to the layer inputs.\n",
- "\n",
- " Args:\n",
- " inputs: Array of layer inputs of shape (batch_size, input_dim).\n",
- " outputs: Array of layer outputs calculated in forward pass of\n",
- " shape (batch_size, output_dim).\n",
- " grads_wrt_outputs: Array of gradients with respect to the layer\n",
- " outputs of shape (batch_size, output_dim).\n",
- "\n",
- " Returns:\n",
- " Array of gradients with respect to the layer inputs of shape\n",
- " (batch_size, input_dim).\n",
- " \"\"\"\n",
- " return (self._mask * grads_wrt_outputs[..., None]).reshape(inputs.shape)\n",
- "\n",
- " def __repr__(self):\n",
- " return 'MaxPoolingLayer(pool_size={0})'.format(self.pool_size)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Test your implementation by running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "test_inputs = np.array([[-3, -4, 5, 8], [0, -2, 3, -8], [1, 5, 3, 2]])\n",
- "test_outputs_1 = np.array([[8], [3], [5]])\n",
- "test_grads_wrt_outputs_1 = np.array([[10], [5], [-3]])\n",
- "test_grads_wrt_inputs_1 = np.array([[0, 0, 0, 10], [0, 0, 5, 0], [0, -3, 0, 0]])\n",
- "test_outputs_2 = np.array([[-3, 8], [0, 3], [5, 3]])\n",
- "test_grads_wrt_outputs_2 = np.array([[3, -1], [2, 5], [5, 3]])\n",
- "test_grads_wrt_inputs_2 = np.array([[3, 0, 0, -1], [2, 0, 5, 0], [0, 5, 3, 0]])\n",
- "layer_1 = MaxPoolingLayer(4)\n",
- "layer_2 = MaxPoolingLayer(2)\n",
- "# Check fprop with pool_size = 4\n",
- "assert np.allclose(layer_1.fprop(test_inputs), test_outputs_1)\n",
- "# Check bprop with pool_size = 4\n",
- "assert np.allclose(\n",
- " layer_1.bprop(test_inputs, test_outputs_1, test_grads_wrt_outputs_1),\n",
- " test_grads_wrt_inputs_1\n",
- ")\n",
- "# Check fprop with pool_size = 2\n",
- "assert np.allclose(layer_2.fprop(test_inputs), test_outputs_2)\n",
- "# Check bprop with pool_size = 2\n",
- "assert np.allclose(\n",
- " layer_2.bprop(test_inputs, test_outputs_2, test_grads_wrt_outputs_2),\n",
- " test_grads_wrt_inputs_2\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 4: Training with maxout\n",
- "\n",
- "Use your `MaxPoolingLayer` implementation in a multiple layer models to experiment with how well maxout networks are able to classify MNIST digits. As with the dropout training exercise, code has been provided below as a starting point for setting up the model objects, but again feel free to substitute any components.\n",
- "\n",
- "If you have time you may wish to experiment with training a model using a combination of maxout and dropout or another regularisation method covered in the last lab notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import logging\n",
- "from mlp.data_providers import MNISTDataProvider\n",
- "from mlp.models import MultipleLayerModel\n",
- "from mlp.layers import AffineLayer\n",
- "from mlp.errors import CrossEntropySoftmaxError\n",
- "from mlp.initialisers import GlorotUniformInit, ConstantInit\n",
- "from mlp.learning_rules import MomentumLearningRule\n",
- "from mlp.optimisers import Optimiser\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "# Seed a random number generator\n",
- "seed = 31102016 \n",
- "rng = np.random.RandomState(seed)\n",
- "\n",
- "# Set up a logger object to print info about the training run to stdout\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
- "logger.handlers = [logging.StreamHandler()]\n",
- "\n",
- "# Create data provider objects for the MNIST data set\n",
- "train_data = MNISTDataProvider('train', batch_size=50, rng=rng)\n",
- "valid_data = MNISTDataProvider('valid', batch_size=50, rng=rng)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "ename": "TypeError",
- "evalue": "train() got an unexpected keyword argument 'notebook'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0mstats_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mstats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimiser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstats_interval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstats_interval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnotebook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;31m# Plot the change in the validation and training set error over training.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mTypeError\u001b[0m: train() got an unexpected keyword argument 'notebook'"
- ]
- }
- ],
- "source": [
- "# Size of pools to take maximum over\n",
- "pool_size = 2\n",
- "\n",
- "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
- "\n",
- "# Use Glorot initialisation scheme for weights and zero biases\n",
- "weights_init = GlorotUniformInit(rng=rng)\n",
- "biases_init = ConstantInit(0.)\n",
- "\n",
- "# Create three affine layer model interleaved with max-pooling layers\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim * pool_size, weights_init, biases_init), \n",
- " MaxPoolingLayer(pool_size),\n",
- " AffineLayer(hidden_dim, hidden_dim * pool_size, weights_init, biases_init), \n",
- " MaxPoolingLayer(pool_size),\n",
- " AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
- "])\n",
- "\n",
- "# Multiclass classification therefore use cross-entropy + softmax error\n",
- "error = CrossEntropySoftmaxError()\n",
- "\n",
- "# Use a momentum learning rule - you could use an adaptive learning rule\n",
- "# implemented for the coursework here instead\n",
- "learning_rule = MomentumLearningRule(0.02, 0.9)\n",
- "\n",
- "# Monitor classification accuracy during training\n",
- "data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
- "\n",
- "optimiser = Optimiser(\n",
- " model, error, learning_rule, train_data, valid_data, data_monitors, notebook=True)\n",
- "\n",
- "num_epochs = 100\n",
- "stats_interval = 5\n",
- "\n",
- "stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
- "\n",
- "# Plot the change in the validation and training set error over training.\n",
- "fig_1 = plt.figure(figsize=(8, 4))\n",
- "ax_1 = fig_1.add_subplot(111)\n",
- "for k in ['error(train)', 'error(valid)']:\n",
- " ax_1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- "ax_1.legend(loc=0)\n",
- "ax_1.set_xlabel('Epoch number')\n",
- "\n",
- "# Plot the change in the validation and training set accuracy over training.\n",
- "fig_2 = plt.figure(figsize=(8, 4))\n",
- "ax_2 = fig_2.add_subplot(111)\n",
- "for k in ['acc(train)', 'acc(valid)']:\n",
- " ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- "ax_2.legend(loc=0)\n",
- "ax_2.set_xlabel('Epoch number')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/BatchNormalizationLayer_tests.ipynb b/notebooks/BatchNormalizationLayer_tests.ipynb
deleted file mode 100644
index df12340..0000000
--- a/notebooks/BatchNormalizationLayer_tests.ipynb
+++ /dev/null
@@ -1,152 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from mlp.layers import BatchNormalizationLayer\n",
- "test_inputs = np.array([[-1.38066782, -0.94725498, -3.05585424, 2.28644454, 0.85520889,\n",
- " 0.10575624, 0.23618609, 0.84723205, 1.06569909, -2.21704034],\n",
- " [ 0.11060968, -0.0747448 , 0.56809029, 2.45926149, -2.28677816,\n",
- " -0.9964566 , 2.7356007 , 1.98002308, -0.39032315, 1.46515481]])\n",
- "test_grads_wrt_outputs = np.array([[-0.43857052, 1.00380109, -1.18425494, 0.00486091, 0.21470207,\n",
- " -0.12179054, -0.11508482, 0.738482 , -1.17249238, 0.69188295],\n",
- " [ 1.07802015, 0.69901145, 0.81603688, -1.76743026, -1.24418692,\n",
- " -0.65729963, -0.50834305, -0.49016145, 1.63749743, -0.71123104]])\n",
- "\n",
- "#produce BatchNorm fprop and bprop\n",
- "activation_layer = BatchNormalizationLayer(input_dim=10)\n",
- "\n",
- "beta = np.array(10*[0.3])\n",
- "gamma = np.array(10*[0.5])\n",
- "\n",
- "activation_layer.params = [gamma, beta]\n",
- "BN_fprop = activation_layer.fprop(test_inputs)\n",
- "BN_bprop = activation_layer.bprop(\n",
- " test_inputs, BN_fprop, test_grads_wrt_outputs)\n",
- "BN_grads_wrt_params = activation_layer.grads_wrt_params(\n",
- " test_inputs, test_grads_wrt_outputs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "true_fprop_outputs = np.array([[-0.1999955 , -0.19998686, -0.19999924, -0.1996655 , 0.79999899,\n",
- " 0.79999177, -0.1999984 , -0.19999221, 0.79999528, -0.19999926],\n",
- " [ 0.7999955 , 0.79998686, 0.79999924, 0.7996655 , -0.19999899,\n",
- " -0.19999177, 0.7999984 , 0.79999221, -0.19999528, 0.79999926]])\n",
- "assert BN_fprop.shape == true_fprop_outputs.shape, (\n",
- " 'Layer bprop returns incorrect shaped array. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_fprop_outputs.shape, BN_fprop.shape)\n",
- ")\n",
- "assert np.allclose(np.round(BN_fprop, decimals=2), np.round(true_fprop_outputs, decimals=2)), (\n",
- "'Layer bprop does not return correct values. '\n",
- "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
- ".format(true_fprop_outputs, BN_fprop, BN_fprop-true_fprop_outputs)\n",
- ")\n",
- "\n",
- "print(\"Batch Normalization F-prop test passed\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "true_bprop_outputs = np.array([[ -9.14558020e-06, 9.17665617e-06, -8.40575535e-07,\n",
- " 6.85384297e-03, 9.40668131e-07, 7.99795574e-06,\n",
- " 5.03719464e-07, 1.69038704e-05, -1.82061629e-05,\n",
- " 5.62083224e-07],\n",
- " [ 9.14558020e-06, -9.17665617e-06, 8.40575535e-07,\n",
- " -6.85384297e-03, -9.40668131e-07, -7.99795574e-06,\n",
- " -5.03719464e-07, -1.69038704e-05, 1.82061629e-05,\n",
- " -5.62083224e-07]])\n",
- "assert BN_bprop.shape == true_bprop_outputs.shape, (\n",
- " 'Layer bprop returns incorrect shaped array. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_bprop_outputs.shape, BN_bprop.shape)\n",
- ")\n",
- "assert np.allclose(np.round(BN_bprop, decimals=2), np.round(true_bprop_outputs, decimals=2)), (\n",
- "'Layer bprop does not return correct values. '\n",
- "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
- ".format(true_bprop_outputs, BN_bprop, BN_bprop-true_bprop_outputs)\n",
- ")\n",
- "\n",
- "print(\"Batch Normalization B-prop test passed\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "grads_wrt_gamma, grads_wrt_beta = BN_grads_wrt_params\n",
- "true_grads_wrt_gamma = np.array(([ 1.51657703, -0.30478163, 2.00028878, -1.77110552, 1.45888603,\n",
- " 0.53550028, -0.39325697, -1.2286243 , -2.8099633 , -1.40311192]))\n",
- "true_grads_wrt_beta = np.array([ 0.63944963, 1.70281254, -0.36821806, -1.76256935, -1.02948485,\n",
- " -0.77909018, -0.62342786, 0.24832055, 0.46500505, -0.01934809])\n",
- "\n",
- "assert grads_wrt_gamma.shape == true_grads_wrt_gamma.shape, (\n",
- " 'Layer bprop returns incorrect shaped array. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_grads_wrt_gamma.shape, grads_wrt_gamma.shape)\n",
- ")\n",
- "assert np.allclose(np.round(grads_wrt_gamma, decimals=2), np.round(true_grads_wrt_gamma, decimals=2)), (\n",
- "'Layer bprop does not return correct values. '\n",
- "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
- ".format(true_grads_wrt_gamma, grads_wrt_gamma, grads_wrt_gamma-true_grads_wrt_gamma)\n",
- ")\n",
- "\n",
- "assert grads_wrt_beta.shape == true_grads_wrt_beta.shape, (\n",
- " 'Layer bprop returns incorrect shaped array. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_grads_wrt_beta.shape, grads_wrt_beta.shape)\n",
- ")\n",
- "assert np.allclose(np.round(grads_wrt_beta, decimals=2), np.round(true_grads_wrt_beta, decimals=2)), (\n",
- "'Layer bprop does not return correct values. '\n",
- "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
- ".format(true_grads_wrt_beta, grads_wrt_beta, grads_wrt_beta-true_grads_wrt_beta)\n",
- ")\n",
- "\n",
- "print(\"Batch Normalization grads wrt to params test passed\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/ConvolutionalLayer_tests.ipynb b/notebooks/ConvolutionalLayer_tests.ipynb
deleted file mode 100644
index 8b04ae1..0000000
--- a/notebooks/ConvolutionalLayer_tests.ipynb
+++ /dev/null
@@ -1,307 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Below a skeleton class and associated test functions for the `fprop`, `bprop` and `grads_wrt_params` methods of the ConvolutionalLayer class are included.\n",
- "\n",
- "The test functions assume that in your implementation of `fprop` for the convolutional layer, outputs are calculated only for 'valid' overlaps of the kernel filters with the input - i.e. without any padding.\n",
- "\n",
- "It is also assumed that if convolutions with non-unit strides are implemented the default behaviour is to take unit-strides, with the test cases only correct for unit strides in both directions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The three test functions are defined in the cell below. All the functions take as first argument the *class* corresponding to the convolutional layer implementation to be tested (**not** an instance of the class). It is assumed the class being tested has an `__init__` method with at least all of the arguments defined in the skeleton definition above. A boolean second argument to each function can be used to specify if the layer implements a cross-correlation or convolution based operation (see note in [seventh lecture slides](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp07-cnn.pdf))."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "\n",
- "def test_conv_layer_fprop(layer_class, do_cross_correlation=False):\n",
- " \"\"\"Tests `fprop` method of a convolutional layer.\n",
- " \n",
- " Checks the outputs of `fprop` method for a fixed input against known\n",
- " reference values for the outputs and raises an AssertionError if\n",
- " the outputted values are not consistent with the reference values. If\n",
- " tests are all passed returns True.\n",
- " \n",
- " Args:\n",
- " layer_class: Convolutional layer implementation following the \n",
- " interface defined in the provided skeleton class.\n",
- " do_cross_correlation: Whether the layer implements an operation\n",
- " corresponding to cross-correlation (True) i.e kernels are\n",
- " not flipped before sliding over inputs, or convolution\n",
- " (False) with filters being flipped.\n",
- "\n",
- " Raises:\n",
- " AssertionError: Raised if output of `layer.fprop` is inconsistent \n",
- " with reference values either in shape or values.\n",
- " \"\"\"\n",
- " inputs = np.arange(96).reshape((2, 3, 4, 4))\n",
- " kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\n",
- " if do_cross_correlation:\n",
- " kernels = kernels[:, :, ::-1, ::-1]\n",
- " biases = np.arange(2)\n",
- " true_output = np.array(\n",
- " [[[[ -958., -1036., -1114.],\n",
- " [-1270., -1348., -1426.],\n",
- " [-1582., -1660., -1738.]],\n",
- " [[ 1707., 1773., 1839.],\n",
- " [ 1971., 2037., 2103.],\n",
- " [ 2235., 2301., 2367.]]],\n",
- " [[[-4702., -4780., -4858.],\n",
- " [-5014., -5092., -5170.],\n",
- " [-5326., -5404., -5482.]],\n",
- " [[ 4875., 4941., 5007.],\n",
- " [ 5139., 5205., 5271.],\n",
- " [ 5403., 5469., 5535.]]]]\n",
- " )\n",
- " \n",
- " layer = layer_class(\n",
- " num_input_channels=kernels.shape[1], \n",
- " num_output_channels=kernels.shape[0], \n",
- " input_dim_1=inputs.shape[2], \n",
- " input_dim_2=inputs.shape[3],\n",
- " kernel_dim_1=kernels.shape[2],\n",
- " kernel_dim_2=kernels.shape[3]\n",
- " )\n",
- " layer.params = [kernels, biases]\n",
- " layer_output = layer.fprop(inputs)\n",
- " \n",
- " assert layer_output.shape == true_output.shape, (\n",
- " 'Layer fprop gives incorrect shaped output. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_output.shape, layer_output.shape)\n",
- " )\n",
- " assert np.allclose(layer_output, true_output), (\n",
- " 'Layer fprop does not give correct output. '\n",
- " 'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}.'\n",
- " .format(true_output, layer_output, true_output-layer_output)\n",
- " )\n",
- " return True\n",
- "\n",
- "def test_conv_layer_bprop(layer_class, do_cross_correlation=False):\n",
- " \"\"\"Tests `bprop` method of a convolutional layer.\n",
- " \n",
- " Checks the outputs of `bprop` method for a fixed input against known\n",
- " reference values for the gradients with respect to inputs and raises \n",
- " an AssertionError if the returned values are not consistent with the\n",
- " reference values. If tests are all passed returns True.\n",
- " \n",
- " Args:\n",
- " layer_class: Convolutional layer implementation following the \n",
- " interface defined in the provided skeleton class.\n",
- " do_cross_correlation: Whether the layer implements an operation\n",
- " corresponding to cross-correlation (True) i.e kernels are\n",
- " not flipped before sliding over inputs, or convolution\n",
- " (False) with filters being flipped.\n",
- "\n",
- " Raises:\n",
- " AssertionError: Raised if output of `layer.bprop` is inconsistent \n",
- " with reference values either in shape or values.\n",
- " \"\"\"\n",
- " inputs = np.arange(96).reshape((2, 3, 4, 4))\n",
- " kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\n",
- " if do_cross_correlation:\n",
- " kernels = kernels[:, :, ::-1, ::-1]\n",
- " biases = np.arange(2)\n",
- " grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))\n",
- " outputs = np.array(\n",
- " [[[[ -958., -1036., -1114.],\n",
- " [-1270., -1348., -1426.],\n",
- " [-1582., -1660., -1738.]],\n",
- " [[ 1707., 1773., 1839.],\n",
- " [ 1971., 2037., 2103.],\n",
- " [ 2235., 2301., 2367.]]],\n",
- " [[[-4702., -4780., -4858.],\n",
- " [-5014., -5092., -5170.],\n",
- " [-5326., -5404., -5482.]],\n",
- " [[ 4875., 4941., 5007.],\n",
- " [ 5139., 5205., 5271.],\n",
- " [ 5403., 5469., 5535.]]]]\n",
- " )\n",
- " true_grads_wrt_inputs = np.array(\n",
- " [[[[ 147., 319., 305., 162.],\n",
- " [ 338., 716., 680., 354.],\n",
- " [ 290., 608., 572., 294.],\n",
- " [ 149., 307., 285., 144.]],\n",
- " [[ 23., 79., 81., 54.],\n",
- " [ 114., 284., 280., 162.],\n",
- " [ 114., 272., 268., 150.],\n",
- " [ 73., 163., 157., 84.]],\n",
- " [[-101., -161., -143., -54.],\n",
- " [-110., -148., -120., -30.],\n",
- " [ -62., -64., -36., 6.],\n",
- " [ -3., 19., 29., 24.]]],\n",
- " [[[ 39., 67., 53., 18.],\n",
- " [ 50., 68., 32., -6.],\n",
- " [ 2., -40., -76., -66.],\n",
- " [ -31., -89., -111., -72.]],\n",
- " [[ 59., 115., 117., 54.],\n",
- " [ 114., 212., 208., 90.],\n",
- " [ 114., 200., 196., 78.],\n",
- " [ 37., 55., 49., 12.]],\n",
- " [[ 79., 163., 181., 90.],\n",
- " [ 178., 356., 384., 186.],\n",
- " [ 226., 440., 468., 222.],\n",
- " [ 105., 199., 209., 96.]]]])\n",
- " layer = layer_class(\n",
- " num_input_channels=kernels.shape[1], \n",
- " num_output_channels=kernels.shape[0], \n",
- " input_dim_1=inputs.shape[2], \n",
- " input_dim_2=inputs.shape[3],\n",
- " kernel_dim_1=kernels.shape[2],\n",
- " kernel_dim_2=kernels.shape[3]\n",
- " )\n",
- " layer.params = [kernels, biases]\n",
- " layer_grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs)\n",
- " assert layer_grads_wrt_inputs.shape == true_grads_wrt_inputs.shape, (\n",
- " 'Layer bprop returns incorrect shaped array. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_grads_wrt_inputs.shape, layer_grads_wrt_inputs.shape)\n",
- " )\n",
- " assert np.allclose(layer_grads_wrt_inputs, true_grads_wrt_inputs), (\n",
- " 'Layer bprop does not return correct values. '\n",
- " 'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
- " .format(true_grads_wrt_inputs, layer_grads_wrt_inputs, layer_grads_wrt_inputs-true_grads_wrt_inputs)\n",
- " )\n",
- " return True\n",
- "\n",
- "def test_conv_layer_grad_wrt_params(\n",
- " layer_class, do_cross_correlation=False):\n",
- " \"\"\"Tests `grad_wrt_params` method of a convolutional layer.\n",
- " \n",
- " Checks the outputs of `grad_wrt_params` method for fixed inputs \n",
- " against known reference values for the gradients with respect to \n",
- " kernels and biases, and raises an AssertionError if the returned\n",
- " values are not consistent with the reference values. If tests\n",
- " are all passed returns True.\n",
- " \n",
- " Args:\n",
- " layer_class: Convolutional layer implementation following the \n",
- " interface defined in the provided skeleton class.\n",
- " do_cross_correlation: Whether the layer implements an operation\n",
- " corresponding to cross-correlation (True) i.e kernels are\n",
- " not flipped before sliding over inputs, or convolution\n",
- " (False) with filters being flipped.\n",
- "\n",
- " Raises:\n",
- " AssertionError: Raised if output of `layer.bprop` is inconsistent \n",
- " with reference values either in shape or values.\n",
- " \"\"\"\n",
- " inputs = np.arange(96).reshape((2, 3, 4, 4))\n",
- " kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\n",
- " biases = np.arange(2)\n",
- " grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))\n",
- " true_kernel_grads = np.array(\n",
- " [[[[ -240., -114.],\n",
- " [ 264., 390.]],\n",
- " [[-2256., -2130.],\n",
- " [-1752., -1626.]],\n",
- " [[-4272., -4146.],\n",
- " [-3768., -3642.]]],\n",
- " [[[ 5268., 5232.],\n",
- " [ 5124., 5088.]],\n",
- " [[ 5844., 5808.],\n",
- " [ 5700., 5664.]],\n",
- " [[ 6420., 6384.],\n",
- " [ 6276., 6240.]]]])\n",
- " if do_cross_correlation:\n",
- " kernels = kernels[:, :, ::-1, ::-1]\n",
- " true_kernel_grads = true_kernel_grads[:, :, ::-1, ::-1]\n",
- " true_bias_grads = np.array([-126., 36.])\n",
- " layer = layer_class(\n",
- " num_input_channels=kernels.shape[1], \n",
- " num_output_channels=kernels.shape[0], \n",
- " input_dim_1=inputs.shape[2], \n",
- " input_dim_2=inputs.shape[3],\n",
- " kernel_dim_1=kernels.shape[2],\n",
- " kernel_dim_2=kernels.shape[3]\n",
- " )\n",
- " layer.params = [kernels, biases]\n",
- " layer_kernel_grads, layer_bias_grads = (\n",
- " layer.grads_wrt_params(inputs, grads_wrt_outputs))\n",
- " assert layer_kernel_grads.shape == true_kernel_grads.shape, (\n",
- " 'grads_wrt_params gives incorrect shaped kernel gradients output. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_kernel_grads.shape, layer_kernel_grads.shape)\n",
- " )\n",
- " assert np.allclose(layer_kernel_grads, true_kernel_grads), (\n",
- " 'grads_wrt_params does not give correct kernel gradients output. '\n",
- " 'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}.'\n",
- " .format(true_kernel_grads, layer_kernel_grads)\n",
- " )\n",
- " assert layer_bias_grads.shape == true_bias_grads.shape, (\n",
- " 'grads_wrt_params gives incorrect shaped bias gradients output. '\n",
- " 'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
- " .format(true_bias_grads.shape, layer_bias_grads.shape)\n",
- " )\n",
- " assert np.allclose(layer_bias_grads, true_bias_grads), (\n",
- " 'grads_wrt_params does not give correct bias gradients output. '\n",
- " 'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}.'\n",
- " .format(true_bias_grads, layer_bias_grads)\n",
- " )\n",
- " return True"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "An example of using the test functions if given in the cell below. This assumes you implement a convolution (rather than cross-correlation) operation. If the implementation is correct "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from mlp.layers import ConvolutionalLayer\n",
- "fprop_correct = test_conv_layer_fprop(ConvolutionalLayer, False)\n",
- "bprop_correct = test_conv_layer_bprop(ConvolutionalLayer, False)\n",
- "grads_wrt_param_correct = test_conv_layer_grad_wrt_params(ConvolutionalLayer, False)\n",
- "if fprop_correct and grads_wrt_param_correct and bprop_correct:\n",
- " print('All tests passed.')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/Coursework_2.ipynb b/notebooks/Coursework_2.ipynb
deleted file mode 100644
index 3f4e522..0000000
--- a/notebooks/Coursework_2.ipynb
+++ /dev/null
@@ -1,147 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Coursework 2\n",
- "\n",
- "This notebook is intended to be used as a starting point for your experiments. The instructions can be found in the instructions file located under spec/coursework2.pdf. The methods provided here are just helper functions. If you want more complex graphs such as side by side comparisons of different experiments you should learn more about matplotlib and implement them. Before each experiment remember to re-initialize neural network weights and reset the data providers so you get a properly initialized experiment. For each experiment try to keep most hyperparameters the same except the one under investigation so you can understand what the effects of each are."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "plt.style.use('ggplot')\n",
- "\n",
- "def train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval, notebook=True):\n",
- " \n",
- " # As well as monitoring the error over training also monitor classification\n",
- " # accuracy i.e. proportion of most-probable predicted classes being equal to targets\n",
- " data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
- "\n",
- " # Use the created objects to initialise a new Optimiser instance.\n",
- " optimiser = Optimiser(\n",
- " model, error, learning_rule, train_data, valid_data, data_monitors, notebook=notebook)\n",
- "\n",
- " # Run the optimiser for 5 epochs (full passes through the training set)\n",
- " # printing statistics every epoch.\n",
- " stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
- "\n",
- " # Plot the change in the validation and training set error over training.\n",
- " fig_1 = plt.figure(figsize=(8, 4))\n",
- " ax_1 = fig_1.add_subplot(111)\n",
- " for k in ['error(train)', 'error(valid)']:\n",
- " ax_1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- " ax_1.legend(loc=0)\n",
- " ax_1.set_xlabel('Epoch number')\n",
- "\n",
- " # Plot the change in the validation and training set accuracy over training.\n",
- " fig_2 = plt.figure(figsize=(8, 4))\n",
- " ax_2 = fig_2.add_subplot(111)\n",
- " for k in ['acc(train)', 'acc(valid)']:\n",
- " ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- " ax_2.legend(loc=0)\n",
- " ax_2.set_xlabel('Epoch number')\n",
- " \n",
- " return stats, keys, run_time, fig_1, ax_1, fig_2, ax_2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The below code will set up the data providers, random number\n",
- "# generator and logger objects needed for training runs. As\n",
- "# loading the data from file take a little while you generally\n",
- "# will probably not want to reload the data providers on\n",
- "# every training run. If you wish to reset their state you\n",
- "# should instead use the .reset() method of the data providers.\n",
- "import numpy as np\n",
- "import logging\n",
- "from mlp.data_providers import MNISTDataProvider, EMNISTDataProvider\n",
- "\n",
- "# Seed a random number generator\n",
- "seed = 10102016 \n",
- "rng = np.random.RandomState(seed)\n",
- "batch_size = 100\n",
- "# Set up a logger object to print info about the training run to stdout\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
- "logger.handlers = [logging.StreamHandler()]\n",
- "\n",
- "# Create data provider objects for the MNIST data set\n",
- "train_data = EMNISTDataProvider('train', batch_size=batch_size, rng=rng)\n",
- "valid_data = EMNISTDataProvider('valid', batch_size=batch_size, rng=rng)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The model set up code below is provided as a starting point.\n",
- "# You will probably want to add further code cells for the\n",
- "# different experiments you run.\n",
- "\n",
- "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer, ReluLayer, LeakyReluLayer, ELULayer, SELULayer\n",
- "from mlp.errors import CrossEntropySoftmaxError\n",
- "from mlp.models import MultipleLayerModel\n",
- "from mlp.initialisers import ConstantInit, GlorotUniformInit\n",
- "from mlp.learning_rules import GradientDescentLearningRule\n",
- "from mlp.optimisers import Optimiser\n",
- "\n",
- "#setup hyperparameters\n",
- "learning_rate = 0.1\n",
- "num_epochs = 100\n",
- "stats_interval = 1\n",
- "input_dim, output_dim, hidden_dim = 784, 47, 100\n",
- "\n",
- "weights_init = GlorotUniformInit(rng=rng)\n",
- "biases_init = ConstantInit(0.)\n",
- "model = MultipleLayerModel([\n",
- " AffineLayer(input_dim, hidden_dim, weights_init, biases_init), \n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init), \n",
- " ReluLayer(),\n",
- " AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
- "])\n",
- "\n",
- "error = CrossEntropySoftmaxError()\n",
- "# Use a basic gradient descent learning rule\n",
- "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
- "\n",
- "#Remember to use notebook=False when you write a script to be run in a terminal\n",
- "_ = train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval, notebook=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/Introduction_to_tensorflow.ipynb b/notebooks/Introduction_to_tensorflow.ipynb
new file mode 100644
index 0000000..07f045f
--- /dev/null
+++ b/notebooks/Introduction_to_tensorflow.ipynb
@@ -0,0 +1,514 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Introduction to TensorFlow\n",
+ "\n",
+ "## Computation graphs\n",
+ "\n",
+ "In the first semester we used the NumPy-based `mlp` Python package to illustrate the concepts involved in automatically propagating gradients through multiple-layer neural network models. We also looked at how to use these calculated derivatives to do gradient-descent based training of models in supervised learning tasks such as classification and regression.\n",
+ "\n",
+ "A key theme in the first semester's work was the idea of defining models in a modular fashion. There we considered models composed of a sequence of *layer* modules, the output of each of which fed into the input of the next in the sequence and each applying a transformation to map inputs to outputs. By defining a standard interface to layer objects with each defining a `fprop` method to *forward propagate* inputs to outputs, and a `bprop` method to *back propagate* gradients with respect to the output of the layer to gradients with respect to the input of the layer, the layer modules could be composed together arbitarily and activations and gradients forward and back propagated through the whole stack respectively.\n",
+ "\n",
+ "
\n",
+ " \n",
+ " 'Pipeline' model composed of sequence of single input, single output layer modules\n",
+ "
\n",
+ "\n",
+ "By construction a layer was defined as an object with a single array input and single array output. This is a natural fit for the architectures of standard feedforward networks which can be thought of a single pipeline of transformations from user provided input data to predicted outputs as illustrated in the figure above. \n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Models which fit less well into pipeline structure: left, a sequence-to-sequence recurrent network; right, a feed forward network with skip connections.\n",
+ "
\n",
+ "\n",
+ "Towards the end of last semester however we encountered several models which do not fit so well in to this pipeline-like structure. For instance (unrolled) recurrent neural networks tend to have inputs feeding in to and outputs feeding out from multiple points along a deep feedforward model corresponding to the updates of the hidden recurrent state, as illustrated in the left panel in the figure above. It is not trivial to see how to map this structure to our layer based pipeline. Similarly models with skip connections between layers as illustrated in the right panel of the above figure also do not fit particularly well in to a pipeline structure.\n",
+ "\n",
+ "Ideally we would like to be able to compose modular components in more general structures than the pipeline structure we have being using so far. In particular it turns out to be useful to be able to deal with models which have structures defined by arbitrary [*directed acyclic graphs*](https://en.wikipedia.org/wiki/Directed_acyclic_graph) (DAGs), that is graphs connected by directed edges and without any directed cycles. Both the recurrent network and skip-connections examples can be naturally expressed as DAGs as well many other model structures.\n",
+ "\n",
+ "When working with these more general graphical structures, rather than considering a graph made up of layer modules, it often more useful to consider lower level mathematical operations or *ops* that make up the computation as the fundamental building block. A DAG composed of ops is often termed a *computation graph*. THis terminolgy was covered briefly in [lecture 6](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp06-enc.pdf), and also in the [MLPR course](http://www.inf.ed.ac.uk/teaching/courses/mlpr/2016/notes/w5a_backprop.html). The backpropagation rules we used to propagate gradients through a stack of layer modules can be naturally generalised to apply to computation graphs, with this method of applying the chain rule to automatically propagate gradients backwards through a general computation graph also sometimes termed [*reverse-mode automatic differentiation*](https://en.wikipedia.org/wiki/Automatic_differentiation#Reverse_accumulation).\n",
+ "\n",
+ "
\n",
+ " \n",
+ " Computation / data flow graph for an affine transformation $\\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}$\n",
+ "
\n",
+ "\n",
+ "The figure above shows a very simple computation graph corresponding to the mathematical expression $\\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}$, i.e. the affine transformation we encountered last semester. Here the nodes of the graph are operations and the edges the vector or matrix values passed between operations. The opposite convention with nodes as values and edges as operations is also sometimes used. Note that just like there was ambiguity about what to define as a layer (as discussed previously at beginning of the [third lab notebook](03_Multiple_layer_models.ipynb), there are a range of choices for the level of abstraction to use in the op nodes in a computational graph. For instance, we could also have chosen to express the above computational graph with a single `AffineTransform` op node with three inputs (one matrix, two vector) and one vector output. Equally we might choose to express the `MatMul` op in terms of the underlying individual scalar addition and multiplication operations. What to consider an operation is therefore somewhat a matter of choice and what is convenient in a particular setting.\n",
