109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
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#!/disk/scratch/mlp/miniconda2/bin/python
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import os
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import datetime
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import numpy as np
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import tensorflow as tf
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import mlp.data_providers as data_providers
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# check necessary environment variables are defined
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assert 'MLP_DATA_DIR' in os.environ, (
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'An environment variable MLP_DATA_DIR must be set to the path containing'
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' MLP data before running script.')
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assert 'OUTPUT_DIR' in os.environ, (
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'An environment variable OUTPUT_DIR must be set to the path to write'
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' output to before running script.')
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# load data
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train_data = data_providers.MNISTDataProvider('train', batch_size=50)
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valid_data = data_providers.MNISTDataProvider('valid', batch_size=50)
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valid_inputs = valid_data.inputs
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valid_targets = valid_data.to_one_of_k(valid_data.targets)
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# define model graph
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with tf.name_scope('data'):
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inputs = tf.placeholder(tf.float32, [None, 784], name='inputs')
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targets = tf.placeholder(tf.float32, [None, 10], name='targets')
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with tf.name_scope('parameters'):
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weights = tf.Variable(tf.zeros([784, 10]), name='weights')
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biases = tf.Variable(tf.zeros([10]), name='biases')
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with tf.name_scope('model'):
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outputs = tf.matmul(inputs, weights) + biases
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with tf.name_scope('error'):
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error = tf.reduce_mean(
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tf.nn.softmax_cross_entropy_with_logits(outputs, targets))
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with tf.name_scope('train'):
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train_step = tf.train.GradientDescentOptimizer(0.5).minimize(error)
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with tf.name_scope('accuracy'):
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accuracy = tf.reduce_mean(tf.cast(tf.equal(
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tf.argmax(outputs, 1), tf.argmax(targets, 1)), tf.float32))
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# add summary operations
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tf.summary.scalar('error', error)
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tf.summary.scalar('accuracy', accuracy)
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summary_op = tf.summary.merge_all()
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# create objects for writing summaries and checkpoints during training
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timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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exp_dir = os.path.join(os.environ['OUTPUT_DIR'], timestamp)
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checkpoint_dir = os.path.join(exp_dir, 'checkpoints')
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if not os.path.exists(exp_dir):
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os.makedirs(exp_dir)
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if not os.path.exists(checkpoint_dir):
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os.makedirs(checkpoint_dir)
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train_writer = tf.summary.FileWriter(os.path.join(exp_dir, 'train-summaries'))
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valid_writer = tf.summary.FileWriter(os.path.join(exp_dir, 'valid-summaries'))
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saver = tf.train.Saver()
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# create arrays to store run train / valid set stats
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num_epoch = 5
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train_accuracy = np.zeros(num_epoch)
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train_error = np.zeros(num_epoch