+ "\n",
+ "## TensorFlow\n",
+ "\n",
+ "To allow us to work with models defined by more general computation graphs and to avoid the need to write `fprop` and `bprop` methods for each new model component we want to try out, this semester we will be using the open-source computation graph framework [TensorFlow](https://www.tensorflow.org/), originally developed by the Google Brain team:\n",
+ "\n",
+ "> TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs \n",
+ "in a desktop, server, or mobile device with a single API.\n",
+ "\n",
+ "TensorFlow allows complex computation graphs (also known as data flow graphs in TensorFlow parlance) to be defined via a Python interface, with efficient C++ implementations for running the corresponding operations on different devices. TensorFlow also includes tools for automatic gradient computation and a large and growing suite of pre-define operations useful for gradient-based training of machine learning models.\n",
+ "\n",
+ "In this notebook we will introduce some of the basic elements of constructing, training and evaluating models with TensorFlow. This will use similar material to some of the [official TensorFlow tutorials](https://www.tensorflow.org/tutorials/) but with an additional emphasis of making links to the material covered in this course last semester. For those who have not used a computational graph framework such as TensorFlow or Theano before you may find the [basic usage tutorial](https://www.tensorflow.org/get_started/basic_usage) useful to go through.\n",
+ "\n",
+ "### Installing TensorFlow\n",
+ "\n",
+ "To install TensorFlow, open a terminal, activate your Conda `mlp` environment using\n",
+ "\n",
+ "```\n",
+ "source activate mlp\n",
+ "```\n",
+ "\n",
+ "and then run\n",
+ "\n",
+ "```\n",
+ "pip install tensorflow # for CPU users\n",
+ "```\n",
+ "\n",
+ "```\n",
+ "pip install tensorflow_gpu # for GPU users\n",
+ "```\n",
+ "\n",
+ "This should locally install the stable release version of TensorFlow (currently 1.4.1) in your Conda environment. After installing TensorFlow you may need to restart the kernel in the notebook to allow it to be imported."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 1: EMNIST softmax regression\n",
+ "\n",
+ "As a first example we will train a simple softmax regression model to classify handwritten digit images from the EMNIST data set encountered last semester (for those fed up of working with EMNIST - don't worry you will soon be moving on to other datasets!). This is equivalent to the model implemented in the first exercise of the third lab notebook. We will walk through constructing an equivalent model in TensorFlow and explain new TensorFlow model concepts as we use them. You should run each cell as you progress through the exercise.\n",
+ "\n",
+ "Similarly to the common convention of importing NumPy under the shortform alias `np` it is common to import the Python TensorFlow top-level module under the alias `tf`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We begin by defining [*placeholder*](https://www.tensorflow.org/api_docs/python/io_ops/placeholders) objects for the data inputs and targets arrays. These are nodes in the computation graph to which we will later *feed* in external data, such as batches of training set inputs and targets. This abstraction allows us to reuse the same computation graph for different data inputs - we can think of placeholders as acting equivalently to the arguments of a function. It is actually possible to feed data into any node in a TensorFlow graph however the advantage of using a placeholder is that is *must* always have a value fed into it (an exception will be raised if a value isn't provided) and no arbitrary alternative values needs to be entered.\n",
+ "\n",
+ "The `tf.placeholder` function has three arguments:\n",
+ "\n",
+ " * `dtype` : The [TensorFlow datatype](https://www.tensorflow.org/api_docs/python/framework/tensor_types) for the tensor e.g. `tf.float32` for single-precision floating point values.\n",
+ " * `shape` (optional) : An iterable defining the shape (size of each dimension) of the tensor e.g. `shape=(5, 2)` would indicate a 2D tensor (matrix) with first dimension of size 5 and second dimension of size 2. An entry of `None` in the shape definition corresponds to the corresponding dimension size being left unspecified, so for example `shape=(None, 28, 28)` would allow any 3D inputs with final two dimensions of size 28 to be inputted.\n",
+ " * `name` (optional): String argument defining a name for the tensor which can be useful when visualising a computation graph and for debugging purposes.\n",
+ " \n",
+ "As we will generally be working with batches of datapoints, both the `inputs` and `targets` will be 2D tensors with the first dimension corresponding to the batch size (set as `None` here to allow it to specified later) and the second dimension corresponding to the size of each input or output vector. As in the previous semester's work we will use a 1-of-K encoding for the class targets so for EMNIST each output corresponds to a vector of length 47 (number of digit/letter classes)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "inputs = tf.placeholder(tf.float32, [None, 784], 'inputs')\n",
+ "targets = tf.placeholder(tf.float32, [None, 47], 'targets')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now define [*variable*](https://www.tensorflow.org/api_docs/python/state_ops/variables) objects for the model parameters. Variables are stateful tensors in the computation graph - they have to be explicitly initialised and their internal values can be updated as part of the operations in a graph e.g. gradient updates to model parameter during training. They can also be saved to disk and pre-saved values restored in to a graph at a later time.\n",
+ "\n",
+ "The `tf.Variable` constructor takes an `initial_value` as its first argument; this should be a TensorFlow tensor which specifies the initial value to assign to the variable, often a constant tensor such as all zeros, or random samples from a distribution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "weights = tf.Variable(tf.zeros([784, 47]))\n",
+ "biases = tf.Variable(tf.zeros([47]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now build the computation graph corresponding to producing the predicted outputs of the model (log unnormalised class probabilities) given the data inputs and model parameters. We use the TensorFlow [`matmul`](https://www.tensorflow.org/api_docs/python/math_ops/matrix_math_functions#matmul) op to compute the matrix-matrix product between the 2D array of input vectors and the weight matrix parameter variable. TensorFlow [overloads all of the common arithmetic operators](http://stackoverflow.com/a/35095052) for tensor objects so `x + y` where at least one of `x` or `y` is a tensor instance (both `tf.placeholder` and `tf.Variable` return (sub-classes) of `tf.Tensor`) corresponds to the TensorFlow elementwise addition op `tf.add`. Further elementwise binary arithmetic operators like addition follow NumPy style [broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html), so in the expression below the `+ biases` sub-expression will correspond to creating an operation in the computation graph which adds the bias vector to each of the rows of the 2D tensor output of the `matmul` op."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outputs = tf.matmul(inputs, weights) + biases"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "While we could have defined `outputs` as the softmax of the expression above to produce normalised class probabilities as the outputs of the model, as discussed last semester when using a softmax output combined with a cross-entropy error function it usually desirable from a numerical stability and efficiency perspective to wrap the softmax computation in to the error computation (as done in the `CrossEntropySoftmaxError` class in our `mlp` framework). \n",
+ "\n",
+ "In TensorFlow this can be achieved with the `softmax_cross_entropy_with_logits` op which is part of the `tf.nn` submodule which contains a number of ops specifically for neural network type models. This op takes as its first input log unnormalised class probabilities (sometimes termed logits) and as second input the class label targets which should be of the same dimension as the first input. By default the last dimension of the input tensors is assumed to correspond to the class dimension - this can be altered via an optional `dim` argument.\n",
+ "\n",
+ "The output of the `softmax_cross_entropy_with_logits` op here is a 1D tensor with a cross-entropy error value for each data point in the batch. We wish to minimise the mean cross-entropy error across the full dataset and will use the mean of the error on the batch as a stochastic estimator of this value. In TensorFlow ops which *reduce* a tensor along a dimension(s), for example by taking a sum, mean, or product, are prefixed with `reduce`, with the default behaviour being to perform the reduction across all dimensions of the input tensor and return a scalar output. Therefore the second line below will take the per data point cross-entropy errors and produce a single mean value across the whole batch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "per_datapoint_errors = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\n",
+ "error = tf.reduce_mean(per_datapoint_errors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although for the purposes of training we will use the cross-entropy error as this is differentiable, for evaluation we will also be interested in the classification accuracy i.e. what proportion of all of the predicted classes correspond to the true target label. We can calculate this in TensorFlow similarly to how we used NumPy to do this previously - we use the TensorFlow `tf.argmax` op to find the index of along the class dimension corresponding to the maximum predicted class probability and check if this is equal to the index along the class dimension of the 1-of-$k$ encoded target labels. Analagously to the error computation above, this computes per-datapoint values which we then need to average across with a `reduce_mean` op to produce the classification accuracy for a batch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "per_datapoint_pred_is_correct = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\n",
+ "accuracy = tf.reduce_mean(tf.cast(per_datapoint_pred_is_correct, tf.float32))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As mentioned previously TensorFlow is able to automatically calculate gradients of scalar computation graph outputs with respect to tensors in the computation graph. We can explicitly construct a new sub-graph corresponding to the gradient of a scalar with respect to one or more tensors in the graph using the [`tf.gradients`](https://www.tensorflow.org/api_docs/python/train/gradient_computation) function. \n",
+ "\n",
+ "TensorFlow also however includes a number of higher-level `Optimizer` classes in the `tf.train` module that internally deal with constructing graphs corresponding to the gradients of some scalar loss with respect to one or more `Variable` tensors in the graph (usually corresponding to model parameters) and then using these gradients to update the variables (roughly equivalent to the `LearningRule` classes in the `mlp` framework). The most basic `Optimizer` instance is the `GradientDescentOptimizer` which simply adds operations corresponding to basic (stochastic) gradient descent to the graph (i.e. no momentum, adaptive learning rates etc.). The `__init__` constructor method for this class takes one argument `learning_rate` corresponding to the gradient descent learning rate / step size encountered previously.\n",
+ "\n",
+ "Usually we are not interested in the `Optimizer` object other than in adding operations in the graph corresponding to the optimisation steps. This can be achieved using the `minimize` method of the object which takes as first argument the tensor object corresponding to the scalar loss / error to be minimized. A further optional keyword argument `var_list` can be used to specify a list of variables to compute the gradients of the loss with respect to and update; by default this is set to `None` which indicates to use all trainable variables in the current graph. The `minimize` method returns an operation corresponding to applying the gradient updates to the variables - we need to store a reference to this to allow us to run these operations later. Note we do not need to store a reference to the optimizer as we have no further need of this object hence commonly the steps of constructing the `Optimizer` and calling `minimize` are commonly all applied in a single line as below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_step = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(error)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have now constructed a computation graph which can compute predicted outputs, use these to calculate an error value (and accuracy) and use the gradients of the error with respect to the model parameter variables to update their values with a gradient descent step.\n",
+ "\n",
+ "Although we have defined our computation graph, we have not yet initialised any tensor data in memory - all of the tensor variables defined above are just symbolic representations of parts of the computation graph. We can think of the computation graph as a whole as being similar to a function - it defines a sequence of operations but does not directly run those operations on data itself.\n",
+ "\n",
+ "To run the operations in (part of) a TensorFlow graph we need to create a [`Session`](https://www.tensorflow.org/api_docs/python/client/session_management) object:\n",
+ "\n",
+ "> A `Session` object encapsulates the environment in which `Operation` objects are executed, and `Tensor` objects are evaluated.\n",
+ "\n",
+ "A session object can be constructed using either `tf.Session()` or `tf.InteractiveSession()`. The only difference in the latter is that it installs itself as the default session on construction. This can be useful in interactive contexts such as shells or the notebook interface in which an alternative to running a graph operation using the session `run` method (see below) is to call the `eval` method of an operation e.g. `op.eval()`; generally a session in which the op runs needs to be passed to `eval`; however if an interactive session is used, then this is set as a default to use in `eval` calls."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sess = tf.InteractiveSession()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The key property of a session object is its `run` method. This takes an operation (or list of operations) in a defined graph as an argument and runs the parts of the computation graph necessary to evaluate the output(s) (if any) of the operation(s), and additionally performs any updates to variables states defined by the graph (e.g. gradient updates of parameters). The output values if any of the operation(s) are returned by the `run` call.\n",
+ "\n",
+ "A standard operation which needs to be called before any other operations on a graph which includes variable nodes is a variable *initializer* operation. This, as the name suggests, initialises the values of the variables in the session to the values defined by the `initial_value` argument when adding the variables to the graph. For instance for the graph we have defined here this will initialise the `weights` variable value in the session to a 2D array of zeros of shape `(784, 10)` and the `biases` variable to a 1D array of shape `(10,)`.\n",
+ "\n",
+ "We can access initializer ops for each variable individually using the `initializer` property of the variables in question and then individually run these, however a common pattern is to use the `tf.global_variables_initializer()` function to create a single initializer op which will initialise all globally defined variables in the default graph and then run this as done below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "init_op = tf.global_variables_initializer()\n",
+ "sess.run(init_op)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are now almost ready to begin training our defined model, however as a final step we need to create objects for accessing batches of EMNIST input and target data. In the tutorial code provided in `tf.examples.tutorials.mnist` there is an `input_data` sub-module which provides a `read_data_sets` function for downloading the MNIST data and constructing an object for iterating over MNIST data. However in the `mlp` package we already have the MNIST and EMNIST data provider classes that we used extensively last semester, and corresponding local copies of the MNIST and EMNIST data, so we will use that here as it provides all the necessary functionality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import data_providers as data_providers\n",
+ "train_data = data_providers.EMNISTDataProvider('train', batch_size=50, flatten=True, one_hot=True)\n",
+ "valid_data = data_providers.EMNISTDataProvider('valid', batch_size=50, flatten=True, one_hot=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are now all set to train our model. As when training models last semester, the training procedure will involve two nested loops - an outer loop corresponding to multiple full-passes through the dataset or *epochs* and an inner loop iterating over individual batches in the training data.\n",
+ "\n",
+ "The `init_op` we ran with `sess.run` previously did not depend on the placeholders `inputs` and `target` in our graph, so we simply ran it with `sess.run(init_op)`. The `train_step` operation corresponding to the gradient based updates of the `weights` and `biases` parameter variables does however depend on the `inputs` and `targets` placeholders and so we need to specify values to *feed* into these placeholders; as we wish the gradient updates to be calculated using the gradients with respect to a batch of inputs and targets, the values that we feed in are the input and target batches. This is specified using the keyword `feed_dict` argument to the session `run` method. As the name suggests this should be a Python dictionary (`dict`) with keys corresponding to references to the tensors in the graph to feed values in to and values the corresponding array values to feed in (typically NumPy `ndarray` instances) - here we have `feed_dict = {inputs: input_batch, targets: target_batch}`.\n",
+ "\n",
+ "Another difference in our use of the session `run` method below is that we call it with a list of two operations - `[train_step, error]` rather than just a single operation. This allows the output (and variable updates) of multiple operations in a graph to be evaluated together - here we both run the `train_step` operation to update the parameter values and evaluate the `error` operation to return the mean error on the batch. Although we could split this into two separate session `run` calls, as the operations calculating the batch error will need to be evaluated when running the `train_step` operation (as this is the value gradients are calculated with respect to) this would involve redoing some of the computation and so be less efficient than combining them in a single `run` call.\n",
+ "\n",
+ "As we are running two different operations, the `run` method returns two values here. The `train_step` operation has no outputs and so the first return value is `None` - in the code below we assign this to `_`, this being a common convention in Python code for assigning return values we are not interested in using. The second return value is the average error across the batch which we assign to `batch_error` and use to keep a running average of the dataset error across the epochs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_epoch = 20\n",
+ "for e in range(num_epoch):\n",
+ " running_error = 0.\n",
+ " for input_batch, target_batch in train_data:\n",
+ " _, batch_error = sess.run(\n",
+ " [train_step, error], \n",
+ " feed_dict={inputs: input_batch, targets: target_batch})\n",
+ " running_error += batch_error\n",
+ " running_error /= train_data.num_batches\n",
+ " print('End of epoch {0}: running error average = {1:.2f}'.format(e + 1, running_error))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To check your understanding of using sessions objects to evaluate parts of a graph and feeding values in to a graph, complete the definition of the function in the cell below. This should iterate across all batches in a provided data provider and calculate the error and classification accuracy for each, accumulating the average error and accuracy values across the whole dataset and returning these as a tuple."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_error_and_accuracy(data):\n",
+ " \"\"\"Calculate average error and classification accuracy across a dataset.\n",
+ " \n",
+ " Args:\n",
+ " data: Data provider which iterates over input-target batches in dataset.\n",
+ " \n",
+ " Returns:\n",
+ " Tuple with first element scalar value corresponding to average error\n",
+ " across all batches in dataset and second value corresponding to\n",
+ " average classification accuracy across all batches in dataset.\n",
+ " \"\"\"\n",
+ " err = 0\n",
+ " acc = 0\n",
+ " for input_batch, target_batch in data:\n",
+ " err += sess.run(error, feed_dict={inputs: input_batch, targets: target_batch})\n",
+ " acc += sess.run(accuracy, feed_dict={inputs: input_batch, targets: target_batch})\n",
+ " err /= data.num_batches\n",
+ " acc /= data.num_batches\n",
+ " return err, acc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Test your implementation by running the cell below - this should print the error and accuracy of the trained model on the validation and training datasets if implemented correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print('Train data: Error={0:.2f} Accuracy={1:.2f}'\n",
+ " .format(*get_error_and_accuracy(train_data)))\n",
+ "print('Valid data: Error={0:.2f} Accuracy={1:.2f}'\n",
+ " .format(*get_error_and_accuracy(valid_data)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 2: Explicit graphs, name scopes, summaries and TensorBoard\n",
+ "\n",
+ "In the exercise above we introduced most of the basic concepts needed for constructing graphs in TensorFlow and running graph operations. In an attempt to avoid introducing too many new terms and syntax at once however we skipped over some of the non-essential elements of creating and running models in TensorFlow, in particular some of the provided functionality for organising and structuring the computation graphs created and for monitoring the progress of training runs.\n",
+ "\n",
+ "Now that you are hopefully more familiar with the basics of TensorFlow we will introduce some of these features as they are likely to provide useful when you are building and training more complex models in the rest of this semester.\n",
+ "\n",
+ "Although we started off by motivating TensorFlow as a framework which builds computation graphs, in the code above we never explicitly referenced a graph object. This is because TensorFlow always registers a default graph at start up and all operations are added to this graph by default. The default graph can be accessed using `tf.get_default_graph()`. For example running the code in the cell below will assign a reference to the default graph to `default_graph` and print the total number of operations in the current graph definition."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "default_graph = tf.get_default_graph()\n",
+ "print('Number of operations in graph: {0}'\n",
+ " .format(len(default_graph.get_operations())))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also explicitly create a new graph object using `tf.Graph()`. This may be useful if we wish to build up several independent computation graphs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "graph = tf.Graph()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To add operations to a constructed graph object, we use the `graph.as_default()` [context manager](http://book.pythontips.com/en/latest/context_managers.html). Context managers are used with the `with` statement in Python - `with context_manager:` opens a block in Python in which a special `__enter__` method of the `context_manager` object is called before the code in the block is run and a further special `__exit__` method is run after the block code has finished execution. This can be used to for example manage allocation of resources (e.g. file handles) but also to locally change some 'context' in the code - in the example here, `graph.as_default()` is a context manager which changes the default graph within the following block to be `graph` before returning to the previous default graph once the block code is finished running. Context managers are used extensively in TensorFlow so it is worth being familiar with how they work.\n",
+ "\n",
+ "Another common context manager usage in TensorFlow is to define *name scopes*. As we encountered earlier, individual operations in a TensorFlow graph can be assigned names. As we will see later this is useful for making graphs interpretable when we use the tools provided in TensorFlow for visualising them. As computation graphs can become very big (even the quite simple graph we created in the first exercise has around 100 operations in it) even with interpretable names attached to the graph operations it can still be difficult to understand and debug what is happening in a graph. Therefore rather than simply allowing a single-level naming scheme to be applied to the individual operations in the graph, TensorFlow supports hierachical naming of sub-graphs. This allows sets of related operations to be grouped together under a common name, and thus allows both higher and lower level structure in a graph to be easily identified.\n",
+ "\n",
+ "This hierarchical naming is performed by using the name scope context manager `tf.name_scope('name')`. Starting a block `with tf.name_scope('name'):`, will cause all the of the operations added to a graph within that block to be grouped under the name specified in the `tf.name_scope` call. Name scope blocks can be nested to allow finer-grained sub-groupings of operations. Name scopes can be used to group operations at various levels e.g. operations corresponding to inference/prediction versus training, grouping operations which correspond to the classical definition of a neural network layer etc.\n",
+ "\n",
+ "The code in the cell below uses both a `graph.as_default()` context manager and name scopes to create a second copy of the computation graph corresponding to softmax regression that we constructed in the previous exercise."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with graph.as_default():\n",
+ " with tf.name_scope('data'):\n",
+ " inputs = tf.placeholder(tf.float32, [None, 784], name='inputs')\n",
+ " targets = tf.placeholder(tf.float32, [None, 47], name='targets')\n",
+ " with tf.name_scope('parameters'):\n",
+ " weights = tf.Variable(tf.zeros([784, 47]), name='weights')\n",
+ " biases = tf.Variable(tf.zeros([47]), name='biases')\n",
+ " with tf.name_scope('model'):\n",
+ " outputs = tf.matmul(inputs, weights) + biases\n",
+ " with tf.name_scope('error'):\n",
+ " error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets))\n",
+ " with tf.name_scope('train'):\n",
+ " train_step = tf.train.GradientDescentOptimizer(0.5).minimize(error)\n",
+ " with tf.name_scope('accuracy'):\n",
+ " accuracy = tf.reduce_mean(tf.cast(\n",
+ " tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1)), tf.float32))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As hinted earlier TensorFlow comes with tools for visualising computation graphs. In particular [TensorBoard](https://www.tensorflow.org/how_tos/summaries_and_tensorboard/) is an interactive web application for amongst other things visualising TensorFlow computation graphs (we will explore some of its other functionality in the latter part of the exercise). Typically TensorBoard in launched from a terminal and a browser used to connect to the resulting locally running TensorBoard server instance. However for the purposes of graph visualisation it is also possible to embed a remotely-served TensorBoard graph visualisation interface in a Jupyter notebook using the helper function below (a slight variant of the recipe in [this notebook](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb)).\n",
+ "\n",
+ "Note: The code below seems to not work for some people when accessing the notebook in Firefox. You can either try loading the notebook in an alternative browser, or just skip this section for now and explore the graph visualisation tool when launching TensorBoard below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import display, HTML\n",
+ "import datetime\n",
+ "\n",
+ "def show_graph(graph_def, frame_size=(900, 600)):\n",
+ " \"\"\"Visualize TensorFlow graph.\"\"\"\n",
+ " if hasattr(graph_def, 'as_graph_def'):\n",
+ " graph_def = graph_def.as_graph_def()\n",
+ " timestamp = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n",
+ " code = \"\"\"\n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " \"\"\".format(height=frame_size[1], data=repr(str(graph_def)), id='graph'+timestamp)\n",
+ " iframe = \"\"\"\n",
+ " \n",
+ " \"\"\".format(width=frame_size[0], height=frame_size[1] + 20, src=code.replace('\"', '"'))\n",
+ " display(HTML(iframe))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the cell below to display a visualisation of the graph we just defined. Notice that by default all operations within a particular defined name scope are grouped under a single node; this allows the top-level structure of the graph and how data flows between the various components to be easily visualised. We can also expand these nodes however to interrogate the operations within them - simply double-click on one of the nodes to do this (double-clicking on the expanded node will cause it to collapse again). If you expand the `model` node you should see a graph closely mirroring the affine transform example given as a motivation above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_graph(graph)"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/Introduction_to_tf_mlp_repo.ipynb b/notebooks/Introduction_to_tf_mlp_repo.ipynb
new file mode 100644
index 0000000..8abb571
--- /dev/null
+++ b/notebooks/Introduction_to_tf_mlp_repo.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Tensorflow Experimentation Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "In the previous tutorial we introduced the tensorflow framework and some very basic functionality that it can provide. In this tutorial we will present a far more readable and research oriented tensorflow based code-base that allows one to quickly build new model architectures and research experiments in tensorflow. The proposed code-structure has been tested in real research and has proven a very readable and easily modifiable setup. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The tf_mlp package contains all necessary modules with which one can easily train and evaluate a classifier. The following packages can be found:\n",
+ "\n",
+ " 1. utils: \n",
+ " 1. network_summary: Provides utilities with which one can get network summaries, such as the number of parameters and names of layers.\n",
+ " 2. parser_utils which are used to parse arguments passed to the training scripts.\n",
+ " 3. storage, which is responsible for storing network statistics.\n",
+ " 2. data_providers.py : Provides the data providers for training, validation and testing.\n",
+ " 3. network_architectures.py: Defines the network architectures. We provide VGGNet as an example.\n",
+ " 4. network_builder.py: Builds the tensorflow computation graph. In more detail, it builds the losses, tensorflow summaries and training operations.\n",
+ " 5. network_trainer.py: Runs an experiment, composed of training, validation and testing. It is setup to use arguments such that one can easily write multiple bash scripts with different hyperparameters and run experiments very quickly with minimal code changes.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run an experiment just run:\n",
+ "\n",
+ "```\n",
+ "python network_trainer.py --batch_size 128 --epochs 100 --experiment_prefix VGG_EMNIST --tensorboard_use True --batch_norm_use True --strided_dim_reduction True --seed 16122017\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The arguments after network_trainer.py can be changed to suit your experimental needs. For more arguments and exploring how to add new arguments of your own please view parser_utils.py under utils and network_trainer.py as they provide all the functionality that is necessary to add arguments."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally remember to make sure your code is not just efficient but readable. Research code has a very bad reputation, so let's try to improve readability one research line code at a time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run tensorboard just point tensorboard to the correct logs repository as follows:\n",
+ " ```tensorboard --port 60xx --logdir /path/to/logs```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ ""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3.0
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
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+
+
+
+
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diff --git a/notes/cifar10_100_and_million_song_datasets.md b/notes/cifar10_100_and_million_song_datasets.md
new file mode 100644
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+## Datasets Available on AFS
+
+For your convinience we provided data providers for cifar10/100 and million song dataset. Below you can find
+information on the datasets and the AFS paths where one can find them.
+
+## CIFAR-10 and CIFAR-100 datasets
+
+[CIFAR-10 and CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) are a pair of image classification datasets collected by collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. They are labelled subsets of the much larger [80 million tiny images](dataset). They are a common benchmark task for image classification - a list of current accuracy benchmarks for both data sets are maintained by Rodrigo Benenson [here](http://rodrigob.github.io/are_we_there_yet/build/).
+
+As the name suggests, CIFAR-10 has images in 10 classes:
+
+ airplane
+ automobile
+ bird
+ cat
+ deer
+ dog
+ frog
+ horse
+ ship
+ truck
+
+with 6000 images per class for an overall dataset size of 60000. Each image has three (RGB) colour channels and pixel dimension 32×32, corresponding to a total dimension per input image of 3×32×32=3072. For each colour channel the input values have been normalised to the range [0, 1].
+
+CIFAR-100 has images of identical dimensions to CIFAR-10 but rather than 10 classes they are instead split across 100 fine-grained classes (and 20 coarser 'superclasses' comprising multiple finer classes):
+
+
+
+
Superclass
+
Classes
+
+
+
aquatic mammals
+
beaver, dolphin, otter, seal, whale
+
+
+
fish
+
aquarium fish, flatfish, ray, shark, trout
+
+
+
flowers
+
orchids, poppies, roses, sunflowers, tulips
+
+
+
food containers
+
bottles, bowls, cans, cups, plates
+
+
+
fruit and vegetables
+
apples, mushrooms, oranges, pears, sweet peppers
+
+
+
household electrical devices
+
clock, computer keyboard, lamp, telephone, television
+
+
+
household furniture
+
bed, chair, couch, table, wardrobe
+
+
+
insects
+
bee, beetle, butterfly, caterpillar, cockroach
+
+
+
large carnivores
+
bear, leopard, lion, tiger, wolf
+
+
+
large man-made outdoor things
+
bridge, castle, house, road, skyscraper
+
+
+
large natural outdoor scenes
+
cloud, forest, mountain, plain, sea
+
+
+
large omnivores and herbivores
+
camel, cattle, chimpanzee, elephant, kangaroo
+
+
+
medium-sized mammals
+
fox, porcupine, possum, raccoon, skunk
+
+
+
non-insect invertebrates
+
crab, lobster, snail, spider, worm
+
+
+
people
+
baby, boy, girl, man, woman
+
+
+
reptiles
+
crocodile, dinosaur, lizard, snake, turtle
+
+
+
small mammals
+
hamster, mouse, rabbit, shrew, squirrel
+
+
+
trees
+
maple, oak, palm, pine, willow
+
+
+
vehicles 1
+
bicycle, bus, motorcycle, pickup truck, train
+
+
+
vehicles 2
+
lawn-mower, rocket, streetcar, tank, tractor
+
+
+
+Each class has 600 examples in it, giving an overall dataset size of 60000 i.e. the same as CIFAR-10.
+
+Both CIFAR-10 and CIFAR-100 have standard splits into 50000 training examples and 10000 test examples. For CIFAR-100 as there is an optional Kaggle competition (see below) scored on predictions on the test set, we have used a non-standard assignation of examples to test and training set and only provided the inputs (and not target labels) for the 10000 examples chosen for the test set.
+
+For CIFAR-10 the 10000 test set examples have labels provided: to avoid any accidental over-fitting to the test set **you should only use these for the final evaluation of your model(s)**. If you repeatedly evaluate models on the test set during model development it is easy to end up indirectly fitting to the test labels - for those who have not already read it see this [excellent cautionary note from the MLPR notes by Iain Murray](http://www.inf.ed.ac.uk/teaching/courses/mlpr/2016/notes/w2a_train_test_val.html#warning-dont-fool-yourself-or-make-a-fool-of-yourself).
+
+For both CIFAR-10 and CIFAR-100, the remaining 50000 non-test examples have been split in to a 40000 example training dataset and a 10000 example validation dataset, each with target labels provided. If you wish to use a more complex cross-fold validation scheme you may want to combine these two portions of the dataset and define your own functions for separating out a validation set.
+
+Data provider classes for both CIFAR-10 and CIFAR-100 are available in the `mlp.data_providers` module. Both have similar behaviour to the `MNISTDataProvider` used extensively last semester. A `which_set` argument can be used to specify whether to return a data provided for the training dataset (`which_set='train'`) or validation dataset (`which_set='valid'`).
+
+The CIFAR-100 data provider also takes an optional `use_coarse_targets` argument in its constructor. By default this is set to `False` and the targets returned by the data provider correspond to 1-of-K encoded binary vectors for the 100 fine-grained object classes. If `use_coarse_targets=True` then instead the data provider will return 1-of-K encoded binary vector targets for the 20 coarse-grained superclasses associated with each input instead.
+
+Both data provider classes provide a `label_map` attribute which is a list of strings which are the class labels corresponding to the integer targets (i.e. prior to conversion to a 1-of-K encoded binary vector).
+
+### Accessing the CIFAR-10 and CIFAR-100 data
+
+Before using the data provider objects you will need to make sure the data files are accessible to the `mlp` package by existing under the directory specified by the `MLP_DATA_DIR` path.
+
+The data is available as compressed NumPy `.npz` files in the AFS directory `/afs/inf.ed.ac.uk/group/teaching/mlp/data/2017-18/`.
+
+If you are working on DICE one option is to redefine your `MLP_DATA_DIR` to directly point to the shared AFS data directory by editing the `env_vars.sh` start up file for your environment. This will avoid using up your DICE quota by storing the data files in your homespace but may involve slower initial loading of the data on initialising the data providers if many people are trying access the same files at once. The environment variable can be redefined by running
+
+```
+gedit ~/miniconda3/envs/mlp/etc/conda/activate.d/env_vars.sh
+```
+
+in a terminal window (assuming you installed `miniconda3` to your home directory), and changing the line
+
+```
+export MLP_DATA_DIR=$HOME/mlpractical/data
+```
+
+to
+
+```
+export MLP_DATA_DIR="`/afs/inf.ed.ac.uk/group/teaching/mlp/data/2017-18/`"
+```
+
+and then saving and closing the editor. You will need reload the `mlp` environment using `source activate mlp` and restart the Jupyter notebook server in the reloaded environment for the new environment variable definition to be available.
+
+For those working on DICE who have sufficient quota remaining or those using there own machine, an alternative option is to copy the data files in to your local `mlp/data` directory (or wherever your `MLP_DATA_DIR` environment variable currently points to if different).
+
+
+Assuming your local `mlpractical` repository is in your home directory you should be able to copy the required files on DICE by running
+
+```
+cp `/afs/inf.ed.ac.uk/group/teaching/mlp/data/2017-18/cifar*.npz ~/mlpractical/data
+```
+
+On a non-DICE machine, you will need to either [set up local access to AFS](http://computing.help.inf.ed.ac.uk/informatics-filesystem), use a remote file transfer client like `scp` or you can alternatively download the files using the iFile web interface [here](https://ifile.inf.ed.ac.uk/?path=%2Fafs%2Finf.ed.ac.uk%2Fgroup%2Fteaching%2Fmlp%2Fdata&goChange=Go) (requires DICE credentials).
+
+As some of the files are quite large you may wish to copy only those you are using currently (e.g. only the files for one of the two tasks) to your local filespace to avoid filling up your quota. The `cifar-100-test-inputs.npz` file will only be needed by those intending to enter the associated optional Kaggle competition.
+
+## Genre classification with the Million Song Dataset
+
+The [Million Song Dataset](http://labrosa.ee.columbia.edu/millionsong/) is a
+
+> freely-available collection of audio features and metadata for a million contemporary popular music tracks
+
+originally collected and compiled by Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere.
+
+The dataset is intended to encourage development of algorithms in the field of [music information retrieval](https://en.wikipedia.org/wiki/Music_information_retrieval). The [data for each track](http://labrosa.ee.columbia.edu/millionsong/pages/example-track-description) includes both textual features such as artist and album names, numerical descriptors such as duration and various audio features derived using a music analysis platform provided by [The Echo Nest](https://en.wikipedia.org/wiki/The_Echo_Nest) (since acquired by Spotify). Of the various audio features and segmentations included in the full dataset, the most detailed information is included at a 'segment' level: each segment corresponds to an automatically identified 'quasi-stable music event' - roughly contiguous sections of the audio with similar perceptual quality. The number of segments per track is variable and each segment can itself be of variable length - typically they seem to be around 0.2 - 0.4 seconds but can be as long as 10 seconds or more.
+
+For each segment of the track various extracted audio features are available - a 12 dimensional vector of [chroma features](https://en.wikipedia.org/wiki/Chroma_feature), a 12 dimensional vector of ['MFCC-like'](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum) timbre features and various measures of the loudness of the segment, including loudness at the segment start and maximum loudness. In the version of the data we provide, we include a 25 dimensional vector for each included segment, consisting of the 12 timbre features, 12 chroma features and loudness at start of segment concatenated in that order. To allow easier integration in to standard feedforward models, the basic version of the data we provide includes features only for a fixed length crop of the central 120 segments of each track (with tracks with less than 120 segments therefore not being included). This gives an overall input dimension per track of 120×25=3000. Each of the 3000 input dimensions has been been preprocessed by subtracting the per-dimension mean across the training data and dividing by the per-dimension standard deviation across the training data.
+
+We provide data providers for the fixed length crops versions of the input features, with the inputs being returned in batches of 3000 dimensional vectors (these can be reshaped to (120, 25) to get the per-segment features). To allow for more complex variable-length sequence modelling with for example recurrent neural networks, we also provide a variable length version of the data. This is only provided as compressed NumPy (`.npz`) data files rather than data provider objects - you will need to write your own data provider if you wish to use this version of the data. As the inputs are of variable number of segments they have been ['bucketed'](https://www.tensorflow.org/tutorials/seq2seq/#bucketing_and_padding) into groups of similar maximum length, with the following binning scheme used:
+
+ 120 - 250 segments
+ 251 - 500 segments
+ 501 - 650 segments
+ 651 - 800 segments
+ 801 - 950 segments
+ 951 - 1200 segments
+ 1201 - 2000 segments
+ 2000 - 4000 segments
+
+For each bucket the NumPy data files include inputs and targets arrays with second dimension equal to the maximum sgement size in the bucket (e.g. 250 for the bucket) and first dimension equal to the number of tracks with number of segments in that bucket. These are named `inputs_{n}` and `targets_{n}` in the data file where `{n}` is the maximal number of segments in the bucket e.g. `inputs_250` and `targets_250` for the first bucket. For tracks with less segments than the maximum size in the bucket, the features for the track have been padded with `NaN` values. For tracks with more segments than the maximum bucket size of 4000, only the first 4000 segments have been included.
+
+To allow you to match tracks between the fixed length and variable length datasets, the data files also include an array for each bucket giving the indices of the corresponding track in the fixed length input arrays. For example the array `indices_250` will be an array of the same size as the first dimension of `inputs_250` and `targets_250` with the first element of `indices_250` giving the index into the `inputs` and `targets` array of the fixed length data corresponding to first element of `inputs_250` and `targets_250`.
+
+The Million Song Dataset in its original form does not provide any genre labels, however various external groups have proposed genre labels for portions of the data by cross-referencing the track IDs against external music tagging databases. Analagously to the provision of both simpler and more complex classifications tasks for the CIFAR-10 / CIFAR-100 datasets, we provide two classification task datasets derived from the Million Song Dataset - one with 10 coarser level genre classes, and another with 25 finer-grained genre / style classifications.
+
+The 10-genre classification task uses the [*CD2C tagtraum genre annotations*](http://www.tagtraum.com/msd_genre_datasets.html) derived from multiple source databases (beaTunes genre dataset, Last.fm dataset, Top-MAGD dataset), with the *CD2C* variant using only non-ambiguous annotations (i.e. not including tracks with multiple genre labels). Of the 15 genre labels provided in the CD2C annotations, 5 (World, Latin, Punk, Folk and New Age) were not included due to having fewer than 5000 examples available. This left 10 remaining genre classes:
+
+ Rap
+ Rock
+ RnB
+ Electronic
+ Metal
+ Blues
+ Pop
+ Jazz
+ Country
+ Reggae
+
+For each of these 10 classes, 5000 labelled examples have been collected for training / validation (i.e. 50000 example in total) and a further 1000 example per class for testing, with the exception of the `Blues` class for which only 991 testing examples are provided due to there being insufficient labelled tracks of the minimum required length (i.e. a total of 9991 test examples).
+
+The 9991 test set examples have labels provided: however to avoid any accidental over-fitting to the test set **you should only use these for the final evaluation of your model(s)**. If you repeatedly evaluate models on the test set during model development it is easy to end up indirectly fitting to the test labels - for those who have not already read it see this [excellent cautionary note int the MLPR notes by Iain Murray](http://www.inf.ed.ac.uk/teaching/courses/mlpr/2016/notes/w2a_train_test_val.html#warning-dont-fool-yourself-or-make-a-fool-of-yourself).
+
+
+The 25-genre classification tasks uses the [*MSD Allmusic Style Dataset*](http://www.ifs.tuwien.ac.at/mir/msd/MASD.html) labels derived from the [AllMusic.com](http://www.allmusic.com/) database by [Alexander Schindler, Rudolf Mayer and Andreas Rauber of Vienna University of Technology](http://www.ifs.tuwien.ac.at/~schindler/pubs/ISMIR2012.pdf). The 25 genre / style labels used are:
+
+ Big Band
+ Blues Contemporary
+ Country Traditional
+ Dance
+ Electronica
+ Experimental
+ Folk International
+ Gospel
+ Grunge Emo
+ Hip Hop Rap
+ Jazz Classic
+ Metal Alternative
+ Metal Death
+ Metal Heavy
+ Pop Contemporary
+ Pop Indie
+ Pop Latin
+ Punk
+ Reggae
+ RnB Soul
+ Rock Alternative
+ Rock College
+ Rock Contemporary
+ Rock Hard
+ Rock Neo Psychedelia
+
+For each of these 25 classes, 2000 labelled examples have been collected for training / validation (i.e. 50000 example in total). A further 400 example per class have been collected for testing (i.e. 10000 examples in total), which you are provided inputs but not targets for. The optional Kaggle competition being run for this dataset (see email) is scored based on the 25-genre class label predictions on these unlabelled test inputs.
+
+The tracks used for the 25-genre classification task only partially overlap with those used for the 10-genre classification task and we do not provide any mapping between the two.
+
+For each of the two tasks, the 50000 examples collected for training have been pre-split in to a 40000 example training dataset and a 10000 example validation dataset. If you wish to use a more complex cross-fold validation scheme you may want to combine these two portions of the dataset and define your own functions / classes for separating out a validation set.
+
+Data provider classes for both fixed length input data for the 10 and 25 genre classification tasks in the `mlp.data_providers` module as `MSD10GenreDataProvider` and `MSD25GenreDataProvider`. Both have similar behaviour to the `MNISTDataProvider` used extensively last semester. A `which_set` argument can be used to specify whether to return a data provided for the training dataset (`which_set='train'`) or validation dataset (`which_set='valid'`). Both data provider classes provide a `label_map` attribute which is a list of strings which are the class labels corresponding to the integer targets (i.e. prior to conversion to a 1-of-K encoded binary vector).
+
+The test dataset files for the 10 genre classification task are provided as two separate NumPy data files `msd-10-genre-test-inputs.npz` and `msd-10-genre-test-targets.npz`. These can be loaded using [`np.load`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.html) function. The inputs are stored as a $10000\times3000$ array under the key `inputs` in the file `msd-10-genre-test-inputs.npz` and the targets in a 10000 element array of integer labels under the key `targets` in `msd-10-genre-test-targets.npz`. A corresponding `msd-25-genre-test-inputs.npz` file is provided for the 25 genre task inputs.
+
+### Accessing the Million Song Dataset data
+
+Before using the data provider objects you will need to make sure the data files are accessible to the `mlp` package by existing under the directory specified by the `MLP_DATA_DIR` path.
+
+The fixed length input data and associated targets is available as compressed NumPy `.npz` files in the AFS directory ``/afs/inf.ed.ac.uk/group/teaching/mlp/data/2017-18/``.
+
+If you are working on DICE one option is to redefine your `MLP_DATA_DIR` to directly point to the shared AFS data directory by editing the `env_vars.sh` start up file for your environment. This will avoid using up your DICE quota by storing the data files in your homespace but may involve slower initial loading of the data on initialising the data providers if many people are trying access the same files at once. The environment variable can be redefined by running
+
+```
+gedit ~/miniconda3/envs/mlp/etc/conda/activate.d/env_vars.sh
+```
+
+in a terminal window (assuming you installed `miniconda3` to your home directory), and changing the line
+
+```
+export MLP_DATA_DIR=$HOME/mlpractical/data
+```
+
+to
+
+```
+export MLP_DATA_DIR="/afs/inf.ed.ac.uk/group/teaching/mlp/data/2017-18/"
+```
+
+and then saving and closing the editor. You will need reload the `mlp` environment using `source activate mlp` and restart the Jupyter notebook server in the reloaded environment for the new environment variable definition to be available.
+
+Assuming your local `mlpractical` repository is in your home directory you should be able to copy the required files on DICE by running
+
+```
+cp `/afs/inf.ed.ac.uk/group/teaching/mlp/data/2017-18/msd*.npz ~/mlpractical/data
+```
+
+On a non-DICE machine, you will need to either [set up local access to AFS](http://computing.help.inf.ed.ac.uk/informatics-filesystem), use a remote file transfer client like `scp` or you can alternatively download the files using the iFile web interface [here](https://ifile.inf.ed.ac.uk/?path=%2Fafs%2Finf.ed.ac.uk%2Fgroup%2Fteaching%2Fmlp%2Fdata&goChange=Go) (requires DICE credentials).
+
+As some of the files are quite large you may wish to copy only those you are using currently (e.g. only the files for one of the two tasks) to your local filespace to avoid filling up your quota. The `cifar-100-test-inputs.npz` file will only be needed by those intending to enter the associated optional Kaggle competition.
diff --git a/notes/gpu-cluster-quick-start.md b/notes/gpu-cluster-quick-start.md
new file mode 100644
index 0000000..5a4fb47
--- /dev/null
+++ b/notes/gpu-cluster-quick-start.md
@@ -0,0 +1,166 @@
+# GPU Cluster Quick-Start Guide
+
+This guide is intended to guide students into the basics of using the mlp1/mlp2 GPU clusters. It is not intended to be
+an exhaustive guide that goes deep into micro-details of the Slurm ecosystem. For an exhaustive guide please visit
+[the Slurm Documentation page.](https://slurm.schedmd.com/)
+
+## What is the GPU Cluster?
+It's cluster consisting of server rack machines, each equipped with 8 NVIDIA 1060 GTX 6GB. Initially there are 9 servers (72 GPUs) available for use, during February this should grow up to 25 servers (200 GPUs). The system has is managed using the open source cluster management software named
+ [Slurm](https://slurm.schedmd.com/overview.html). Slurm has various advantages over the competition, including full
+ support of GPU resource scheduling.
+
+## Why do I need it?
+Most Deep Learning experiments require a large amount of compute as you have noticed in term 1. Usage of GPU can
+accelerate experiments around 30-50x therefore making experiments that require a large amount of time feasible by
+slashing their runtimes down by a massive factor. For a simple example consider an experiment that required a month to
+run, that would make it infeasible to actually do research with. Now consider that experiment only requiring 1 day to
+run, which allows one to iterate over methodologies, tune hyperparameters and overall try far more things. This simple
+example expresses one of the simplest reasons behind the GPU hype that surrounds machine learning research today.
+
+## Getting Started
+
+### Accessing the Cluster:
+1. If you are not on a DICE machine, then ssh into your dice home using ```ssh sxxxxxx@student.ssh.inf.ed.ac.uk```
+2. Then ssh into either mlp1 or mlp2 which are the headnodes of the GPU cluster - it does not matter which you use. To do that
+ run ```ssh mlp1``` or ```ssh mlp2```.
+3. You are now logged into the gpu cluster. If this is your first time logging in you'll need to build your environment. This is because your home directory on the GPU cluster is separate to your usual AFS home directory on DICE.
+
+### Installing requirements:
+1. Start by downloading the miniconda3 installation file using
+ ```wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh```.
+2. Now run the installation using ```bash Miniconda3-latest-Linux-x86_64.sh```. At the first prompt reply yes.
+```
+Do you accept the license terms? [yes|no]
+[no] >>> yes
+```
+3. At the second prompt simply press enter.
+```
+Miniconda3 will now be installed into this location:
+/home/sxxxxxxx/miniconda3
+
+ - Press ENTER to confirm the location
+ - Press CTRL-C to abort the installation
+ - Or specify a different location below
+```
+4. Now you need to activate your environment by first running:
+```source .bashrc```.
+This reloads .bashrc which includes the new miniconda path.
+5. Run ```source activate``` to load miniconda root.
+6. Now run ```conda create -n mlp python=3``` this will create the mlp environment. At the prompt choose y.
+7. Now run ```source activate mlp```.
+8. Install git using```conda install git```. Then config git using:
+```
+git config --global user.name "[your name]"
+git config --global user.email "[matric-number]@sms.ed.ac.uk"
+```
+9. Now clone the mlpractical repo using ```git clone https://github.com/CSTR-Edinburgh/mlpractical.git```.
+10. Checkout the semester_2 branch using ```git checkout mlp2017-8/semester_2_materials```.
+11. ```cd mlpractical``` and then install the required packages using ```pip install -r requirements_gpu.txt```.
+12. Once this is done you will need to setup the MLP_DATA path using the following block of commands:
+```bash
+cd ~/miniconda3/envs/mlp
+mkdir -p ./etc/conda/activate.d
+mkdir -p ./etc/conda/deactivate.d
+echo -e '#!/bin/sh\n' >> ./etc/conda/activate.d/env_vars.sh
+echo "export MLP_DATA_DIR=$HOME/mlpractical/data" >> ./etc/conda/activate.d/env_vars.sh
+echo -e '#!/bin/sh\n' >> ./etc/conda/deactivate.d/env_vars.sh
+echo 'unset MLP_DATA_DIR' >> ./etc/conda/deactivate.d/env_vars.sh
+export MLP_DATA_DIR=$HOME/mlpractical/data
+
+```
+
+13. This includes all of the required installations. Proceed to the next section outlining how to use the slurm cluster
+ management software. Please remember to clean your setup files using ```conda clean -t```
+
+### Using Slurm
+Slurm provides us with some commands that can be used to submit, delete, view, explore current jobs, nodes and resources among others.
+To submit a job one needs to use ```sbatch script.sh``` which will automatically find available nodes and pass the job,
+ resources and restrictions required. The script.sh is the bash script containing the job that we want to run. Since we will be using the NVIDIA CUDA and CUDNN libraries
+ we have provided a sample script which should be used for your job submissions. The script is explained in detail below:
+
+```bash
+#!/bin/sh
+#SBATCH -N 1 # nodes requested
+#SBATCH -n 1 # tasks requested
+#SBATCH --gres=gpu:1 # use 1 GPU
+#SBATCH --mem=16000 # memory in Mb
+#SBATCH -o outfile # send stdout to outfile
+#SBATCH -e errfile # send stderr to errfile
+#SBATCH -t 1:00:00 # time requested in hour:minute:seconds
+
+# Setup CUDA and CUDNN related paths
+export CUDA_HOME=/opt/cuda-8.0.44
+
+export CUDNN_HOME=/opt/cuDNN-6.0_8.0
+
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:$LIBRARY_PATH
+
+export CPATH=${CUDNN_HOME}/include:$CPATH
+
+export PATH=${CUDA_HOME}/bin:${PATH}
+
+export PYTHON_PATH=$PATH
+
+# Setup a folder in the very fast scratch disk which can be used for storing experiment objects and any other files
+# that may require storage during execution.
+mkdir -p /disk/scratch/${STUDENT_ID}
+
+export TMPDIR=/disk/scratch/${STUDENT_ID}/
+export TMP=/disk/scratch/${STUDENT_ID}/
+
+# Activate the relevant virtual environment:
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp
+
+# Run the python script that will train our network
+python network_trainer.py --batch_size 128 --epochs 200 --experiment_prefix vgg-net-emnist-sample-exp --dropout_rate 0.4 --batch_norm_use True --strided_dim_reduction True --seed 25012018
+
+```
+
+To actually run this use ```sbatch gpu_cluster_tutorial_training_script.sh```. When you do this, the job will be submitted and you will be given a job id.
+```bash
+[burly]sxxxxxxx: sbatch gpu_cluster_tutorial_training_script.sh
+Submitted batch job 147
+
+```
+
+To view a list of all running jobs use ```squeue``` for a minimal presentation and ```smap``` for a more involved presentation. Furthermore to view node information use ```sinfo```.
+```bash
+squeue
+ JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
+ 143 interacti bash iainr R 8:00 1 landonia05
+ 147 interacti gpu_clus sxxxxxxx R 1:05 1 landonia02
+
+```
+Also in case you want to stop/delete a job use ```scancel job_id``` where job_id is the id of the job.
+
+Furthermore in case you want to test some of your code interactively to prototype your solution before you submit it to
+ a node you can use ```srun -p interactive --gres=gpu:2 --pty python my_code_exp.py```.
+
+## Slurm Cheatsheet
+For a nice list of most commonly used Slurm commands please visit [here](https://bitsanddragons.wordpress.com/2017/04/12/slurm-user-cheatsheet/).
+
+## Syncing or copying data over to DICE
+
+At some point you will need to copy your data to DICE so you can analyse them and produce charts, write reports, store for future use etc.
+To do that there is a couple of ways:
+1. If you want to get your files while you are in dice simply run ```scp mlp1:/home//output output``` where username is the student id
+ and output is the file you want to copy. Use scp -r for folders. Furthermore you might want to just selectively sync
+ only new files. You can achieve that via syncing using rsync.
+ ```rsync -ua --progress mlp1:/home//project_dir target_dir```. The option -u updates only changed files, -a will pack the files before sending and --progress will give you a progress bar that shows what is being sent and how fast.
+rsync is useful when you write code remotely and want to push it to the cluster, since it can track files and automatically update changed files it saves both compute time and human time, because you won't have to spent time figuring out what to send.
+
+2. If you want to send your files while in mlp1-2 to dice. First run ```renc``` give your password and enter. Then run:
+```
+cp ~/output /afs/inf.ed.ac.uk/u/s/
+```
+
+This should directly copy the files to AFS. Furthermore one can use rsync as shown before.
+
+## Additional Help
+
+If you require additional help as usual please post on piazza or ask in the tech support helpdesk.
diff --git a/report/algorithm.sty b/report/algorithm.sty
deleted file mode 100644
index a723c1c..0000000
--- a/report/algorithm.sty
+++ /dev/null
@@ -1,79 +0,0 @@
-% ALGORITHM STYLE -- Released 8 April 1996
-% for LaTeX-2e
-% Copyright -- 1994 Peter Williams
-% E-mail Peter.Williams@dsto.defence.gov.au
-\NeedsTeXFormat{LaTeX2e}
-\ProvidesPackage{algorithm}
-\typeout{Document Style `algorithm' - floating environment}
-
-\RequirePackage{float}
-\RequirePackage{ifthen}
-\newcommand{\ALG@within}{nothing}
-\newboolean{ALG@within}
-\setboolean{ALG@within}{false}
-\newcommand{\ALG@floatstyle}{ruled}
-\newcommand{\ALG@name}{Algorithm}
-\newcommand{\listalgorithmname}{List of \ALG@name s}
-
-% Declare Options
-% first appearance
-\DeclareOption{plain}{
- \renewcommand{\ALG@floatstyle}{plain}
-}
-\DeclareOption{ruled}{
- \renewcommand{\ALG@floatstyle}{ruled}
-}
-\DeclareOption{boxed}{
- \renewcommand{\ALG@floatstyle}{boxed}
-}
-% then numbering convention
-\DeclareOption{part}{
- \renewcommand{\ALG@within}{part}
- \setboolean{ALG@within}{true}
-}
-\DeclareOption{chapter}{
- \renewcommand{\ALG@within}{chapter}
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-}
-\DeclareOption{section}{
- \renewcommand{\ALG@within}{section}
- \setboolean{ALG@within}{true}
-}
-\DeclareOption{subsection}{
- \renewcommand{\ALG@within}{subsection}
- \setboolean{ALG@within}{true}
-}
-\DeclareOption{subsubsection}{
- \renewcommand{\ALG@within}{subsubsection}
- \setboolean{ALG@within}{true}
-}
-\DeclareOption{nothing}{
- \renewcommand{\ALG@within}{nothing}
- \setboolean{ALG@within}{true}
-}
-\DeclareOption*{\edef\ALG@name{\CurrentOption}}
-
-% ALGORITHM
-%
-\ProcessOptions
-\floatstyle{\ALG@floatstyle}
-\ifthenelse{\boolean{ALG@within}}{
- \ifthenelse{\equal{\ALG@within}{part}}
- {\newfloat{algorithm}{htbp}{loa}[part]}{}
- \ifthenelse{\equal{\ALG@within}{chapter}}
- {\newfloat{algorithm}{htbp}{loa}[chapter]}{}
- \ifthenelse{\equal{\ALG@within}{section}}
- {\newfloat{algorithm}{htbp}{loa}[section]}{}
- \ifthenelse{\equal{\ALG@within}{subsection}}
- {\newfloat{algorithm}{htbp}{loa}[subsection]}{}
- \ifthenelse{\equal{\ALG@within}{subsubsection}}
- {\newfloat{algorithm}{htbp}{loa}[subsubsection]}{}
- \ifthenelse{\equal{\ALG@within}{nothing}}
- {\newfloat{algorithm}{htbp}{loa}}{}
-}{
- \newfloat{algorithm}{htbp}{loa}
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-\floatname{algorithm}{\ALG@name}
-
-\newcommand{\listofalgorithms}{\listof{algorithm}{\listalgorithmname}}
-
diff --git a/report/algorithmic.sty b/report/algorithmic.sty
deleted file mode 100644
index e2502a6..0000000
--- a/report/algorithmic.sty
+++ /dev/null
@@ -1,201 +0,0 @@
-% ALGORITHMIC STYLE -- Released 8 APRIL 1996
-% for LaTeX version 2e
-% Copyright -- 1994 Peter Williams
-% E-mail PeterWilliams@dsto.defence.gov.au
-%
-% Modified by Alex Smola (08/2000)
-% E-mail Alex.Smola@anu.edu.au
-%
-\NeedsTeXFormat{LaTeX2e}
-\ProvidesPackage{algorithmic}
-\typeout{Document Style `algorithmic' - environment}
-%
-\RequirePackage{ifthen}
-\RequirePackage{calc}
-\newboolean{ALC@noend}
-\setboolean{ALC@noend}{false}
-\newcounter{ALC@line}
-\newcounter{ALC@rem}
-\newlength{\ALC@tlm}
-%
-\DeclareOption{noend}{\setboolean{ALC@noend}{true}}
-%
-\ProcessOptions
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-% ALGORITHMIC
-\newcommand{\algorithmicrequire}{\textbf{Require:}}
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-%changed by alex smola
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-%end changed by alex smola
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-%changes by alex smola
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- \newenvironment{ALC@if}{\begin{ALC@g}}{\end{ALC@g}}
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-\ALC@com{##1}\begin{ALC@if}}
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-\ALC@com{##1}\begin{ALC@for}}
- \newcommand{\FORALL}[2][default]{\ALC@it\algorithmicforall\ ##2\ %
-\algorithmicdo%
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- \newcommand{\SHORTFORALL}[2]{\ALC@it\algorithmicforall\ ##1\ %
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-\ALC@com{##1}\begin{ALC@whl}}
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-%changed by alex smola
- \newcommand{\FUNCTION}[2][default]{\ALC@it\algorithmicfunction\ ##2\ %
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diff --git a/report/example-refs.bib b/report/example-refs.bib
deleted file mode 100644
index a9565fe..0000000
--- a/report/example-refs.bib
+++ /dev/null
@@ -1,75 +0,0 @@
-@inproceedings{langley00,
- author = {P. Langley},
- title = {Crafting Papers on Machine Learning},
- year = {2000},
- pages = {1207--1216},
- editor = {Pat Langley},
- booktitle = {Proceedings of the 17th International Conference
- on Machine Learning (ICML 2000)},
- address = {Stanford, CA},
- publisher = {Morgan Kaufmann}
-}
-
-@TechReport{mitchell80,
- author = "T. M. Mitchell",
- title = "The Need for Biases in Learning Generalizations",
- institution = "Computer Science Department, Rutgers University",
- year = "1980",
- address = "New Brunswick, MA",
-}
-
-@phdthesis{kearns89,
- author = {M. J. Kearns},
- title = {Computational Complexity of Machine Learning},
- school = {Department of Computer Science, Harvard University},
- year = {1989}
-}
-
-@Book{MachineLearningI,
- editor = "R. S. Michalski and J. G. Carbonell and T.
- M. Mitchell",
- title = "Machine Learning: An Artificial Intelligence
- Approach, Vol. I",
- publisher = "Tioga",
- year = "1983",
- address = "Palo Alto, CA"
-}
-
-@Book{DudaHart2nd,
- author = "R. O. Duda and P. E. Hart and D. G. Stork",
- title = "Pattern Classification",
- publisher = "John Wiley and Sons",
- edition = "2nd",
- year = "2000"
-}
-
-@misc{anonymous,
- title= {Suppressed for Anonymity},
- author= {Author, N. N.},
- year= {2011},
-}
-
-@InCollection{Newell81,
- author = "A. Newell and P. S. Rosenbloom",
- title = "Mechanisms of Skill Acquisition and the Law of
- Practice",
- booktitle = "Cognitive Skills and Their Acquisition",
- pages = "1--51",
- publisher = "Lawrence Erlbaum Associates, Inc.",
- year = "1981",
- editor = "J. R. Anderson",
- chapter = "1",
- address = "Hillsdale, NJ"
-}
-
-
-@Article{Samuel59,
- author = "A. L. Samuel",
- title = "Some Studies in Machine Learning Using the Game of
- Checkers",
- journal = "IBM Journal of Research and Development",
- year = "1959",
- volume = "3",
- number = "3",
- pages = "211--229"
-}
\ No newline at end of file
diff --git a/report/fancyhdr.sty b/report/fancyhdr.sty
deleted file mode 100644
index 5a4d897..0000000
--- a/report/fancyhdr.sty
+++ /dev/null
@@ -1,485 +0,0 @@
-% fancyhdr.sty version 3.2
-% Fancy headers and footers for LaTeX.
-% Piet van Oostrum,
-% Dept of Computer and Information Sciences, University of Utrecht,
-% Padualaan 14, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands
-% Telephone: +31 30 2532180. Email: piet@cs.uu.nl
-% ========================================================================
-% LICENCE:
-% This file may be distributed under the terms of the LaTeX Project Public
-% License, as described in lppl.txt in the base LaTeX distribution.
-% Either version 1 or, at your option, any later version.
-% ========================================================================
-% MODIFICATION HISTORY:
-% Sep 16, 1994
-% version 1.4: Correction for use with \reversemargin
-% Sep 29, 1994:
-% version 1.5: Added the \iftopfloat, \ifbotfloat and \iffloatpage commands
-% Oct 4, 1994:
-% version 1.6: Reset single spacing in headers/footers for use with
-% setspace.sty or doublespace.sty
-% Oct 4, 1994:
-% version 1.7: changed \let\@mkboth\markboth to
-% \def\@mkboth{\protect\markboth} to make it more robust
-% Dec 5, 1994:
-% version 1.8: corrections for amsbook/amsart: define \@chapapp and (more
-% importantly) use the \chapter/sectionmark definitions from ps@headings if
-% they exist (which should be true for all standard classes).
-% May 31, 1995:
-% version 1.9: The proposed \renewcommand{\headrulewidth}{\iffloatpage...
-% construction in the doc did not work properly with the fancyplain style.
-% June 1, 1995:
-% version 1.91: The definition of \@mkboth wasn't restored on subsequent
-% \pagestyle{fancy}'s.
-% June 1, 1995:
-% version 1.92: The sequence \pagestyle{fancyplain} \pagestyle{plain}
-% \pagestyle{fancy} would erroneously select the plain version.
-% June 1, 1995:
-% version 1.93: \fancypagestyle command added.
-% Dec 11, 1995:
-% version 1.94: suggested by Conrad Hughes
-% CJCH, Dec 11, 1995: added \footruleskip to allow control over footrule
-% position (old hardcoded value of .3\normalbaselineskip is far too high
-% when used with very small footer fonts).
-% Jan 31, 1996:
-% version 1.95: call \@normalsize in the reset code if that is defined,
-% otherwise \normalsize.
-% this is to solve a problem with ucthesis.cls, as this doesn't
-% define \@currsize. Unfortunately for latex209 calling \normalsize doesn't
-% work as this is optimized to do very little, so there \@normalsize should
-% be called. Hopefully this code works for all versions of LaTeX known to
-% mankind.
-% April 25, 1996:
-% version 1.96: initialize \headwidth to a magic (negative) value to catch
-% most common cases that people change it before calling \pagestyle{fancy}.
-% Note it can't be initialized when reading in this file, because
-% \textwidth could be changed afterwards. This is quite probable.
-% We also switch to \MakeUppercase rather than \uppercase and introduce a
-% \nouppercase command for use in headers. and footers.
-% May 3, 1996:
-% version 1.97: Two changes:
-% 1. Undo the change in version 1.8 (using the pagestyle{headings} defaults
-% for the chapter and section marks. The current version of amsbook and
-% amsart classes don't seem to need them anymore. Moreover the standard
-% latex classes don't use \markboth if twoside isn't selected, and this is
-% confusing as \leftmark doesn't work as expected.
-% 2. include a call to \ps@empty in ps@@fancy. This is to solve a problem
-% in the amsbook and amsart classes, that make global changes to \topskip,
-% which are reset in \ps@empty. Hopefully this doesn't break other things.
-% May 7, 1996:
-% version 1.98:
-% Added % after the line \def\nouppercase
-% May 7, 1996:
-% version 1.99: This is the alpha version of fancyhdr 2.0
-% Introduced the new commands \fancyhead, \fancyfoot, and \fancyhf.
-% Changed \headrulewidth, \footrulewidth, \footruleskip to
-% macros rather than length parameters, In this way they can be
-% conditionalized and they don't consume length registers. There is no need
-% to have them as length registers unless you want to do calculations with
-% them, which is unlikely. Note that this may make some uses of them
-% incompatible (i.e. if you have a file that uses \setlength or \xxxx=)
-% May 10, 1996:
-% version 1.99a:
-% Added a few more % signs
-% May 10, 1996:
-% version 1.99b:
-% Changed the syntax of \f@nfor to be resistent to catcode changes of :=
-% Removed the [1] from the defs of \lhead etc. because the parameter is
-% consumed by the \@[xy]lhead etc. macros.
-% June 24, 1997:
-% version 1.99c:
-% corrected \nouppercase to also include the protected form of \MakeUppercase
-% \global added to manipulation of \headwidth.
-% \iffootnote command added.
-% Some comments added about \@fancyhead and \@fancyfoot.
-% Aug 24, 1998
-% version 1.99d
-% Changed the default \ps@empty to \ps@@empty in order to allow
-% \fancypagestyle{empty} redefinition.
-% Oct 11, 2000
-% version 2.0
-% Added LPPL license clause.
-%
-% A check for \headheight is added. An errormessage is given (once) if the
-% header is too large. Empty headers don't generate the error even if
-% \headheight is very small or even 0pt.
-% Warning added for the use of 'E' option when twoside option is not used.
-% In this case the 'E' fields will never be used.
-%
-% Mar 10, 2002
-% version 2.1beta
-% New command: \fancyhfoffset[place]{length}
-% defines offsets to be applied to the header/footer to let it stick into
-% the margins (if length > 0).
-% place is like in fancyhead, except that only E,O,L,R can be used.
-% This replaces the old calculation based on \headwidth and the marginpar
-% area.
-% \headwidth will be dynamically calculated in the headers/footers when
-% this is used.
-%
-% Mar 26, 2002
-% version 2.1beta2
-% \fancyhfoffset now also takes h,f as possible letters in the argument to
-% allow the header and footer widths to be different.
-% New commands \fancyheadoffset and \fancyfootoffset added comparable to
-% \fancyhead and \fancyfoot.
-% Errormessages and warnings have been made more informative.
-%
-% Dec 9, 2002
-% version 2.1
-% The defaults for \footrulewidth, \plainheadrulewidth and
-% \plainfootrulewidth are changed from \z@skip to 0pt. In this way when
-% someone inadvertantly uses \setlength to change any of these, the value
-% of \z@skip will not be changed, rather an errormessage will be given.
-
-% March 3, 2004
-% Release of version 3.0
-
-% Oct 7, 2004
-% version 3.1
-% Added '\endlinechar=13' to \fancy@reset to prevent problems with
-% includegraphics in header when verbatiminput is active.
-
-% March 22, 2005
-% version 3.2
-% reset \everypar (the real one) in \fancy@reset because spanish.ldf does
-% strange things with \everypar between << and >>.
-
-\def\ifancy@mpty#1{\def\temp@a{#1}\ifx\temp@a\@empty}
-
-\def\fancy@def#1#2{\ifancy@mpty{#2}\fancy@gbl\def#1{\leavevmode}\else
- \fancy@gbl\def#1{#2\strut}\fi}
-
-\let\fancy@gbl\global
-
-\def\@fancyerrmsg#1{%
- \ifx\PackageError\undefined
- \errmessage{#1}\else
- \PackageError{Fancyhdr}{#1}{}\fi}
-\def\@fancywarning#1{%
- \ifx\PackageWarning\undefined
- \errmessage{#1}\else
- \PackageWarning{Fancyhdr}{#1}{}\fi}
-
-% Usage: \@forc \var{charstring}{command to be executed for each char}
-% This is similar to LaTeX's \@tfor, but expands the charstring.
-
-\def\@forc#1#2#3{\expandafter\f@rc\expandafter#1\expandafter{#2}{#3}}
-\def\f@rc#1#2#3{\def\temp@ty{#2}\ifx\@empty\temp@ty\else
- \f@@rc#1#2\f@@rc{#3}\fi}
-\def\f@@rc#1#2#3\f@@rc#4{\def#1{#2}#4\f@rc#1{#3}{#4}}
-
-% Usage: \f@nfor\name:=list\do{body}
-% Like LaTeX's \@for but an empty list is treated as a list with an empty
-% element
-
-\newcommand{\f@nfor}[3]{\edef\@fortmp{#2}%
- \expandafter\@forloop#2,\@nil,\@nil\@@#1{#3}}
-
-% Usage: \def@ult \cs{defaults}{argument}
-% sets \cs to the characters from defaults appearing in argument
-% or defaults if it would be empty. All characters are lowercased.
-
-\newcommand\def@ult[3]{%
- \edef\temp@a{\lowercase{\edef\noexpand\temp@a{#3}}}\temp@a
- \def#1{}%
- \@forc\tmpf@ra{#2}%
- {\expandafter\if@in\tmpf@ra\temp@a{\edef#1{#1\tmpf@ra}}{}}%
- \ifx\@empty#1\def#1{#2}\fi}
-%
-% \if@in
-%
-\newcommand{\if@in}[4]{%
- \edef\temp@a{#2}\def\temp@b##1#1##2\temp@b{\def\temp@b{##1}}%
- \expandafter\temp@b#2#1\temp@b\ifx\temp@a\temp@b #4\else #3\fi}
-
-\newcommand{\fancyhead}{\@ifnextchar[{\f@ncyhf\fancyhead h}%
- {\f@ncyhf\fancyhead h[]}}
-\newcommand{\fancyfoot}{\@ifnextchar[{\f@ncyhf\fancyfoot f}%
- {\f@ncyhf\fancyfoot f[]}}
-\newcommand{\fancyhf}{\@ifnextchar[{\f@ncyhf\fancyhf{}}%
- {\f@ncyhf\fancyhf{}[]}}
-
-% New commands for offsets added
-
-\newcommand{\fancyheadoffset}{\@ifnextchar[{\f@ncyhfoffs\fancyheadoffset h}%
- {\f@ncyhfoffs\fancyheadoffset h[]}}
-\newcommand{\fancyfootoffset}{\@ifnextchar[{\f@ncyhfoffs\fancyfootoffset f}%
- {\f@ncyhfoffs\fancyfootoffset f[]}}
-\newcommand{\fancyhfoffset}{\@ifnextchar[{\f@ncyhfoffs\fancyhfoffset{}}%
- {\f@ncyhfoffs\fancyhfoffset{}[]}}
-
-% The header and footer fields are stored in command sequences with
-% names of the form: \f@ncy with for [eo], from [lcr]
-% and from [hf].
-
-\def\f@ncyhf#1#2[#3]#4{%
- \def\temp@c{}%
- \@forc\tmpf@ra{#3}%
- {\expandafter\if@in\tmpf@ra{eolcrhf,EOLCRHF}%
- {}{\edef\temp@c{\temp@c\tmpf@ra}}}%
- \ifx\@empty\temp@c\else
- \@fancyerrmsg{Illegal char `\temp@c' in \string#1 argument:
- [#3]}%
- \fi
- \f@nfor\temp@c{#3}%
- {\def@ult\f@@@eo{eo}\temp@c
- \if@twoside\else
- \if\f@@@eo e\@fancywarning
- {\string#1's `E' option without twoside option is useless}\fi\fi
- \def@ult\f@@@lcr{lcr}\temp@c
- \def@ult\f@@@hf{hf}{#2\temp@c}%
- \@forc\f@@eo\f@@@eo
- {\@forc\f@@lcr\f@@@lcr
- {\@forc\f@@hf\f@@@hf
- {\expandafter\fancy@def\csname
- f@ncy\f@@eo\f@@lcr\f@@hf\endcsname
- {#4}}}}}}
-
-\def\f@ncyhfoffs#1#2[#3]#4{%
- \def\temp@c{}%
- \@forc\tmpf@ra{#3}%
- {\expandafter\if@in\tmpf@ra{eolrhf,EOLRHF}%
- {}{\edef\temp@c{\temp@c\tmpf@ra}}}%
- \ifx\@empty\temp@c\else
- \@fancyerrmsg{Illegal char `\temp@c' in \string#1 argument:
- [#3]}%
- \fi
- \f@nfor\temp@c{#3}%
- {\def@ult\f@@@eo{eo}\temp@c
- \if@twoside\else
- \if\f@@@eo e\@fancywarning
- {\string#1's `E' option without twoside option is useless}\fi\fi
- \def@ult\f@@@lcr{lr}\temp@c
- \def@ult\f@@@hf{hf}{#2\temp@c}%
- \@forc\f@@eo\f@@@eo
- {\@forc\f@@lcr\f@@@lcr
- {\@forc\f@@hf\f@@@hf
- {\expandafter\setlength\csname
- f@ncyO@\f@@eo\f@@lcr\f@@hf\endcsname
- {#4}}}}}%
- \fancy@setoffs}
-
-% Fancyheadings version 1 commands. These are more or less deprecated,
-% but they continue to work.
-
-\newcommand{\lhead}{\@ifnextchar[{\@xlhead}{\@ylhead}}
-\def\@xlhead[#1]#2{\fancy@def\f@ncyelh{#1}\fancy@def\f@ncyolh{#2}}
-\def\@ylhead#1{\fancy@def\f@ncyelh{#1}\fancy@def\f@ncyolh{#1}}
-
-\newcommand{\chead}{\@ifnextchar[{\@xchead}{\@ychead}}
-\def\@xchead[#1]#2{\fancy@def\f@ncyech{#1}\fancy@def\f@ncyoch{#2}}
-\def\@ychead#1{\fancy@def\f@ncyech{#1}\fancy@def\f@ncyoch{#1}}
-
-\newcommand{\rhead}{\@ifnextchar[{\@xrhead}{\@yrhead}}
-\def\@xrhead[#1]#2{\fancy@def\f@ncyerh{#1}\fancy@def\f@ncyorh{#2}}
-\def\@yrhead#1{\fancy@def\f@ncyerh{#1}\fancy@def\f@ncyorh{#1}}
-
-\newcommand{\lfoot}{\@ifnextchar[{\@xlfoot}{\@ylfoot}}
-\def\@xlfoot[#1]#2{\fancy@def\f@ncyelf{#1}\fancy@def\f@ncyolf{#2}}
-\def\@ylfoot#1{\fancy@def\f@ncyelf{#1}\fancy@def\f@ncyolf{#1}}
-
-\newcommand{\cfoot}{\@ifnextchar[{\@xcfoot}{\@ycfoot}}
-\def\@xcfoot[#1]#2{\fancy@def\f@ncyecf{#1}\fancy@def\f@ncyocf{#2}}
-\def\@ycfoot#1{\fancy@def\f@ncyecf{#1}\fancy@def\f@ncyocf{#1}}
-
-\newcommand{\rfoot}{\@ifnextchar[{\@xrfoot}{\@yrfoot}}
-\def\@xrfoot[#1]#2{\fancy@def\f@ncyerf{#1}\fancy@def\f@ncyorf{#2}}
-\def\@yrfoot#1{\fancy@def\f@ncyerf{#1}\fancy@def\f@ncyorf{#1}}
-
-\newlength{\fancy@headwidth}
-\let\headwidth\fancy@headwidth
-\newlength{\f@ncyO@elh}
-\newlength{\f@ncyO@erh}
-\newlength{\f@ncyO@olh}
-\newlength{\f@ncyO@orh}
-\newlength{\f@ncyO@elf}
-\newlength{\f@ncyO@erf}
-\newlength{\f@ncyO@olf}
-\newlength{\f@ncyO@orf}
-\newcommand{\headrulewidth}{0.4pt}
-\newcommand{\footrulewidth}{0pt}
-\newcommand{\footruleskip}{.3\normalbaselineskip}
-
-% Fancyplain stuff shouldn't be used anymore (rather
-% \fancypagestyle{plain} should be used), but it must be present for
-% compatibility reasons.
-
-\newcommand{\plainheadrulewidth}{0pt}
-\newcommand{\plainfootrulewidth}{0pt}
-\newif\if@fancyplain \@fancyplainfalse
-\def\fancyplain#1#2{\if@fancyplain#1\else#2\fi}
-
-\headwidth=-123456789sp %magic constant
-
-% Command to reset various things in the headers:
-% a.o. single spacing (taken from setspace.sty)
-% and the catcode of ^^M (so that epsf files in the header work if a
-% verbatim crosses a page boundary)
-% It also defines a \nouppercase command that disables \uppercase and
-% \Makeuppercase. It can only be used in the headers and footers.
-\let\fnch@everypar\everypar% save real \everypar because of spanish.ldf
-\def\fancy@reset{\fnch@everypar{}\restorecr\endlinechar=13
- \def\baselinestretch{1}%
- \def\nouppercase##1{{\let\uppercase\relax\let\MakeUppercase\relax
- \expandafter\let\csname MakeUppercase \endcsname\relax##1}}%
- \ifx\undefined\@newbaseline% NFSS not present; 2.09 or 2e
- \ifx\@normalsize\undefined \normalsize % for ucthesis.cls
- \else \@normalsize \fi
- \else% NFSS (2.09) present
- \@newbaseline%
- \fi}
-
-% Initialization of the head and foot text.
-
-% The default values still contain \fancyplain for compatibility.
-\fancyhf{} % clear all
-% lefthead empty on ``plain'' pages, \rightmark on even, \leftmark on odd pages
-% evenhead empty on ``plain'' pages, \leftmark on even, \rightmark on odd pages
-\if@twoside
- \fancyhead[el,or]{\fancyplain{}{\sl\rightmark}}
- \fancyhead[er,ol]{\fancyplain{}{\sl\leftmark}}
-\else
- \fancyhead[l]{\fancyplain{}{\sl\rightmark}}
- \fancyhead[r]{\fancyplain{}{\sl\leftmark}}
-\fi
-\fancyfoot[c]{\rm\thepage} % page number
-
-% Use box 0 as a temp box and dimen 0 as temp dimen.
-% This can be done, because this code will always
-% be used inside another box, and therefore the changes are local.
-
-\def\@fancyvbox#1#2{\setbox0\vbox{#2}\ifdim\ht0>#1\@fancywarning
- {\string#1 is too small (\the#1): ^^J Make it at least \the\ht0.^^J
- We now make it that large for the rest of the document.^^J
- This may cause the page layout to be inconsistent, however\@gobble}%
- \dimen0=#1\global\setlength{#1}{\ht0}\ht0=\dimen0\fi
- \box0}
-
-% Put together a header or footer given the left, center and
-% right text, fillers at left and right and a rule.
-% The \lap commands put the text into an hbox of zero size,
-% so overlapping text does not generate an errormessage.
-% These macros have 5 parameters:
-% 1. LEFTSIDE BEARING % This determines at which side the header will stick
-% out. When \fancyhfoffset is used this calculates \headwidth, otherwise
-% it is \hss or \relax (after expansion).
-% 2. \f@ncyolh, \f@ncyelh, \f@ncyolf or \f@ncyelf. This is the left component.
-% 3. \f@ncyoch, \f@ncyech, \f@ncyocf or \f@ncyecf. This is the middle comp.
-% 4. \f@ncyorh, \f@ncyerh, \f@ncyorf or \f@ncyerf. This is the right component.
-% 5. RIGHTSIDE BEARING. This is always \relax or \hss (after expansion).
-
-\def\@fancyhead#1#2#3#4#5{#1\hbox to\headwidth{\fancy@reset
- \@fancyvbox\headheight{\hbox
- {\rlap{\parbox[b]{\headwidth}{\raggedright#2}}\hfill
- \parbox[b]{\headwidth}{\centering#3}\hfill
- \llap{\parbox[b]{\headwidth}{\raggedleft#4}}}\headrule}}#5}
-
-\def\@fancyfoot#1#2#3#4#5{#1\hbox to\headwidth{\fancy@reset
- \@fancyvbox\footskip{\footrule
- \hbox{\rlap{\parbox[t]{\headwidth}{\raggedright#2}}\hfill
- \parbox[t]{\headwidth}{\centering#3}\hfill
- \llap{\parbox[t]{\headwidth}{\raggedleft#4}}}}}#5}
-
-\def\headrule{{\if@fancyplain\let\headrulewidth\plainheadrulewidth\fi
- \hrule\@height\headrulewidth\@width\headwidth \vskip-\headrulewidth}}
-
-\def\footrule{{\if@fancyplain\let\footrulewidth\plainfootrulewidth\fi
- \vskip-\footruleskip\vskip-\footrulewidth
- \hrule\@width\headwidth\@height\footrulewidth\vskip\footruleskip}}
-
-\def\ps@fancy{%
-\@ifundefined{@chapapp}{\let\@chapapp\chaptername}{}%for amsbook
-%
-% Define \MakeUppercase for old LaTeXen.
-% Note: we used \def rather than \let, so that \let\uppercase\relax (from
-% the version 1 documentation) will still work.
-%
-\@ifundefined{MakeUppercase}{\def\MakeUppercase{\uppercase}}{}%
-\@ifundefined{chapter}{\def\sectionmark##1{\markboth
-{\MakeUppercase{\ifnum \c@secnumdepth>\z@
- \thesection\hskip 1em\relax \fi ##1}}{}}%
-\def\subsectionmark##1{\markright {\ifnum \c@secnumdepth >\@ne
- \thesubsection\hskip 1em\relax \fi ##1}}}%
-{\def\chaptermark##1{\markboth {\MakeUppercase{\ifnum \c@secnumdepth>\m@ne
- \@chapapp\ \thechapter. \ \fi ##1}}{}}%
-\def\sectionmark##1{\markright{\MakeUppercase{\ifnum \c@secnumdepth >\z@
- \thesection. \ \fi ##1}}}}%
-%\csname ps@headings\endcsname % use \ps@headings defaults if they exist
-\ps@@fancy
-\gdef\ps@fancy{\@fancyplainfalse\ps@@fancy}%
-% Initialize \headwidth if the user didn't
-%
-\ifdim\headwidth<0sp
-%
-% This catches the case that \headwidth hasn't been initialized and the
-% case that the user added something to \headwidth in the expectation that
-% it was initialized to \textwidth. We compensate this now. This loses if
-% the user intended to multiply it by a factor. But that case is more
-% likely done by saying something like \headwidth=1.2\textwidth.
-% The doc says you have to change \headwidth after the first call to
-% \pagestyle{fancy}. This code is just to catch the most common cases were
-% that requirement is violated.
-%
- \global\advance\headwidth123456789sp\global\advance\headwidth\textwidth
-\fi}
-\def\ps@fancyplain{\ps@fancy \let\ps@plain\ps@plain@fancy}
-\def\ps@plain@fancy{\@fancyplaintrue\ps@@fancy}
-\let\ps@@empty\ps@empty
-\def\ps@@fancy{%
-\ps@@empty % This is for amsbook/amsart, which do strange things with \topskip
-\def\@mkboth{\protect\markboth}%
-\def\@oddhead{\@fancyhead\fancy@Oolh\f@ncyolh\f@ncyoch\f@ncyorh\fancy@Oorh}%
-\def\@oddfoot{\@fancyfoot\fancy@Oolf\f@ncyolf\f@ncyocf\f@ncyorf\fancy@Oorf}%
-\def\@evenhead{\@fancyhead\fancy@Oelh\f@ncyelh\f@ncyech\f@ncyerh\fancy@Oerh}%
-\def\@evenfoot{\@fancyfoot\fancy@Oelf\f@ncyelf\f@ncyecf\f@ncyerf\fancy@Oerf}%
-}
-% Default definitions for compatibility mode:
-% These cause the header/footer to take the defined \headwidth as width
-% And to shift in the direction of the marginpar area
-
-\def\fancy@Oolh{\if@reversemargin\hss\else\relax\fi}
-\def\fancy@Oorh{\if@reversemargin\relax\else\hss\fi}
-\let\fancy@Oelh\fancy@Oorh
-\let\fancy@Oerh\fancy@Oolh
-
-\let\fancy@Oolf\fancy@Oolh
-\let\fancy@Oorf\fancy@Oorh
-\let\fancy@Oelf\fancy@Oelh
-\let\fancy@Oerf\fancy@Oerh
-
-% New definitions for the use of \fancyhfoffset
-% These calculate the \headwidth from \textwidth and the specified offsets.
-
-\def\fancy@offsolh{\headwidth=\textwidth\advance\headwidth\f@ncyO@olh
- \advance\headwidth\f@ncyO@orh\hskip-\f@ncyO@olh}
-\def\fancy@offselh{\headwidth=\textwidth\advance\headwidth\f@ncyO@elh
- \advance\headwidth\f@ncyO@erh\hskip-\f@ncyO@elh}
-
-\def\fancy@offsolf{\headwidth=\textwidth\advance\headwidth\f@ncyO@olf
- \advance\headwidth\f@ncyO@orf\hskip-\f@ncyO@olf}
-\def\fancy@offself{\headwidth=\textwidth\advance\headwidth\f@ncyO@elf
- \advance\headwidth\f@ncyO@erf\hskip-\f@ncyO@elf}
-
-\def\fancy@setoffs{%
-% Just in case \let\headwidth\textwidth was used
- \fancy@gbl\let\headwidth\fancy@headwidth
- \fancy@gbl\let\fancy@Oolh\fancy@offsolh
- \fancy@gbl\let\fancy@Oelh\fancy@offselh
- \fancy@gbl\let\fancy@Oorh\hss
- \fancy@gbl\let\fancy@Oerh\hss
- \fancy@gbl\let\fancy@Oolf\fancy@offsolf
- \fancy@gbl\let\fancy@Oelf\fancy@offself
- \fancy@gbl\let\fancy@Oorf\hss
- \fancy@gbl\let\fancy@Oerf\hss}
-
-\newif\iffootnote
-\let\latex@makecol\@makecol
-\def\@makecol{\ifvoid\footins\footnotetrue\else\footnotefalse\fi
-\let\topfloat\@toplist\let\botfloat\@botlist\latex@makecol}
-\def\iftopfloat#1#2{\ifx\topfloat\empty #2\else #1\fi}
-\def\ifbotfloat#1#2{\ifx\botfloat\empty #2\else #1\fi}
-\def\iffloatpage#1#2{\if@fcolmade #1\else #2\fi}
-
-\newcommand{\fancypagestyle}[2]{%
- \@namedef{ps@#1}{\let\fancy@gbl\relax#2\relax\ps@fancy}}
diff --git a/report/icml2017.bst b/report/icml2017.bst
deleted file mode 100644
index c147918..0000000
--- a/report/icml2017.bst
+++ /dev/null
@@ -1,1441 +0,0 @@
-%% File: `icml2017.bst'
-%% A modification of `plainnl.bst' for use with natbib package
-%%
-%% Copyright 2010 Hal Daum\'e III
-%% Modified by J. Frnkranz
-%% - Changed labels from (X and Y, 2000) to (X & Y, 2000)
-%% - Changed References to last name first and abbreviated first names.
-%%
-%% Copyright 1993-2007 Patrick W Daly
-%% Max-Planck-Institut f\"ur Sonnensystemforschung
-%% Max-Planck-Str. 2
-%% D-37191 Katlenburg-Lindau
-%% Germany
-%% E-mail: daly@mps.mpg.de
-%%
-%% This program can be redistributed and/or modified under the terms
-%% of the LaTeX Project Public License Distributed from CTAN
-%% archives in directory macros/latex/base/lppl.txt; either
-%% version 1 of the License, or any later version.
-%%
- % Version and source file information:
- % \ProvidesFile{icml2010.mbs}[2007/11/26 1.93 (PWD)]
- %
- % BibTeX `plainnat' family
- % version 0.99b for BibTeX versions 0.99a or later,
- % for LaTeX versions 2.09 and 2e.
- %
- % For use with the `natbib.sty' package; emulates the corresponding
- % member of the `plain' family, but with author-year citations.
- %
- % With version 6.0 of `natbib.sty', it may also be used for numerical
- % citations, while retaining the commands \citeauthor, \citefullauthor,
- % and \citeyear to print the corresponding information.
- %
- % For version 7.0 of `natbib.sty', the KEY field replaces missing
- % authors/editors, and the date is left blank in \bibitem.
- %
- % Includes field EID for the sequence/citation number of electronic journals
- % which is used instead of page numbers.
- %
- % Includes fields ISBN and ISSN.
- %
- % Includes field URL for Internet addresses.
- %
- % Includes field DOI for Digital Object Idenfifiers.
- %
- % Works best with the url.sty package of Donald Arseneau.
- %
- % Works with identical authors and year are further sorted by
- % citation key, to preserve any natural sequence.
- %
-ENTRY
- { address
- author
- booktitle
- chapter
- doi
- eid
- edition
- editor
- howpublished
- institution
- isbn
- issn
- journal
- key
- month
- note
- number
- organization
- pages
- publisher
- school
- series
- title
- type
- url
- volume
- year
- }
- {}
- { label extra.label sort.label short.list }
-
-INTEGERS { output.state before.all mid.sentence after.sentence after.block }
-
-FUNCTION {init.state.consts}
-{ #0 'before.all :=
- #1 'mid.sentence :=
- #2 'after.sentence :=
- #3 'after.block :=
-}
-
-STRINGS { s t }
-
-FUNCTION {output.nonnull}
-{ 's :=
- output.state mid.sentence =
- { ", " * write$ }
- { output.state after.block =
- { add.period$ write$
- newline$
- "\newblock " write$
- }
- { output.state before.all =
- 'write$
- { add.period$ " " * write$ }
- if$
- }
- if$
- mid.sentence 'output.state :=
- }
- if$
- s
-}
-
-FUNCTION {output}
-{ duplicate$ empty$
- 'pop$
- 'output.nonnull
- if$
-}
-
-FUNCTION {output.check}
-{ 't :=
- duplicate$ empty$
- { pop$ "empty " t * " in " * cite$ * warning$ }
- 'output.nonnull
- if$
-}
-
-FUNCTION {fin.entry}
-{ add.period$
- write$
- newline$
-}
-
-FUNCTION {new.block}
-{ output.state before.all =
- 'skip$
- { after.block 'output.state := }
- if$
-}
-
-FUNCTION {new.sentence}
-{ output.state after.block =
- 'skip$
- { output.state before.all =
- 'skip$
- { after.sentence 'output.state := }
- if$
- }
- if$
-}
-
-FUNCTION {not}
-{ { #0 }
- { #1 }
- if$
-}
-
-FUNCTION {and}
-{ 'skip$
- { pop$ #0 }
- if$
-}
-
-FUNCTION {or}
-{ { pop$ #1 }
- 'skip$
- if$
-}
-
-FUNCTION {new.block.checka}
-{ empty$
- 'skip$
- 'new.block
- if$
-}
-
-FUNCTION {new.block.checkb}
-{ empty$
- swap$ empty$
- and
- 'skip$
- 'new.block
- if$
-}
-
-FUNCTION {new.sentence.checka}
-{ empty$
- 'skip$
- 'new.sentence
- if$
-}
-
-FUNCTION {new.sentence.checkb}
-{ empty$
- swap$ empty$
- and
- 'skip$
- 'new.sentence
- if$
-}
-
-FUNCTION {field.or.null}
-{ duplicate$ empty$
- { pop$ "" }
- 'skip$
- if$
-}
-
-FUNCTION {emphasize}
-{ duplicate$ empty$
- { pop$ "" }
- { "\emph{" swap$ * "}" * }
- if$
-}
-
-INTEGERS { nameptr namesleft numnames }
-
-FUNCTION {format.names}
-{ 's :=
- #1 'nameptr :=
- s num.names$ 'numnames :=
- numnames 'namesleft :=
- { namesleft #0 > }
- { s nameptr "{vv~}{ll}{, jj}{, ff}" format.name$ 't :=
- nameptr #1 >
- { namesleft #1 >
- { ", " * t * }
- { numnames #2 >
- { "," * }
- 'skip$
- if$
- t "others" =
- { " et~al." * }
- { " and " * t * }
- if$
- }
- if$
- }
- 't
- if$
- nameptr #1 + 'nameptr :=
- namesleft #1 - 'namesleft :=
- }
- while$
-}
-
-FUNCTION {format.key}
-{ empty$
- { key field.or.null }
- { "" }
- if$
-}
-
-FUNCTION {format.authors}
-{ author empty$
- { "" }
- { author format.names }
- if$
-}
-
-FUNCTION {format.editors}
-{ editor empty$
- { "" }
- { editor format.names
- editor num.names$ #1 >
- { " (eds.)" * }
- { " (ed.)" * }
- if$
- }
- if$
-}
-
-FUNCTION {format.isbn}
-{ isbn empty$
- { "" }
- { new.block "ISBN " isbn * }
- if$
-}
-
-FUNCTION {format.issn}
-{ issn empty$
- { "" }
- { new.block "ISSN " issn * }
- if$
-}
-
-FUNCTION {format.url}
-{ url empty$
- { "" }
- { new.block "URL \url{" url * "}" * }
- if$
-}
-
-FUNCTION {format.doi}
-{ doi empty$
- { "" }
- { new.block "\doi{" doi * "}" * }
- if$
-}
-
-FUNCTION {format.title}
-{ title empty$
- { "" }
- { title "t" change.case$ }
- if$
-}
-
-FUNCTION {format.full.names}
-{'s :=
- #1 'nameptr :=
- s num.names$ 'numnames :=
- numnames 'namesleft :=
- { namesleft #0 > }
- { s nameptr
- "{vv~}{ll}" format.name$ 't :=
- nameptr #1 >
- {
- namesleft #1 >
- { ", " * t * }
- {
- numnames #2 >
- { "," * }
- 'skip$
- if$
- t "others" =
- { " et~al." * }
- { " and " * t * }
- if$
- }
- if$
- }
- 't
- if$
- nameptr #1 + 'nameptr :=
- namesleft #1 - 'namesleft :=
- }
- while$
-}
-
-FUNCTION {author.editor.full}
-{ author empty$
- { editor empty$
- { "" }
- { editor format.full.names }
- if$
- }
- { author format.full.names }
- if$
-}
-
-FUNCTION {author.full}
-{ author empty$
- { "" }
- { author format.full.names }
- if$
-}
-
-FUNCTION {editor.full}
-{ editor empty$
- { "" }
- { editor format.full.names }
- if$
-}
-
-FUNCTION {make.full.names}
-{ type$ "book" =
- type$ "inbook" =
- or
- 'author.editor.full
- { type$ "proceedings" =
- 'editor.full
- 'author.full
- if$
- }
- if$
-}
-
-FUNCTION {output.bibitem}
-{ newline$
- "\bibitem[" write$
- label write$
- ")" make.full.names duplicate$ short.list =
- { pop$ }
- { * }
- if$
- "]{" * write$
- cite$ write$
- "}" write$
- newline$
- ""
- before.all 'output.state :=
-}
-
-FUNCTION {n.dashify}
-{ 't :=
- ""
- { t empty$ not }
- { t #1 #1 substring$ "-" =
- { t #1 #2 substring$ "--" = not
- { "--" *
- t #2 global.max$ substring$ 't :=
- }
- { { t #1 #1 substring$ "-" = }
- { "-" *
- t #2 global.max$ substring$ 't :=
- }
- while$
- }
- if$
- }
- { t #1 #1 substring$ *
- t #2 global.max$ substring$ 't :=
- }
- if$
- }
- while$
-}
-
-FUNCTION {format.date}
-{ year duplicate$ empty$
- { "empty year in " cite$ * warning$
- pop$ "" }
- 'skip$
- if$
- month empty$
- 'skip$
- { month
- " " * swap$ *
- }
- if$
- extra.label *
-}
-
-FUNCTION {format.btitle}
-{ title emphasize
-}
-
-FUNCTION {tie.or.space.connect}
-{ duplicate$ text.length$ #3 <
- { "~" }
- { " " }
- if$
- swap$ * *
-}
-
-FUNCTION {either.or.check}
-{ empty$
- 'pop$
- { "can't use both " swap$ * " fields in " * cite$ * warning$ }
- if$
-}
-
-FUNCTION {format.bvolume}
-{ volume empty$
- { "" }
- { "volume" volume tie.or.space.connect
- series empty$
- 'skip$
- { " of " * series emphasize * }
- if$
- "volume and number" number either.or.check
- }
- if$
-}
-
-FUNCTION {format.number.series}
-{ volume empty$
- { number empty$
- { series field.or.null }
- { output.state mid.sentence =
- { "number" }
- { "Number" }
- if$
- number tie.or.space.connect
- series empty$
- { "there's a number but no series in " cite$ * warning$ }
- { " in " * series * }
- if$
- }
- if$
- }
- { "" }
- if$
-}
-
-FUNCTION {format.edition}
-{ edition empty$
- { "" }
- { output.state mid.sentence =
- { edition "l" change.case$ " edition" * }
- { edition "t" change.case$ " edition" * }
- if$
- }
- if$
-}
-
-INTEGERS { multiresult }
-
-FUNCTION {multi.page.check}
-{ 't :=
- #0 'multiresult :=
- { multiresult not
- t empty$ not
- and
- }
- { t #1 #1 substring$
- duplicate$ "-" =
- swap$ duplicate$ "," =
- swap$ "+" =
- or or
- { #1 'multiresult := }
- { t #2 global.max$ substring$ 't := }
- if$
- }
- while$
- multiresult
-}
-
-FUNCTION {format.pages}
-{ pages empty$
- { "" }
- { pages multi.page.check
- { "pp.\ " pages n.dashify tie.or.space.connect }
- { "pp.\ " pages tie.or.space.connect }
- if$
- }
- if$
-}
-
-FUNCTION {format.eid}
-{ eid empty$
- { "" }
- { "art." eid tie.or.space.connect }
- if$
-}
-
-FUNCTION {format.vol.num.pages}
-{ volume field.or.null
- number empty$
- 'skip$
- { "\penalty0 (" number * ")" * *
- volume empty$
- { "there's a number but no volume in " cite$ * warning$ }
- 'skip$
- if$
- }
- if$
- pages empty$
- 'skip$
- { duplicate$ empty$
- { pop$ format.pages }
- { ":\penalty0 " * pages n.dashify * }
- if$
- }
- if$
-}
-
-FUNCTION {format.vol.num.eid}
-{ volume field.or.null
- number empty$
- 'skip$
- { "\penalty0 (" number * ")" * *
- volume empty$
- { "there's a number but no volume in " cite$ * warning$ }
- 'skip$
- if$
- }
- if$
- eid empty$
- 'skip$
- { duplicate$ empty$
- { pop$ format.eid }
- { ":\penalty0 " * eid * }
- if$
- }
- if$
-}
-
-FUNCTION {format.chapter.pages}
-{ chapter empty$
- 'format.pages
- { type empty$
- { "chapter" }
- { type "l" change.case$ }
- if$
- chapter tie.or.space.connect
- pages empty$
- 'skip$
- { ", " * format.pages * }
- if$
- }
- if$
-}
-
-FUNCTION {format.in.ed.booktitle}
-{ booktitle empty$
- { "" }
- { editor empty$
- { "In " booktitle emphasize * }
- { "In " format.editors * ", " * booktitle emphasize * }
- if$
- }
- if$
-}
-
-FUNCTION {empty.misc.check}
-{ author empty$ title empty$ howpublished empty$
- month empty$ year empty$ note empty$
- and and and and and
- key empty$ not and
- { "all relevant fields are empty in " cite$ * warning$ }
- 'skip$
- if$
-}
-
-FUNCTION {format.thesis.type}
-{ type empty$
- 'skip$
- { pop$
- type "t" change.case$
- }
- if$
-}
-
-FUNCTION {format.tr.number}
-{ type empty$
- { "Technical Report" }
- 'type
- if$
- number empty$
- { "t" change.case$ }
- { number tie.or.space.connect }
- if$
-}
-
-FUNCTION {format.article.crossref}
-{ key empty$
- { journal empty$
- { "need key or journal for " cite$ * " to crossref " * crossref *
- warning$
- ""
- }
- { "In \emph{" journal * "}" * }
- if$
- }
- { "In " }
- if$
- " \citet{" * crossref * "}" *
-}
-
-FUNCTION {format.book.crossref}
-{ volume empty$
- { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
- "In "
- }
- { "Volume" volume tie.or.space.connect
- " of " *
- }
- if$
- editor empty$
- editor field.or.null author field.or.null =
- or
- { key empty$
- { series empty$
- { "need editor, key, or series for " cite$ * " to crossref " *
- crossref * warning$
- "" *
- }
- { "\emph{" * series * "}" * }
- if$
- }
- 'skip$
- if$
- }
- 'skip$
- if$
- " \citet{" * crossref * "}" *
-}
-
-FUNCTION {format.incoll.inproc.crossref}
-{ editor empty$
- editor field.or.null author field.or.null =
- or
- { key empty$
- { booktitle empty$
- { "need editor, key, or booktitle for " cite$ * " to crossref " *
- crossref * warning$
- ""
- }
- { "In \emph{" booktitle * "}" * }
- if$
- }
- { "In " }
- if$
- }
- { "In " }
- if$
- " \citet{" * crossref * "}" *
-}
-
-FUNCTION {article}
-{ output.bibitem
- format.authors "author" output.check
- author format.key output
- new.block
- format.title "title" output.check
- new.block
- crossref missing$
- { journal emphasize "journal" output.check
- eid empty$
- { format.vol.num.pages output }
- { format.vol.num.eid output }
- if$
- format.date "year" output.check
- }
- { format.article.crossref output.nonnull
- eid empty$
- { format.pages output }
- { format.eid output }
- if$
- }
- if$
- format.issn output
- format.doi output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {book}
-{ output.bibitem
- author empty$
- { format.editors "author and editor" output.check
- editor format.key output
- }
- { format.authors output.nonnull
- crossref missing$
- { "author and editor" editor either.or.check }
- 'skip$
- if$
- }
- if$
- new.block
- format.btitle "title" output.check
- crossref missing$
- { format.bvolume output
- new.block
- format.number.series output
- new.sentence
- publisher "publisher" output.check
- address output
- }
- { new.block
- format.book.crossref output.nonnull
- }
- if$
- format.edition output
- format.date "year" output.check
- format.isbn output
- format.doi output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {booklet}
-{ output.bibitem
- format.authors output
- author format.key output
- new.block
- format.title "title" output.check
- howpublished address new.block.checkb
- howpublished output
- address output
- format.date output
- format.isbn output
- format.doi output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {inbook}
-{ output.bibitem
- author empty$
- { format.editors "author and editor" output.check
- editor format.key output
- }
- { format.authors output.nonnull
- crossref missing$
- { "author and editor" editor either.or.check }
- 'skip$
- if$
- }
- if$
- new.block
- format.btitle "title" output.check
- crossref missing$
- { format.bvolume output
- format.chapter.pages "chapter and pages" output.check
- new.block
- format.number.series output
- new.sentence
- publisher "publisher" output.check
- address output
- }
- { format.chapter.pages "chapter and pages" output.check
- new.block
- format.book.crossref output.nonnull
- }
- if$
- format.edition output
- format.date "year" output.check
- format.isbn output
- format.doi output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {incollection}
-{ output.bibitem
- format.authors "author" output.check
- author format.key output
- new.block
- format.title "title" output.check
- new.block
- crossref missing$
- { format.in.ed.booktitle "booktitle" output.check
- format.bvolume output
- format.number.series output
- format.chapter.pages output
- new.sentence
- publisher "publisher" output.check
- address output
- format.edition output
- format.date "year" output.check
- }
- { format.incoll.inproc.crossref output.nonnull
- format.chapter.pages output
- }
- if$
- format.isbn output
- format.doi output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {inproceedings}
-{ output.bibitem
- format.authors "author" output.check
- author format.key output
- new.block
- format.title "title" output.check
- new.block
- crossref missing$
- { format.in.ed.booktitle "booktitle" output.check
- format.bvolume output
- format.number.series output
- format.pages output
- address empty$
- { organization publisher new.sentence.checkb
- organization output
- publisher output
- format.date "year" output.check
- }
- { address output.nonnull
- format.date "year" output.check
- new.sentence
- organization output
- publisher output
- }
- if$
- }
- { format.incoll.inproc.crossref output.nonnull
- format.pages output
- }
- if$
- format.isbn output
- format.doi output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {conference} { inproceedings }
-
-FUNCTION {manual}
-{ output.bibitem
- format.authors output
- author format.key output
- new.block
- format.btitle "title" output.check
- organization address new.block.checkb
- organization output
- address output
- format.edition output
- format.date output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {mastersthesis}
-{ output.bibitem
- format.authors "author" output.check
- author format.key output
- new.block
- format.title "title" output.check
- new.block
- "Master's thesis" format.thesis.type output.nonnull
- school "school" output.check
- address output
- format.date "year" output.check
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {misc}
-{ output.bibitem
- format.authors output
- author format.key output
- title howpublished new.block.checkb
- format.title output
- howpublished new.block.checka
- howpublished output
- format.date output
- format.issn output
- format.url output
- new.block
- note output
- fin.entry
- empty.misc.check
-}
-
-FUNCTION {phdthesis}
-{ output.bibitem
- format.authors "author" output.check
- author format.key output
- new.block
- format.btitle "title" output.check
- new.block
- "PhD thesis" format.thesis.type output.nonnull
- school "school" output.check
- address output
- format.date "year" output.check
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {proceedings}
-{ output.bibitem
- format.editors output
- editor format.key output
- new.block
- format.btitle "title" output.check
- format.bvolume output
- format.number.series output
- address output
- format.date "year" output.check
- new.sentence
- organization output
- publisher output
- format.isbn output
- format.doi output
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {techreport}
-{ output.bibitem
- format.authors "author" output.check
- author format.key output
- new.block
- format.title "title" output.check
- new.block
- format.tr.number output.nonnull
- institution "institution" output.check
- address output
- format.date "year" output.check
- format.url output
- new.block
- note output
- fin.entry
-}
-
-FUNCTION {unpublished}
-{ output.bibitem
- format.authors "author" output.check
- author format.key output
- new.block
- format.title "title" output.check
- new.block
- note "note" output.check
- format.date output
- format.url output
- fin.entry
-}
-
-FUNCTION {default.type} { misc }
-
-
-MACRO {jan} {"January"}
-
-MACRO {feb} {"February"}
-
-MACRO {mar} {"March"}
-
-MACRO {apr} {"April"}
-
-MACRO {may} {"May"}
-
-MACRO {jun} {"June"}
-
-MACRO {jul} {"July"}
-
-MACRO {aug} {"August"}
-
-MACRO {sep} {"September"}
-
-MACRO {oct} {"October"}
-
-MACRO {nov} {"November"}
-
-MACRO {dec} {"December"}
-
-
-
-MACRO {acmcs} {"ACM Computing Surveys"}
-
-MACRO {acta} {"Acta Informatica"}
-
-MACRO {cacm} {"Communications of the ACM"}
-
-MACRO {ibmjrd} {"IBM Journal of Research and Development"}
-
-MACRO {ibmsj} {"IBM Systems Journal"}
-
-MACRO {ieeese} {"IEEE Transactions on Software Engineering"}
-
-MACRO {ieeetc} {"IEEE Transactions on Computers"}
-
-MACRO {ieeetcad}
- {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"}
-
-MACRO {ipl} {"Information Processing Letters"}
-
-MACRO {jacm} {"Journal of the ACM"}
-
-MACRO {jcss} {"Journal of Computer and System Sciences"}
-
-MACRO {scp} {"Science of Computer Programming"}
-
-MACRO {sicomp} {"SIAM Journal on Computing"}
-
-MACRO {tocs} {"ACM Transactions on Computer Systems"}
-
-MACRO {tods} {"ACM Transactions on Database Systems"}
-
-MACRO {tog} {"ACM Transactions on Graphics"}
-
-MACRO {toms} {"ACM Transactions on Mathematical Software"}
-
-MACRO {toois} {"ACM Transactions on Office Information Systems"}
-
-MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"}
-
-MACRO {tcs} {"Theoretical Computer Science"}
-
-
-READ
-
-FUNCTION {sortify}
-{ purify$
- "l" change.case$
-}
-
-INTEGERS { len }
-
-FUNCTION {chop.word}
-{ 's :=
- 'len :=
- s #1 len substring$ =
- { s len #1 + global.max$ substring$ }
- 's
- if$
-}
-
-FUNCTION {format.lab.names}
-{ 's :=
- s #1 "{vv~}{ll}" format.name$
- s num.names$ duplicate$
- #2 >
- { pop$ " et~al." * }
- { #2 <
- 'skip$
- { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
- { " et~al." * }
- { " \& " * s #2 "{vv~}{ll}" format.name$ * }
- if$
- }
- if$
- }
- if$
-}
-
-FUNCTION {author.key.label}
-{ author empty$
- { key empty$
- { cite$ #1 #3 substring$ }
- 'key
- if$
- }
- { author format.lab.names }
- if$
-}
-
-FUNCTION {author.editor.key.label}
-{ author empty$
- { editor empty$
- { key empty$
- { cite$ #1 #3 substring$ }
- 'key
- if$
- }
- { editor format.lab.names }
- if$
- }
- { author format.lab.names }
- if$
-}
-
-FUNCTION {author.key.organization.label}
-{ author empty$
- { key empty$
- { organization empty$
- { cite$ #1 #3 substring$ }
- { "The " #4 organization chop.word #3 text.prefix$ }
- if$
- }
- 'key
- if$
- }
- { author format.lab.names }
- if$
-}
-
-FUNCTION {editor.key.organization.label}
-{ editor empty$
- { key empty$
- { organization empty$
- { cite$ #1 #3 substring$ }
- { "The " #4 organization chop.word #3 text.prefix$ }
- if$
- }
- 'key
- if$
- }
- { editor format.lab.names }
- if$
-}
-
-FUNCTION {calc.short.authors}
-{ type$ "book" =
- type$ "inbook" =
- or
- 'author.editor.key.label
- { type$ "proceedings" =
- 'editor.key.organization.label
- { type$ "manual" =
- 'author.key.organization.label
- 'author.key.label
- if$
- }
- if$
- }
- if$
- 'short.list :=
-}
-
-FUNCTION {calc.label}
-{ calc.short.authors
- short.list
- "("
- *
- year duplicate$ empty$
- short.list key field.or.null = or
- { pop$ "" }
- 'skip$
- if$
- *
- 'label :=
-}
-
-FUNCTION {sort.format.names}
-{ 's :=
- #1 'nameptr :=
- ""
- s num.names$ 'numnames :=
- numnames 'namesleft :=
- { namesleft #0 > }
- {
- s nameptr "{vv{ } }{ll{ }}{ ff{ }}{ jj{ }}" format.name$ 't :=
- nameptr #1 >
- {
- " " *
- namesleft #1 = t "others" = and
- { "zzzzz" * }
- { numnames #2 > nameptr #2 = and
- { "zz" * year field.or.null * " " * }
- 'skip$
- if$
- t sortify *
- }
- if$
- }
- { t sortify * }
- if$
- nameptr #1 + 'nameptr :=
- namesleft #1 - 'namesleft :=
- }
- while$
-}
-
-FUNCTION {sort.format.title}
-{ 't :=
- "A " #2
- "An " #3
- "The " #4 t chop.word
- chop.word
- chop.word
- sortify
- #1 global.max$ substring$
-}
-
-FUNCTION {author.sort}
-{ author empty$
- { key empty$
- { "to sort, need author or key in " cite$ * warning$
- ""
- }
- { key sortify }
- if$
- }
- { author sort.format.names }
- if$
-}
-
-FUNCTION {author.editor.sort}
-{ author empty$
- { editor empty$
- { key empty$
- { "to sort, need author, editor, or key in " cite$ * warning$
- ""
- }
- { key sortify }
- if$
- }
- { editor sort.format.names }
- if$
- }
- { author sort.format.names }
- if$
-}
-
-FUNCTION {author.organization.sort}
-{ author empty$
- { organization empty$
- { key empty$
- { "to sort, need author, organization, or key in " cite$ * warning$
- ""
- }
- { key sortify }
- if$
- }
- { "The " #4 organization chop.word sortify }
- if$
- }
- { author sort.format.names }
- if$
-}
-
-FUNCTION {editor.organization.sort}
-{ editor empty$
- { organization empty$
- { key empty$
- { "to sort, need editor, organization, or key in " cite$ * warning$
- ""
- }
- { key sortify }
- if$
- }
- { "The " #4 organization chop.word sortify }
- if$
- }
- { editor sort.format.names }
- if$
-}
-
-
-FUNCTION {presort}
-{ calc.label
- label sortify
- " "
- *
- type$ "book" =
- type$ "inbook" =
- or
- 'author.editor.sort
- { type$ "proceedings" =
- 'editor.organization.sort
- { type$ "manual" =
- 'author.organization.sort
- 'author.sort
- if$
- }
- if$
- }
- if$
- " "
- *
- year field.or.null sortify
- *
- " "
- *
- cite$
- *
- #1 entry.max$ substring$
- 'sort.label :=
- sort.label *
- #1 entry.max$ substring$
- 'sort.key$ :=
-}
-
-ITERATE {presort}
-
-SORT
-
-STRINGS { longest.label last.label next.extra }
-
-INTEGERS { longest.label.width last.extra.num number.label }
-
-FUNCTION {initialize.longest.label}
-{ "" 'longest.label :=
- #0 int.to.chr$ 'last.label :=
- "" 'next.extra :=
- #0 'longest.label.width :=
- #0 'last.extra.num :=
- #0 'number.label :=
-}
-
-FUNCTION {forward.pass}
-{ last.label label =
- { last.extra.num #1 + 'last.extra.num :=
- last.extra.num int.to.chr$ 'extra.label :=
- }
- { "a" chr.to.int$ 'last.extra.num :=
- "" 'extra.label :=
- label 'last.label :=
- }
- if$
- number.label #1 + 'number.label :=
-}
-
-FUNCTION {reverse.pass}
-{ next.extra "b" =
- { "a" 'extra.label := }
- 'skip$
- if$
- extra.label 'next.extra :=
- extra.label
- duplicate$ empty$
- 'skip$
- { "{\natexlab{" swap$ * "}}" * }
- if$
- 'extra.label :=
- label extra.label * 'label :=
-}
-
-EXECUTE {initialize.longest.label}
-
-ITERATE {forward.pass}
-
-REVERSE {reverse.pass}
-
-FUNCTION {bib.sort.order}
-{ sort.label 'sort.key$ :=
-}
-
-ITERATE {bib.sort.order}
-
-SORT
-
-FUNCTION {begin.bib}
-{ preamble$ empty$
- 'skip$
- { preamble$ write$ newline$ }
- if$
- "\begin{thebibliography}{" number.label int.to.str$ * "}" *
- write$ newline$
- "\providecommand{\natexlab}[1]{#1}"
- write$ newline$
- "\providecommand{\url}[1]{\texttt{#1}}"
- write$ newline$
- "\expandafter\ifx\csname urlstyle\endcsname\relax"
- write$ newline$
- " \providecommand{\doi}[1]{doi: #1}\else"
- write$ newline$
- " \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi"
- write$ newline$
-}
-
-EXECUTE {begin.bib}
-
-EXECUTE {init.state.consts}
-
-ITERATE {call.type$}
-
-FUNCTION {end.bib}
-{ newline$
- "\end{thebibliography}" write$ newline$
-}
-
-EXECUTE {end.bib}
diff --git a/report/icml_numpapers.pdf b/report/icml_numpapers.pdf
deleted file mode 100644
index 7d247e6..0000000
Binary files a/report/icml_numpapers.pdf and /dev/null differ
diff --git a/report/mlp-cw1-template.pdf b/report/mlp-cw1-template.pdf
deleted file mode 100644
index 720d2df..0000000
Binary files a/report/mlp-cw1-template.pdf and /dev/null differ
diff --git a/report/mlp-cw1-template.tex b/report/mlp-cw1-template.tex
deleted file mode 100644
index 22124a7..0000000
--- a/report/mlp-cw1-template.tex
+++ /dev/null
@@ -1,207 +0,0 @@
-%% Template for MLP Coursework 1 / 16 October 2017
-
-%% Based on LaTeX template for ICML 2017 - example_paper.tex at
-%% https://2017.icml.cc/Conferences/2017/StyleAuthorInstructions
-
-\documentclass{article}
-
-\usepackage[T1]{fontenc}
-\usepackage{amssymb,amsmath}
-\usepackage{txfonts}
-\usepackage{microtype}
-
-% For figures
-\usepackage{graphicx}
-\usepackage{subfigure}
-
-% For citations
-\usepackage{natbib}
-
-% For algorithms
-\usepackage{algorithm}
-\usepackage{algorithmic}
-
-% the hyperref package is used to produce hyperlinks in the
-% resulting PDF. If this breaks your system, please commend out the
-% following usepackage line and replace \usepackage{mlp2017} with
-% \usepackage[nohyperref]{mlp2017} below.
-\usepackage{hyperref}
-\usepackage{url}
-\urlstyle{same}
-
-% Packages hyperref and algorithmic misbehave sometimes. We can fix
-% this with the following command.
-\newcommand{\theHalgorithm}{\arabic{algorithm}}
-
-
-% Set up MLP coursework style (based on ICML style)
-\usepackage{mlp2017}
-\mlptitlerunning{MLP Coursework 1 (\studentNumber)}
-\bibliographystyle{icml2017}
-
-
-\DeclareMathOperator{\softmax}{softmax}
-\DeclareMathOperator{\sigmoid}{sigmoid}
-\DeclareMathOperator{\sgn}{sgn}
-\DeclareMathOperator{\relu}{relu}
-\DeclareMathOperator{\lrelu}{lrelu}
-\DeclareMathOperator{\elu}{elu}
-\DeclareMathOperator{\selu}{selu}
-\DeclareMathOperator{\maxout}{maxout}
-
-%% You probably do not need to change anything above this comment
-
-%% REPLACE this with your student number
-\def\studentNumber{s1754321}
-
-\begin{document}
-
-\twocolumn[
-\mlptitle{MLP Coursework 1: Activation Functions}
-
-\centerline{\studentNumber}
-
-\vskip 7mm
-]
-
-\begin{abstract}
-The abstract should be 100--200 words long, providing a concise summary of the contents of your report.
-\end{abstract}
-
-\section{Introduction}
-\label{sec:intro}
-This document provides a template for the MLP coursework 1 report. In particular, it structures the document into five sections (plus an abstract and the references) -- you should keep to this structure for your report. If you want to use subsections within a section that is fine, but please do not use any deeper structuring. In this template the text in each section will include an outline of what you should include in each section, along with some practical LaTeX examples (for example figures, tables, algorithms). Your document should be no longer than \textbf{six pages}, with an additional page allowed for references.
-
-The introduction should place your work in context, giving the overall motivation for the work, and clearly outlining the research questions you have explored -- in this case comparison of the behaviour of the different activation functions, experimental investigation of the impact of the depth of the network with respect to accuracy, and experimental investigation of different approaches to weight initialisation. This section should also include a concise description of the MNIST task and data -- be precise: for example state the size of the training and validation sets.
-
-
-\section{Activation functions}
-\label{sec:actfn}
-This section should cover the theoretical methodology -- in this case you should present the four activation functions: ReLU, Leaky ReLU, ELU, and SELU. I didn't do it in this document, but the first time you use an acronym you should say what it stands for, for example Restricted Linear Unit (ReLU). You should use equations to concisely describe each activation function. For example, ReLU:
-\begin{equation}
- \relu(x) = \max(0, x) ,
-\end{equation}
-which has the gradient:
-\begin{equation}
- \frac{d}{dx} \relu(x) =
- \begin{cases}
- 0 & \quad \text{if } x \leq 0 \\
- 1 & \quad \text{if } x > 0 .
- \end{cases}
-\end{equation}
-The \LaTeX for the derivatives is slightly more complicated. We provided definitions near the top of the file (the part before \verb+\begin{document}+) for \verb+\relu+, \verb+\lrelu+, \verb+\elu+, and \verb+\selu+. There is no need to discuss the unit tests for these activation functions in this report.
-
-It is probably not needed in this report, but if you would like to include an algorithm in your report, please use the \verb+algorithm+ and \verb+algorithmic+ environments to format pseudocode (for instance, Algorithm~\ref{alg:example}). These require the corresponding style files, \verb+algorithm.sty+ and \verb+algorithmic.sty+ which are supplied with this package.
-
-\begin{algorithm}[ht]
-\begin{algorithmic}
- \STATE {\bfseries Input:} data $x_i$, size $m$
- \REPEAT
- \STATE Initialize $noChange = true$.
- \FOR{$i=1$ {\bfseries to} $m-1$}
- \IF{$x_i > x_{i+1}$}
- \STATE Swap $x_i$ and $x_{i+1}$
- \STATE $noChange = false$
- \ENDIF
- \ENDFOR
- \UNTIL{$noChange$ is $true$}
-\end{algorithmic}
- \caption{Bubble Sort}
- \label{alg:example}
-\end{algorithm}
-
-\section{Experimental comparison of activation functions}
-\label{sec:actexpts}
-In this section you should present the results and discussion of your experiments comparing networks using the different activation functions on the MNIST task. As explained in the coursework document, you should use 2 hidden layers with 100 hidden units per layer for these experiments. You can compare the learning curves (error vs epoch) for training and/or validation, and the validation set accuracies.
-
-Your experimental sections should include graphs (for instance, figure~\ref{fig:sample-graph}) and/or tables (for instance, table~\ref{tab:sample-table})\footnote{These examples were taken from the ICML template paper.}, using the \verb+figure+ and \verb+table+ environments, in which you use \verb+\includegraphics+ to include an image (pdf, png, or jpg formats). Please export graphs as
-\href{https://en.wikipedia.org/wiki/Vector_graphics}{vector graphics}
-rather than \href{https://en.wikipedia.org/wiki/Raster_graphics}{raster
-files} as this will make sure all detail in the plot is visible.
-Matplotlib supports saving high quality figures in a wide range of
-common image formats using the
-\href{http://matplotlib.org/api/pyplot_api.html\#matplotlib.pyplot.savefig}{\texttt{savefig}}
-function. \textbf{You should use \texttt{savefig} rather than copying
-the screen-resolution raster images outputted in the notebook.} An
-example of using \texttt{savefig} to save a figure as a PDF file (which
-can be included as graphics in a \LaTeX document is given in the coursework document.
-
-If you need a figure or table to stretch across two columns use the \verb+figure*+ or \verb+table*+ environment instead of the \verb+figure+ or \verb+table+ environment. Use the \verb+subfigure+ environment if you want to include multiple graphics in a single figure.
-
-\begin{figure}[tb]
-\vskip 5mm
-\begin{center}
-\centerline{\includegraphics[width=\columnwidth]{icml_numpapers}}
-\caption{Historical locations and number of accepted papers for International
- Machine Learning Conferences (ICML 1993 -- ICML 2008) and
- International Workshops on Machine Learning (ML 1988 -- ML
- 1992). At the time this figure was produced, the number of
- accepted papers for ICML 2008 was unknown and instead estimated.}
-\label{fig:sample-graph}
-\end{center}
-\vskip -5mm
-\end{figure}
-
-\begin{table}[tb]
-\vskip 3mm
-\begin{center}
-\begin{small}
-\begin{sc}
-\begin{tabular}{lcccr}
-\hline
-\abovespace\belowspace
-Data set & Naive & Flexible & Better? \\
-\hline
-\abovespace
-Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
-Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
-Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
-Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
-Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
-Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
-Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
-\belowspace
-Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
-\hline
-\end{tabular}
-\end{sc}
-\end{small}
-\caption{Classification accuracies for naive Bayes and flexible
-Bayes on various data sets.}
-\label{tab:sample-table}
-\end{center}
-\vskip -3mm
-\end{table}
-
-\section{Deep neural network experiments}
-\label{sec:dnnexpts}
-This section should report on your experiments on deeper networks for MNIST. The two sets of experiments are to explore the impact of the depth of the network (number of hidden layers), and a comparison of different approaches to weight initialisation.
-
-In this section, and in the previous section, you should present your experimental results clearly and concisely, followed by an interpretation and discussion of results. You need to present your results in a way that makes it easy for a reader to understand what they mean. You should facilitate comparisons either using graphs with multiple curves or (if appropriate, e.g. for accuracies) a results table. You need to avoid having too many figures, poorly labelled graphs, and graphs which should be comparable but which use different axis scales. A good presentation will enable the reader to compare trends in the same graph -- each graph should summarise the results relating to a particular research (sub)question.
-
-Your discussion should interpret the results, both in terms of summarising the outcomes of a particular experiment, and attempting to relate to the underlying models. A good report would have some analysis, resulting in an understanding of why particular results are observed, perhaps with reference to the literature. Use bibtex to organise your references -- in this case the references are in the file \verb+example-refs.bib+. Here is a an example reference \citep{langley00}.
-
-
-
-
-\section{Conclusions}
-\label{sec:concl}
-You should draw conclusions from the experiments, related to the research questions outlined in the introduction (section~\ref{sec:intro}). You should state the conclusions clearly and concisely. It is good if the conclusion from one experiment influenced what you did in later experiments -- your aim is to learn from your experiments. Extra credit if you relate your findings to what has been reported in the literature.
-
-A good conclusions section would also include a further work discussion, building on work done so far, and referencing the literature where appropriate.
-
-\bibliography{example-refs}
-
-\end{document}
-
-
-% This document was modified from the file originally made available by
-% Pat Langley and Andrea Danyluk for ICML-2K. This version was
-% created by Lise Getoor and Tobias Scheffer, it was slightly modified
-% from the 2010 version by Thorsten Joachims & Johannes Fuernkranz,
-% slightly modified from the 2009 version by Kiri Wagstaff and
-% Sam Roweis's 2008 version, which is slightly modified from
-% Prasad Tadepalli's 2007 version which is a lightly
-% changed version of the previous year's version by Andrew Moore,
-% which was in turn edited from those of Kristian Kersting and
-% Codrina Lauth. Alex Smola contributed to the algorithmic style files.
diff --git a/report/mlp-cw2-template.pdf b/report/mlp-cw2-template.pdf
deleted file mode 100644
index c1c4ea8..0000000
Binary files a/report/mlp-cw2-template.pdf and /dev/null differ
diff --git a/report/mlp-cw2-template.tex b/report/mlp-cw2-template.tex
deleted file mode 100644
index bdb2de9..0000000
--- a/report/mlp-cw2-template.tex
+++ /dev/null
@@ -1,195 +0,0 @@
-%% Template for MLP Coursework 2 / 6 November 2017
-
-%% Based on LaTeX template for ICML 2017 - example_paper.tex at
-%% https://2017.icml.cc/Conferences/2017/StyleAuthorInstructions
-
-\documentclass{article}
-
-\usepackage[T1]{fontenc}
-\usepackage{amssymb,amsmath}
-\usepackage{txfonts}
-\usepackage{microtype}
-
-% For figures
-\usepackage{graphicx}
-\usepackage{subfigure}
-
-% For citations
-\usepackage{natbib}
-
-% For algorithms
-\usepackage{algorithm}
-\usepackage{algorithmic}
-
-% the hyperref package is used to produce hyperlinks in the
-% resulting PDF. If this breaks your system, please commend out the
-% following usepackage line and replace \usepackage{mlp2017} with
-% \usepackage[nohyperref]{mlp2017} below.
-\usepackage{hyperref}
-\usepackage{url}
-\urlstyle{same}
-
-% Packages hyperref and algorithmic misbehave sometimes. We can fix
-% this with the following command.
-\newcommand{\theHalgorithm}{\arabic{algorithm}}
-
-
-% Set up MLP coursework style (based on ICML style)
-\usepackage{mlp2017}
-\mlptitlerunning{MLP Coursework 2 (\studentNumber)}
-\bibliographystyle{icml2017}
-
-
-\DeclareMathOperator{\softmax}{softmax}
-\DeclareMathOperator{\sigmoid}{sigmoid}
-\DeclareMathOperator{\sgn}{sgn}
-\DeclareMathOperator{\relu}{relu}
-\DeclareMathOperator{\lrelu}{lrelu}
-\DeclareMathOperator{\elu}{elu}
-\DeclareMathOperator{\selu}{selu}
-\DeclareMathOperator{\maxout}{maxout}
-
-%% You probably do not need to change anything above this comment
-
-%% REPLACE this with your student number
-\def\studentNumber{sXXXXXXX}
-
-\begin{document}
-
-\twocolumn[
-\mlptitle{MLP Coursework 2: Learning rules, BatchNorm, and ConvNets}
-
-\centerline{\studentNumber}
-
-\vskip 7mm
-]
-
-\begin{abstract}
-The abstract should be 100--200 words long, providing a concise summary of the contents of your report.
-\end{abstract}
-
-\section{Introduction}
-\label{sec:intro}
-This document provides a template for the MLP coursework 2 report. This template structures the report into sections, which you are recommended to use, but can change if you wish. If you want to use subsections within a section that is fine, but please do not use any deeper structuring. In this template the text in each section will include an outline of what you should include in each section, along with some practical LaTeX examples (for example figures, tables, algorithms). Your document should be no longer than \textbf{seven pages}, with an additional page allowed for references.
-
-The introduction should place your work in context, giving the overall motivation for the work, and clearly outlining the research questions you have explored. This section should also include a concise description of the Balanced EMNIST task and data -- be precise: for example state the size of the training, validation, and test sets.
-
-\section{Baseline systems}
-In this section you should report your baseline experiments for EMNIST. No need for theoretical explanations of things covered in the course, but should you go beyond what was covered please explain what you did with references to relevant paper(s) if appropriate. In this section you should aim to cover the both the ``what'' and the ``why'': \emph{what} you did, giving sufficient information (hyperparameter settings, etc.) so that someone else (e.g. another student on the course) could reproduce your results; and \emph{why} you performed the experiments you are reporting - what you are aiming to discover what is the motivation for the particular experiments you undertook. You should also provide some discussion and interpretation of your results.
-
-As before, your experimental sections should include graphs (for instance, figure~\ref{fig:sample-graph}) and/or tables (for instance, table~\ref{tab:sample-table})\footnote{These examples were taken from the ICML template paper.}, using the \verb+figure+ and \verb+table+ environments, in which you use \verb+\includegraphics+ to include an image (pdf, png, or jpg formats). Please export graphs as
-\href{https://en.wikipedia.org/wiki/Vector_graphics}{vector graphics}
-rather than \href{https://en.wikipedia.org/wiki/Raster_graphics}{raster
-files} as this will make sure all detail in the plot is visible.
-Matplotlib supports saving high quality figures in a wide range of
-common image formats using the
-\href{http://matplotlib.org/api/pyplot_api.html\#matplotlib.pyplot.savefig}{\texttt{savefig}}
-function. \textbf{You should use \texttt{savefig} rather than copying
-the screen-resolution raster images outputted in the notebook.} An
-example of using \texttt{savefig} to save a figure as a PDF file (which
-can be included as graphics in a \LaTeX document is given in the coursework 1 document.
-
-If you need a figure or table to stretch across two columns use the \verb+figure*+ or \verb+table*+ environment instead of the \verb+figure+ or \verb+table+ environment. Use the \verb+subfigure+ environment if you want to include multiple graphics in a single figure.
-
-\begin{figure}[tb]
-\vskip 5mm
-\begin{center}
-\centerline{\includegraphics[width=\columnwidth]{icml_numpapers}}
-\caption{Historical locations and number of accepted papers for International
- Machine Learning Conferences (ICML 1993 -- ICML 2008) and
- International Workshops on Machine Learning (ML 1988 -- ML
- 1992). At the time this figure was produced, the number of
- accepted papers for ICML 2008 was unknown and instead estimated.}
-\label{fig:sample-graph}
-\end{center}
-\vskip -5mm
-\end{figure}
-
-\begin{table}[tb]
-\vskip 3mm
-\begin{center}
-\begin{small}
-\begin{sc}
-\begin{tabular}{lcccr}
-\hline
-\abovespace\belowspace
-Data set & Naive & Flexible & Better? \\
-\hline
-\abovespace
-Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
-Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
-Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
-Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
-Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
-Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
-Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
-\belowspace
-Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
-\hline
-\end{tabular}
-\end{sc}
-\end{small}
-\caption{Classification accuracies for naive Bayes and flexible
-Bayes on various data sets.}
-\label{tab:sample-table}
-\end{center}
-\vskip -3mm
-\end{table}
-
-\section{Learning rules}
-In this section you should compare RMSProp and Adam with gradient descent, introducing these learning rules either as equations or as algorithmic pseudocode. If you present the different approaches as algorithms, you can use the \verb+algorithm+ and \verb+algorithmic+ environments to format pseudocode (for instance, Algorithm~\ref{alg:example}). These require the corresponding style files, \verb+algorithm.sty+ and \verb+algorithmic.sty+ which are supplied with this package.
-
-\begin{algorithm}[ht]
-\begin{algorithmic}
- \STATE {\bfseries Input:} data $x_i$, size $m$
- \REPEAT
- \STATE Initialize $noChange = true$.
- \FOR{$i=1$ {\bfseries to} $m-1$}
- \IF{$x_i > x_{i+1}$}
- \STATE Swap $x_i$ and $x_{i+1}$
- \STATE $noChange = false$
- \ENDIF
- \ENDFOR
- \UNTIL{$noChange$ is $true$}
-\end{algorithmic}
- \caption{Bubble Sort}
- \label{alg:example}
-\end{algorithm}
-
-You should, in your own words, explain what the different learning rules do, and how they differ. You should then present your experiments and results, comparing and contrasting stochastic gradient descent, RMSProp, and Adam. As before concentrate on the ``what'' (remember give enough information so someone can reproduce your experiments), the ``why'' (why did you choose the experiments that you performed -- you may have been motivated by your earlier results, by the literature, or by a specific research question), and the interpretation of your results.
-
-In every section, you should present your results in a way that makes it easy for a reader to understand what they mean. You should facilitate comparisons either using graphs with multiple curves or (if appropriate, e.g. for accuracies) a results table. You need to avoid having too many figures, poorly labelled graphs, and graphs which should be comparable but which use different axis scales. A good presentation will enable the reader to compare trends in the same graph -- each graph should summarise the results relating to a particular research (sub)question.
-
-Your discussion should interpret the results, both in terms of summarising the outcomes of a particular experiment, and attempting to relate to the underlying models. A good report would have some analysis, resulting in an understanding of why particular results are observed, perhaps with reference to the literature. Use bibtex to organise your references -- in this case the references are in the file \verb+example-refs.bib+. Here is a an example reference \citep{langley00}.
-
-\section{Batch normalisation}
-In this section you should present batch normalisation, supported using equations or algorithmic pseudocode. Following this present your experiments, again remembering to include the ``what'', the ``why'', and the interpretation of results.
-
-\section{Convolutional networks}
-In this section you should present your experiments with convolutional networks. Explain the idea of convolutional layers and pooling layers, and briefly explain how you did the implementation. There is no need to include chunks of code. You should report the experiments you have undertaken, again remembering to include \emph{what} experiments you performed (include details of hyperparameters, etc.), \emph{why} you performed them (what was the motivation for the experiments, what research questions are you exploring), and the interpretation and discussion of your results.
-
-\section{Test results}
-The results reported in the previous sections should be on the validation set. You should finally report results on the EMNIST test set using what you judge to the be the best deep neural network (without convolutional layers) and the best convolutional network. Again focus on what the experiments were (be precise), why you chose to do them (in particular, how did you choose the architectures/settings to use with the test set), and a discussion/interpretation of the results.
-
-
-\section{Conclusions}
-\label{sec:concl}
-You should draw conclusions from the experiments, related to the research questions outlined in the introduction (section~\ref{sec:intro}). You should state the conclusions clearly and concisely. It is good if the conclusion from one experiment influenced what you did in later experiments -- your aim is to learn from your experiments. Extra credit if you relate your findings to what has been reported in the literature.
-
-A good conclusions section would also include a further work discussion, building on work done so far, and referencing the literature where appropriate.
-
-\bibliography{example-refs}
-
-\end{document}
-
-
-% This document was modified from the file originally made available by
-% Pat Langley and Andrea Danyluk for ICML-2K. This version was
-% created by Lise Getoor and Tobias Scheffer, it was slightly modified
-% from the 2010 version by Thorsten Joachims & Johannes Fuernkranz,
-% slightly modified from the 2009 version by Kiri Wagstaff and
-% Sam Roweis's 2008 version, which is slightly modified from
-% Prasad Tadepalli's 2007 version which is a lightly
-% changed version of the previous year's version by Andrew Moore,
-% which was in turn edited from those of Kristian Kersting and
-% Codrina Lauth. Alex Smola contributed to the algorithmic style files.
diff --git a/report/mlp2017.sty b/report/mlp2017.sty
deleted file mode 100644
index 969517b..0000000
--- a/report/mlp2017.sty
+++ /dev/null
@@ -1,720 +0,0 @@
-% File: mlp2017.sty (LaTeX style file for ICML-2017, version of 2017-05-31)
-
-% Modified by Daniel Roy 2017: changed byline to use footnotes for affiliations, and removed emails
-
-% This file contains the LaTeX formatting parameters for a two-column
-% conference proceedings that is 8.5 inches wide by 11 inches high.
-%
-% Modified by Percy Liang 12/2/2013: changed the year, location from the previous template for ICML 2014
-
-% Modified by Fei Sha 9/2/2013: changed the year, location form the previous template for ICML 2013
-%
-% Modified by Fei Sha 4/24/2013: (1) remove the extra whitespace after the first author's email address (in %the camera-ready version) (2) change the Proceeding ... of ICML 2010 to 2014 so PDF's metadata will show up % correctly
-%
-% Modified by Sanjoy Dasgupta, 2013: changed years, location
-%
-% Modified by Francesco Figari, 2012: changed years, location
-%
-% Modified by Christoph Sawade and Tobias Scheffer, 2011: added line
-% numbers, changed years
-%
-% Modified by Hal Daume III, 2010: changed years, added hyperlinks
-%
-% Modified by Kiri Wagstaff, 2009: changed years
-%
-% Modified by Sam Roweis, 2008: changed years
-%
-% Modified by Ricardo Silva, 2007: update of the ifpdf verification
-%
-% Modified by Prasad Tadepalli and Andrew Moore, merely changing years.
-%
-% Modified by Kristian Kersting, 2005, based on Jennifer Dy's 2004 version
-% - running title. If the original title is to long or is breaking a line,
-% use \mlptitlerunning{...} in the preamble to supply a shorter form.
-% Added fancyhdr package to get a running head.
-% - Updated to store the page size because pdflatex does compile the
-% page size into the pdf.
-%
-% Hacked by Terran Lane, 2003:
-% - Updated to use LaTeX2e style file conventions (ProvidesPackage,
-% etc.)
-% - Added an ``appearing in'' block at the base of the first column
-% (thus keeping the ``appearing in'' note out of the bottom margin
-% where the printer should strip in the page numbers).
-% - Added a package option [accepted] that selects between the ``Under
-% review'' notice (default, when no option is specified) and the
-% ``Appearing in'' notice (for use when the paper has been accepted
-% and will appear).
-%
-% Originally created as: ml2k.sty (LaTeX style file for ICML-2000)
-% by P. Langley (12/23/99)
-
-%%%%%%%%%%%%%%%%%%%%
-%% This version of the style file supports both a ``review'' version
-%% and a ``final/accepted'' version. The difference is only in the
-%% text that appears in the note at the bottom of the first column of
-%% the first page. The default behavior is to print a note to the
-%% effect that the paper is under review and don't distribute it. The
-%% final/accepted version prints an ``Appearing in'' note. To get the
-%% latter behavior, in the calling file change the ``usepackage'' line
-%% from:
-%% \usepackage{icml2017}
-%% to
-%% \usepackage[accepted]{icml2017}
-%%%%%%%%%%%%%%%%%%%%
-
-\NeedsTeXFormat{LaTeX2e}
-\ProvidesPackage{mlp2017}[2017/01/01 MLP Coursework Style File]
-
-% Use fancyhdr package
-\RequirePackage{fancyhdr}
-\RequirePackage{color}
-\RequirePackage{algorithm}
-\RequirePackage{algorithmic}
-\RequirePackage{natbib}
-\RequirePackage{eso-pic} % used by \AddToShipoutPicture
-\RequirePackage{forloop}
-
-%%%%%%%% Options
-%\DeclareOption{accepted}{%
-% \renewcommand{\Notice@String}{\ICML@appearing}
- \gdef\isaccepted{1}
-%}
-\DeclareOption{nohyperref}{%
- \gdef\nohyperref{1}
-}
-
-\ifdefined\nohyperref\else\ifdefined\hypersetup
- \definecolor{mydarkblue}{rgb}{0,0.08,0.45}
- \hypersetup{ %
- pdftitle={},
- pdfauthor={},
- pdfsubject={MLP Coursework 2017-18},
- pdfkeywords={},
- pdfborder=0 0 0,
- pdfpagemode=UseNone,
- colorlinks=true,
- linkcolor=mydarkblue,
- citecolor=mydarkblue,
- filecolor=mydarkblue,
- urlcolor=mydarkblue,
- pdfview=FitH}
-
- \ifdefined\isaccepted \else
- \hypersetup{pdfauthor={Anonymous Submission}}
- \fi
-\fi\fi
-
-%%%%%%%%%%%%%%%%%%%%
-% This string is printed at the bottom of the page for the
-% final/accepted version of the ``appearing in'' note. Modify it to
-% change that text.
-%%%%%%%%%%%%%%%%%%%%
-\newcommand{\ICML@appearing}{\textit{MLP Coursework 1 2017-18}}
-
-%%%%%%%%%%%%%%%%%%%%
-% This string is printed at the bottom of the page for the draft/under
-% review version of the ``appearing in'' note. Modify it to change
-% that text.
-%%%%%%%%%%%%%%%%%%%%
-\newcommand{\Notice@String}{MLP Coursework 1 2017-18}
-
-% Cause the declared options to actually be parsed and activated
-\ProcessOptions\relax
-
-% Uncomment the following for debugging. It will cause LaTeX to dump
-% the version of the ``appearing in'' string that will actually appear
-% in the document.
-%\typeout{>> Notice string='\Notice@String'}
-
-% Change citation commands to be more like old ICML styles
-\newcommand{\yrcite}[1]{\citeyearpar{#1}}
-\renewcommand{\cite}[1]{\citep{#1}}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% to ensure the letter format is used. pdflatex does compile the
-% page size into the pdf. This is done using \pdfpagewidth and
-% \pdfpageheight. As Latex does not know this directives, we first
-% check whether pdflatex or latex is used.
-%
-% Kristian Kersting 2005
-%
-% in order to account for the more recent use of pdfetex as the default
-% compiler, I have changed the pdf verification.
-%
-% Ricardo Silva 2007
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\paperwidth=210mm
-\paperheight=297mm
-
-% old PDFLaTex verification, circa 2005
-%
-%\newif\ifpdf\ifx\pdfoutput\undefined
-% \pdffalse % we are not running PDFLaTeX
-%\else
-% \pdfoutput=1 % we are running PDFLaTeX
-% \pdftrue
-%\fi
-
-\newif\ifpdf %adapted from ifpdf.sty
-\ifx\pdfoutput\undefined
-\else
- \ifx\pdfoutput\relax
- \else
- \ifcase\pdfoutput
- \else
- \pdftrue
- \fi
- \fi
-\fi
-
-\ifpdf
-% \pdfpagewidth=\paperwidth
-% \pdfpageheight=\paperheight
- \setlength{\pdfpagewidth}{210mm}
- \setlength{\pdfpageheight}{297mm}
-\fi
-
-% Physical page layout
-
-\evensidemargin -5.5mm
-\oddsidemargin -5.5mm
-\setlength\textheight{248mm}
-\setlength\textwidth{170mm}
-\setlength\columnsep{6.5mm}
-\setlength\headheight{10pt}
-\setlength\headsep{10pt}
-\addtolength{\topmargin}{-20pt}
-
-%\setlength\headheight{1em}
-%\setlength\headsep{1em}
-\addtolength{\topmargin}{-6mm}
-
-%\addtolength{\topmargin}{-2em}
-
-%% The following is adapted from code in the acmconf.sty conference
-%% style file. The constants in it are somewhat magical, and appear
-%% to work well with the two-column format on US letter paper that
-%% ICML uses, but will break if you change that layout, or if you use
-%% a longer block of text for the copyright notice string. Fiddle with
-%% them if necessary to get the block to fit/look right.
-%%
-%% -- Terran Lane, 2003
-%%
-%% The following comments are included verbatim from acmconf.sty:
-%%
-%%% This section (written by KBT) handles the 1" box in the lower left
-%%% corner of the left column of the first page by creating a picture,
-%%% and inserting the predefined string at the bottom (with a negative
-%%% displacement to offset the space allocated for a non-existent
-%%% caption).
-%%%
-\def\ftype@copyrightbox{8}
-\def\@copyrightspace{
-% Create a float object positioned at the bottom of the column. Note
-% that because of the mystical nature of floats, this has to be called
-% before the first column is populated with text (e.g., from the title
-% or abstract blocks). Otherwise, the text will force the float to
-% the next column. -- TDRL.
-\@float{copyrightbox}[b]
-\begin{center}
-\setlength{\unitlength}{1pc}
-\begin{picture}(20,1.5)
-% Create a line separating the main text from the note block.
-% 4.818pc==0.8in.
-\put(0,2.5){\line(1,0){4.818}}
-% Insert the text string itself. Note that the string has to be
-% enclosed in a parbox -- the \put call needs a box object to
-% position. Without the parbox, the text gets splattered across the
-% bottom of the page semi-randomly. The 19.75pc distance seems to be
-% the width of the column, though I can't find an appropriate distance
-% variable to substitute here. -- TDRL.
-\put(0,0){\parbox[b]{19.75pc}{\small \Notice@String}}
-\end{picture}
-\end{center}
-\end@float}
-
-% Note: A few Latex versions need the next line instead of the former.
-% \addtolength{\topmargin}{0.3in}
-% \setlength\footheight{0pt}
-\setlength\footskip{0pt}
-%\pagestyle{empty}
-\flushbottom \twocolumn
-\sloppy
-
-% Clear out the addcontentsline command
-\def\addcontentsline#1#2#3{}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% commands for formatting paper title, author names, and addresses.
-
-%%start%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%% title as running head -- Kristian Kersting 2005 %%%%%%%%%%%%%
-
-
-%\makeatletter
-%\newtoks\mytoksa
-%\newtoks\mytoksb
-%\newcommand\addtomylist[2]{%
-% \mytoksa\expandafter{#1}%
-% \mytoksb{#2}%
-% \edef#1{\the\mytoksa\the\mytoksb}%
-%}
-%\makeatother
-
-% box to check the size of the running head
-\newbox\titrun
-
-% general page style
-\pagestyle{fancy}
-\fancyhf{}
-\fancyhead{}
-\fancyfoot{}
-% set the width of the head rule to 1 point
-\renewcommand{\headrulewidth}{1pt}
-
-% definition to set the head as running head in the preamble
-\def\mlptitlerunning#1{\gdef\@mlptitlerunning{#1}}
-
-% main definition adapting \mlptitle from 2004
-\long\def\mlptitle#1{%
-
- %check whether @mlptitlerunning exists
- % if not \mlptitle is used as running head
- \ifx\undefined\@mlptitlerunning%
- \gdef\@mlptitlerunning{#1}
- \fi
-
- %add it to pdf information
- \ifdefined\nohyperref\else\ifdefined\hypersetup
- \hypersetup{pdftitle={#1}}
- \fi\fi
-
- %get the dimension of the running title
- \global\setbox\titrun=\vbox{\small\bf\@mlptitlerunning}
-
- % error flag
- \gdef\@runningtitleerror{0}
-
- % running title too long
- \ifdim\wd\titrun>\textwidth%
- {\gdef\@runningtitleerror{1}}%
- % running title breaks a line
- \else\ifdim\ht\titrun>6.25pt
- {\gdef\@runningtitleerror{2}}%
- \fi
- \fi
-
- % if there is somthing wrong with the running title
- \ifnum\@runningtitleerror>0
- \typeout{}%
- \typeout{}%
- \typeout{*******************************************************}%
- \typeout{Title exceeds size limitations for running head.}%
- \typeout{Please supply a shorter form for the running head}
- \typeout{with \string\mlptitlerunning{...}\space prior to \string\begin{document}}%
- \typeout{*******************************************************}%
- \typeout{}%
- \typeout{}%
- % set default running title
- \chead{\small\bf Title Suppressed Due to Excessive Size}%
- \else
- % 'everything' fine, set provided running title
- \chead{\small\bf\@mlptitlerunning}%
- \fi
-
- % no running title on the first page of the paper
- \thispagestyle{empty}
-
-%%%%%%%%%%%%%%%%%%%% Kristian Kersting %%%%%%%%%%%%%%%%%%%%%%%%%
-%end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
- {\center\baselineskip 18pt
- \toptitlebar{\Large\bf #1}\bottomtitlebar}
-}
-
-
-\gdef\icmlfullauthorlist{}
-\newcommand\addstringtofullauthorlist{\g@addto@macro\icmlfullauthorlist}
-\newcommand\addtofullauthorlist[1]{%
- \ifdefined\icmlanyauthors%
- \addstringtofullauthorlist{, #1}%
- \else%
- \addstringtofullauthorlist{#1}%
- \gdef\icmlanyauthors{1}%
- \fi%
- \ifdefined\nohyperref\else\ifdefined\hypersetup%
- \hypersetup{pdfauthor=\icmlfullauthorlist}%
- \fi\fi}
-
-
-\def\toptitlebar{\hrule height1pt \vskip .25in}
-\def\bottomtitlebar{\vskip .22in \hrule height1pt \vskip .3in}
-
-\newenvironment{icmlauthorlist}{%
- \setlength\topsep{0pt}
- \setlength\parskip{0pt}
- \begin{center}
-}{%
- \end{center}
-}
-
-\newcounter{@affiliationcounter}
-\newcommand{\@pa}[1]{%
-% ``#1''
-\ifcsname the@affil#1\endcsname
- % do nothing
-\else
- \ifcsname @icmlsymbol#1\endcsname
- % nothing
- \else
- \stepcounter{@affiliationcounter}%
- \newcounter{@affil#1}%
- \setcounter{@affil#1}{\value{@affiliationcounter}}%
- \fi
-\fi%
-\ifcsname @icmlsymbol#1\endcsname
- \textsuperscript{\csname @icmlsymbol#1\endcsname\,}%
-\else
- %\expandafter\footnotemark[\arabic{@affil#1}\,]%
- \textsuperscript{\arabic{@affil#1}\,}%
-\fi
-}
-
-%\newcommand{\icmlauthor}[2]{%
-%\addtofullauthorlist{#1}%
-%#1\@for\theaffil:=#2\do{\pa{\theaffil}}%
-%}
-\newcommand{\icmlauthor}[2]{%
- \ifdefined\isaccepted
- \mbox{\bf #1}\,\@for\theaffil:=#2\do{\@pa{\theaffil}} \addtofullauthorlist{#1}%
- \else
- \ifdefined\@icmlfirsttime
- \else
- \gdef\@icmlfirsttime{1}
- \mbox{\bf Anonymous Authors}\@pa{@anon} \addtofullauthorlist{Anonymous Authors}
- \fi
- \fi
-}
-
-\newcommand{\icmlsetsymbol}[2]{%
- \expandafter\gdef\csname @icmlsymbol#1\endcsname{#2}
- }
-
-
-\newcommand{\icmlaffiliation}[2]{%
-\ifdefined\isaccepted
-\ifcsname the@affil#1\endcsname
- \expandafter\gdef\csname @affilname\csname the@affil#1\endcsname\endcsname{#2}%
-\else
- {\bf AUTHORERR: Error in use of \textbackslash{}icmlaffiliation command. Label ``#1'' not mentioned in some \textbackslash{}icmlauthor\{author name\}\{labels here\} command beforehand. }
- \typeout{}%
- \typeout{}%
- \typeout{*******************************************************}%
- \typeout{Affiliation label undefined. }%
- \typeout{Make sure \string\icmlaffiliation\space follows }
- \typeout{all of \string\icmlauthor\space commands}%
- \typeout{*******************************************************}%
- \typeout{}%
- \typeout{}%
-\fi
-\else % \isaccepted
- % can be called multiple times... it's idempotent
- \expandafter\gdef\csname @affilname1\endcsname{Anonymous Institution, Anonymous City, Anonymous Region, Anonymous Country}
-\fi
-}
-
-\newcommand{\icmlcorrespondingauthor}[2]{
-\ifdefined\isaccepted
- \ifdefined\icmlcorrespondingauthor@text
- \g@addto@macro\icmlcorrespondingauthor@text{, #1 \textless{}#2\textgreater{}}
- \else
- \gdef\icmlcorrespondingauthor@text{#1 \textless{}#2\textgreater{}}
- \fi
-\else
-\gdef\icmlcorrespondingauthor@text{Anonymous Author \textless{}anon.email@domain.com\textgreater{}}
-\fi
-}
-
-\newcommand{\icmlEqualContribution}{\textsuperscript{*}Equal contribution }
-
-\newcounter{@affilnum}
-\newcommand{\printAffiliationsAndNotice}[1]{%
-\stepcounter{@affiliationcounter}%
-{\let\thefootnote\relax\footnotetext{\hspace*{-\footnotesep}#1%
-\forloop{@affilnum}{1}{\value{@affilnum} < \value{@affiliationcounter}}{
-\textsuperscript{\arabic{@affilnum}}\ifcsname @affilname\the@affilnum\endcsname%
-\csname @affilname\the@affilnum\endcsname%
-\else
-{\bf AUTHORERR: Missing \textbackslash{}icmlaffiliation.}
-\fi
-}.
-\ifdefined\icmlcorrespondingauthor@text
-Correspondence to: \icmlcorrespondingauthor@text.
-\else
-{\bf AUTHORERR: Missing \textbackslash{}icmlcorrespondingauthor.}
-\fi
-
-\ \\
-\Notice@String
-}
-}
-}
-
-
-%\makeatother
-
-\long\def\icmladdress#1{%
- {\bf The \textbackslash{}icmladdress command is no longer used. See the example\_paper PDF .tex for usage of \textbackslash{}icmlauther and \textbackslash{}icmlaffiliation.}
-}
-
-%% keywords as first class citizens
-\def\icmlkeywords#1{%
-% \ifdefined\isaccepted \else
-% \par {\bf Keywords:} #1%
-% \fi
-% \ifdefined\nohyperref\else\ifdefined\hypersetup
-% \hypersetup{pdfkeywords={#1}}
-% \fi\fi
-% \ifdefined\isaccepted \else
-% \par {\bf Keywords:} #1%
-% \fi
- \ifdefined\nohyperref\else\ifdefined\hypersetup
- \hypersetup{pdfkeywords={#1}}
- \fi\fi
-}
-
-% modification to natbib citations
-\setcitestyle{authoryear,round,citesep={;},aysep={,},yysep={;}}
-
-% Redefinition of the abstract environment.
-\renewenvironment{abstract}
- {%
-% Insert the ``appearing in'' copyright notice.
-%\@copyrightspace
-\centerline{\large\bf Abstract}
- \vspace{-0.12in}\begin{quote}}
- {\par\end{quote}\vskip 0.12in}
-
-% numbered section headings with different treatment of numbers
-
-\def\@startsection#1#2#3#4#5#6{\if@noskipsec \leavevmode \fi
- \par \@tempskipa #4\relax
- \@afterindenttrue
-% Altered the following line to indent a section's first paragraph.
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diff --git a/report/natbib.sty b/report/natbib.sty
deleted file mode 100644
index ff0d0b9..0000000
--- a/report/natbib.sty
+++ /dev/null
@@ -1,1246 +0,0 @@
-%%
-%% This is file `natbib.sty',
-%% generated with the docstrip utility.
-%%
-%% The original source files were:
-%%
-%% natbib.dtx (with options: `package,all')
-%% =============================================
-%% IMPORTANT NOTICE:
-%%
-%% This program can be redistributed and/or modified under the terms
-%% of the LaTeX Project Public License Distributed from CTAN
-%% archives in directory macros/latex/base/lppl.txt; either
-%% version 1 of the License, or any later version.
-%%
-%% This is a generated file.
-%% It may not be distributed without the original source file natbib.dtx.
-%%
-%% Full documentation can be obtained by LaTeXing that original file.
-%% Only a few abbreviated comments remain here to describe the usage.
-%% =============================================
-%% Copyright 1993-2009 Patrick W Daly
-%% Max-Planck-Institut f\"ur Sonnensystemforschung
-%% Max-Planck-Str. 2
-%% D-37191 Katlenburg-Lindau
-%% Germany
-%% E-mail: daly@mps.mpg.de
-\NeedsTeXFormat{LaTeX2e}[1995/06/01]
-\ProvidesPackage{natbib}
- [2009/07/16 8.31 (PWD, AO)]
-
- % This package reimplements the LaTeX \cite command to be used for various
- % citation styles, both author-year and numerical. It accepts BibTeX
- % output intended for many other packages, and therefore acts as a
- % general, all-purpose citation-style interface.
- %
- % With standard numerical .bst files, only numerical citations are
- % possible. With an author-year .bst file, both numerical and
- % author-year citations are possible.
- %
- % If author-year citations are selected, \bibitem must have one of the
- % following forms:
- % \bibitem[Jones et al.(1990)]{key}...
- % \bibitem[Jones et al.(1990)Jones, Baker, and Williams]{key}...
- % \bibitem[Jones et al., 1990]{key}...
- % \bibitem[\protect\citeauthoryear{Jones, Baker, and Williams}{Jones
- % et al.}{1990}]{key}...
- % \bibitem[\protect\citeauthoryear{Jones et al.}{1990}]{key}...
- % \bibitem[\protect\astroncite{Jones et al.}{1990}]{key}...
- % \bibitem[\protect\citename{Jones et al., }1990]{key}...
- % \harvarditem[Jones et al.]{Jones, Baker, and Williams}{1990}{key}...
- %
- % This is either to be made up manually, or to be generated by an
- % appropriate .bst file with BibTeX.
- % Author-year mode || Numerical mode
- % Then, \citet{key} ==>> Jones et al. (1990) || Jones et al. [21]
- % \citep{key} ==>> (Jones et al., 1990) || [21]
- % Multiple citations as normal:
- % \citep{key1,key2} ==>> (Jones et al., 1990; Smith, 1989) || [21,24]
- % or (Jones et al., 1990, 1991) || [21,24]
- % or (Jones et al., 1990a,b) || [21,24]
- % \cite{key} is the equivalent of \citet{key} in author-year mode
- % and of \citep{key} in numerical mode
- % Full author lists may be forced with \citet* or \citep*, e.g.
- % \citep*{key} ==>> (Jones, Baker, and Williams, 1990)
- % Optional notes as:
- % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2)
- % \citep[e.g.,][]{key} ==>> (e.g., Jones et al., 1990)
- % \citep[see][pg. 34]{key}==>> (see Jones et al., 1990, pg. 34)
- % (Note: in standard LaTeX, only one note is allowed, after the ref.
- % Here, one note is like the standard, two make pre- and post-notes.)
- % \citealt{key} ==>> Jones et al. 1990
- % \citealt*{key} ==>> Jones, Baker, and Williams 1990
- % \citealp{key} ==>> Jones et al., 1990
- % \citealp*{key} ==>> Jones, Baker, and Williams, 1990
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- % \citeyearpar{key} ==>> (1990)
- % \citetext{priv. comm.} ==>> (priv. comm.)
- % \citenum{key} ==>> 11 [non-superscripted]
- % Note: full author lists depends on whether the bib style supports them;
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- %
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- %
- %
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- % To make this automatic for a given bib style, named newbib, say, make
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- % be called on THE NEXT LATEX RUN (via the aux file).
- %
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- %
- % With \citeindextrue and \citeindexfalse, one can control whether the
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- % indexing file. For this, \makeindex must also be given in the preamble.
- %
- % Package Options: (for selecting punctuation)
- % round - round parentheses are used (default)
- % square - square brackets are used [option]
- % curly - curly braces are used {option}
- % angle - angle brackets are used
\n",
+ " 'Pipeline' model composed of sequence of single input, single output layer modules\n",
+ "
\n",
+ " 
\n",
+ " Computation / data flow graph for an affine transformation $\\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}$\n",
+ "
\n",
- "\n",
- "The dashboard above is showing the `Files` tab, a list of files in the directory the notebook server was launched from. We can navigate in to a sub-directory by clicking on a directory name and back up to the parent directory by clicking the `..` link. An important point to note is that the top-most level that you will be able to navigate to is the directory you run the server from. This is a security feature and generally you should try to limit the access the server has by launching it in the highest level directory which gives you access to all the files you need to work with.\n",
- "\n",
- "As well as allowing you to launch existing notebooks, the `Files` tab of the dashboard also allows new notebooks to be created using the `New` drop-down on the right. It can also perform basic file-management tasks such as renaming and deleting files (select a file by checking the box alongside it to bring up a context menu toolbar).\n",
- "\n",
- "In addition to opening notebook files, we can also edit text files such as `.py` source files, directly in the browser by opening them from the dashboard. The in-built text-editor is less-featured than a full IDE but is useful for quick edits of source files and previewing data files.\n",
- "\n",
- "The `Running` tab of the dashboard gives a list of the currently running notebook instances. This can be useful to keep track of which notebooks are still running and to shutdown (or reopen) old notebook processes when the corresponding tab has been closed.\n",
- "\n",
- "### The notebook interface\n",
- "\n",
- "The top of your notebook window should appear something like this:\n",
- "\n",
- "
\n",
- "\n",
- "The name of the current notebook is displayed at the top of the page and can be edited by clicking on the text of the name. Displayed alongside this is an indication of the last manual *checkpoint* of the notebook file. On-going changes are auto-saved at regular intervals; the check-point mechanism is mainly meant as a way to recover an earlier version of a notebook after making unwanted changes. Note the default system only currently supports storing a single previous checkpoint despite the `Revert to checkpoint` dropdown under the `File` menu perhaps suggesting otherwise.\n",
- "\n",
- "As well as having options to save and revert to checkpoints, the `File` menu also allows new notebooks to be created in same directory as the current notebook, a copy of the current notebook to be made and the ability to export the current notebook to various formats.\n",
- "\n",
- "The `Edit` menu contains standard clipboard functions as well as options for reorganising notebook *cells*. Cells are the basic units of notebooks, and can contain formatted text like the one you are reading at the moment or runnable code as we will see below. The `Edit` and `Insert` drop down menus offer various options for moving cells around the notebook, merging and splitting cells and inserting new ones, while the `Cell` menu allow running of code cells and changing cell types.\n",
- "\n",
- "The `Kernel` menu offers some useful commands for managing the Python process (kernel) running in the notebook. In particular it provides options for interrupting a busy kernel (useful for example if you realise you have set a slow code cell running with incorrect parameters) and to restart the current kernel. This will cause all variables currently defined in the workspace to be lost but may be necessary to get the kernel back to a consistent state after polluting the namespace with lots of global variables or when trying to run code from an updated module and `reload` is failing to work. \n",
- "\n",
- "To the far right of the menu toolbar is a kernel status indicator. When a dark filled circle is shown this means the kernel is currently busy and any further code cell run commands will be queued to happen after the currently running cell has completed. An open status circle indicates the kernel is currently idle.\n",
- "\n",
- "The final row of the top notebook interface is the notebook toolbar which contains shortcut buttons to some common commands such as clipboard actions and cell / kernel management. If you are interested in learning more about the notebook user interface you may wish to run through the `User Interface Tour` under the `Help` menu drop down.\n",
- "\n",
- "### Markdown cells: easy text formatting\n",
- "\n",
- "This entire introduction has been written in what is termed a *Markdown* cell of a notebook. [Markdown](https://en.wikipedia.org/wiki/Markdown) is a lightweight markup language intended to be readable in plain-text. As you may wish to use Markdown cells to keep your own formatted notes in notebooks, a small sampling of the formatting syntax available is below (escaped mark-up on top and corresponding rendered output below that); there are many much more extensive syntax guides - for example [this cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet).\n",
- "\n",
- "---\n",
- "\n",
- "```\n",
- "## Level 2 heading\n",
- "### Level 3 heading\n",
- "\n",
- "*Italicised* and **bold** text.\n",
- "\n",
- " * bulleted\n",
- " * lists\n",
- " \n",
- "and\n",
- "\n",
- " 1. enumerated\n",
- " 2. lists\n",
- "\n",
- "Inline maths $y = mx + c$ using [MathJax](https://www.mathjax.org/) as well as display style\n",
- "\n",
- "$$ ax^2 + bx + c = 0 \\qquad \\Rightarrow \\qquad x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a} $$\n",
- "```\n",
- "---\n",
- "\n",
- "## Level 2 heading\n",
- "### Level 3 heading\n",
- "\n",
- "*Italicised* and **bold** text.\n",
- "\n",
- " * bulleted\n",
- " * lists\n",
- " \n",
- "and\n",
- "\n",
- " 1. enumerated\n",
- " 2. lists\n",
- "\n",
- "Inline maths $y = mx + c$ using [MathJax]() as well as display maths\n",
- "\n",
- "$$ ax^2 + bx + c = 0 \\qquad \\Rightarrow \\qquad x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a} $$\n",
- "\n",
- "---\n",
- "\n",
- "We can also directly use HTML tags in Markdown cells to embed rich content such as images and videos.\n",
- "\n",
- "---\n",
- "```\n",
- "
\n",
- "\n",
- "For a layer with parameters, the gradients with respect to the layer outputs are required to calculate gradients with respect to the layer parameters. Therefore by combining back-propagation of gradients through the model with computing the gradients with respect to parameters in the relevant layers we can calculate gradients of the error function with respect to all of the parameters of a multiple-layer model in a very efficient manner (in fact the computational cost of computing gradients with respect to all of the parameters of the model using this method will only be a constant factor times the cost of calculating the model outputs in the forward pass).\n",
- "\n",
- "We so far have abstractly talked about calculating gradients with respect to the inputs of a layer using gradients with respect to the layer outputs. More concretely we will be using the chain rule for derivatives to do this, similarly to how we used the chain rule in exercise 4 of the previous notebook to calculate gradients with respect to the parameters of an affine layer given gradients with respect to the outputs of the layer.\n",
- "\n",
- "In particular if our layer has a batch of $B$ vector inputs each of dimension $D$, $\\fset{\\vct{x}^{(b)}}_{b=1}^B$, and produces a batch of $B$ vector outputs each of dimension $K$, $\\fset{\\vct{y}^{(b)}}_{b=1}^B$, then we can calculate the gradient with respect to the $d^\\textrm{th}$ dimension of the $b^{\\textrm{th}}$ input given the gradients with respect to the $b^{\\textrm{th}}$ output using\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} \\pd{y^{(b)}_k}{x^{(b)}_d} \\rpa.\n",
- "\\end{equation}\n",
- "\n",
- "Mathematically therefore the `bprop` method takes an array of gradients with respect to the outputs $\\pd{E}{y^{(b)}_k}$ and applies a sum-product operation with the partial derivatives of each output with respect to each input $\\pd{y^{(b)}_k}{x^{(b)}_d}$ to produce gradients with respect to the inputs of the layer $\\pd{E}{x^{(b)}_d}$.\n",
- "\n",
- "For the affine transformation used in the `AffineLayer` implemented last week, i.e a forward propagation corresponding to \n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_k = \\sum_{d=1}^D \\lpa W_{kd} x^{(b)}_d \\rpa + b_k\n",
- "\\end{equation}\n",
- "\n",
- "then the corresponding partial derivatives of layer outputs with respect to inputs are\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{y^{(b)}_k}{x^{(b)}_d} = W_{kd}\n",
- "\\end{equation}\n",
- "\n",
- "and so the back-propagation method for the `AffineLayer` takes the following form\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} W_{kd} \\rpa.\n",
- "\\end{equation}\n",
- "\n",
- "This can be efficiently implemented in NumPy using the `dot` function\n",
- "\n",
- "```python\n",
- "class AffineLayer(LayerWithParameters):\n",
- "\n",
- " # ... [implementation of remaining methods from previous week] ...\n",
- " \n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " return grads_wrt_outputs.dot(self.weights)\n",
- "```\n",
- "\n",
- "An important special case applies when the outputs of a layer are an elementwise function of the inputs such that $y^{(b)}_k$ only depends on $x^{(b)}_d$ when $d = k$. In this case the partial derivatives $\\pd{y^{(b)}_k}{x^{(b)}_d}$ will be zero for $k \\neq d$ and so the above summation collapses to a single term, giving\n",
- "\n",
- "\\begin{equation}\n",
- " \\pd{E}{x^{(b)}_d} = \\pd{E}{y^{(b)}_d} \\pd{y^{(b)}_d}{x^{(b)}_d}\n",
- "\\end{equation}\n",
- "\n",
- "i.e. to calculate the gradient with respect to the $b^{\\textrm{th}}$ input vector we just perform an elementwise multiplication of the gradient with respect to the $b^{\\textrm{th}}$ output vector with the vector of derivatives of the outputs with respect to the inputs. This case applies to the `SigmoidLayer` and to all other layers applying an elementwise function to their inputs.\n",
- "\n",
- "For the logistic sigmoid layer we have that\n",
- "\n",
- "\\begin{equation}\n",
- " y^{(b)}_d = \\frac{1}{1 + \\exp(-x^{(b)}_d)}\n",
- " \\qquad\n",
- " \\Rightarrow\n",
- " \\qquad\n",
- " \\pd{y^{(b)}_d}{x^{(b)}_d} = \n",
- " \\frac{\\exp(-x^{(b)}_d)}{\\lsb 1 + \\exp(-x^{(b)}_d) \\rsb^2} =\n",
- " y^{(b)}_d \\lsb 1 - y^{(b)}_d \\rsb\n",
- "\\end{equation}\n",
- "\n",
- "which you should now be able relate to the implementation of `SigmoidLayer.bprop` given earlier:\n",
- "\n",
- "```python\n",
- "class SigmoidLayer(Layer):\n",
- "\n",
- " def fprop(self, inputs):\n",
- " return 1. / (1. + np.exp(-inputs))\n",
- "\n",
- " def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
- " return grads_wrt_outputs * outputs * (1. - outputs)\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 1: training a softmax model on MNIST\n",
- "\n",
- "For this first exercise we will train a model consisting of an affine transformation plus softmax on a multiclass classification task: classifying the digit labels for handwritten digit images from the MNIST data set introduced in the first notebook.\n",
- "\n",
- "First run the cell below to import the necessary modules and classes and to load the MNIST data provider objects. As it takes a little while to load the MNIST data from disk into memory it is worth loading the data providers just once in a separate cell like this rather than recreating the objects for every training run.\n",
- "\n",
- "We are loading two data provider objects here - one corresponding to the training data set and a second to use as a *validation* data set. This is data we do not train the model on but measure the performance of the trained model on to assess its ability to *generalise* to unseen data. \n",
- "\n",
- "If you are in the Monday or Tuesday lab sessions you will not yet have had the lecture introducing the concepts of generalisation and validation data sets (though those doing MLPR alongside this course should already be familiar with these ideas). As you will need to report both training and validation set performances in your experiments for the first coursework assignment we are providing code here to give an example of how to do this."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import logging\n",
- "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer\n",
- "from mlp.errors import CrossEntropyError, CrossEntropySoftmaxError\n",
- "from mlp.models import SingleLayerModel, MultipleLayerModel\n",
- "from mlp.initialisers import UniformInit\n",
- "from mlp.learning_rules import GradientDescentLearningRule\n",
- "from mlp.data_providers import MNISTDataProvider\n",
- "from mlp.optimisers import Optimiser\n",
- "%matplotlib inline\n",
- "plt.style.use('ggplot')\n",
- "\n",
- "# Seed a random number generator\n",
- "seed = 6102016 \n",
- "rng = np.random.RandomState(seed)\n",
- "\n",
- "# Set up a logger object to print info about the training run to stdout\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
- "logger.handlers = [logging.StreamHandler()]\n",
- "\n",
- "# Create data provider objects for the MNIST data set\n",
- "train_data = MNISTDataProvider('train', rng=rng)\n",
- "valid_data = MNISTDataProvider('valid', rng=rng)\n",
- "input_dim, output_dim = 784, 10"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To minimise replication of code and allow you to run experiments more quickly a helper function is provided below which trains a model and plots the evolution of the error and classification accuracy of the model (on both training and validation sets) over training."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "def train_model_and_plot_stats(\n",
- " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval):\n",
- "\n",
- " # As well as monitoring the error over training also monitor classification\n",
- " # accuracy i.e. proportion of most-probable predicted classes being equal to targets\n",
- " data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
- "\n",
- " # Use the created objects to initialise a new Optimiser instance.\n",
- " optimiser = Optimiser(\n",
- " model, error, learning_rule, train_data, valid_data, data_monitors)\n",
- "\n",
- " # Run the optimiser for 5 epochs (full passes through the training set)\n",
- " # printing statistics every epoch.\n",
- " stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
- "\n",
- " # Plot the change in the validation and training set error over training.\n",
- " fig_1 = plt.figure(figsize=(8, 4))\n",
- " ax_1 = fig_1.add_subplot(111)\n",
- " for k in ['error(train)', 'error(valid)']:\n",
- " ax_1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- " ax_1.legend(loc=0)\n",
- " ax_1.set_xlabel('Epoch number')\n",
- "\n",
- " # Plot the change in the validation and training set accuracy over training.\n",
- " fig_2 = plt.figure(figsize=(8, 4))\n",
- " ax_2 = fig_2.add_subplot(111)\n",
- " for k in ['acc(train)', 'acc(valid)']:\n",
- " ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
- " stats[1:, keys[k]], label=k)\n",
- " ax_2.legend(loc=0)\n",
- " ax_2.set_xlabel('Epoch number')\n",
- " \n",
- " return stats, keys, run_time, fig_1, ax_1, fig_2, ax_2"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Running the cell below will create a model consisting of an affine layer follower by a softmax transformation and train it on the MNIST data set by minimising the multi-class cross entropy error function using a basic gradient descent learning rule. By using the helper function defined above, at the end of training curves of the evolution of the error function and also classification accuracy of the model over the training epochs will be plotted.\n",
- "\n",
- "You should try running the code for various settings of the training hyperparameters defined at the beginning of the cell to get a feel for how these affect how training proceeds. You may wish to create multiple copies of the cell below to allow you to keep track of and compare the results across different hyperparameter settings."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Varying initialisation scale\n",
- "\n",
- "First try a few different parameter initialisation scales"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### `init_scale = 0.01`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 5: 6.4s to complete\n",
- " error(train)=3.10e-01, acc(train)=9.14e-01, error(valid)=2.91e-01, acc(valid)=9.18e-01\n",
- "Epoch 10: 5.4s to complete\n",
- " error(train)=2.88e-01, acc(train)=9.20e-01, error(valid)=2.76e-01, acc(valid)=9.23e-01\n",
- "Epoch 15: 3.5s to complete\n",
- " error(train)=2.78e-01, acc(train)=9.23e-01, error(valid)=2.69e-01, acc(valid)=9.24e-01\n",
- "Epoch 20: 4.2s to complete\n",
- " error(train)=2.71e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
- "Epoch 25: 4.7s to complete\n",
- " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.65e-01, acc(valid)=9.26e-01\n",
- "Epoch 30: 3.7s to complete\n",
- " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
- "Epoch 35: 3.8s to complete\n",
- " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
- "Epoch 40: 4.2s to complete\n",
- " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n",
- "Epoch 45: 4.3s to complete\n",
- " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 50: 4.0s to complete\n",
- " error(train)=2.54e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
- "Epoch 55: 3.6s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
- "Epoch 60: 4.4s to complete\n",
- " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 65: 3.8s to complete\n",
- " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 70: 4.3s to complete\n",
- " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
- "Epoch 75: 3.5s to complete\n",
- " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.57e-01, acc(valid)=9.30e-01\n",
- "Epoch 80: 4.0s to complete\n",
- " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
- "Epoch 85: 3.9s to complete\n",
- " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
- "Epoch 90: 3.6s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.57e-01, acc(valid)=9.29e-01\n",
- "Epoch 95: 4.6s to complete\n",
- " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
- "Epoch 100: 3.8s to complete\n",
- " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n"
- ]
- },
- {
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BQICCggKys7ObLFNaWhq6vn79enr27AlAdnY2BQUF1NfXU1ZWRmlpKQMGDLD4LVhDjbgC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\nEEJ44i/5hIwFo7U1lwAAAABJRU5ErkJggg==\n",
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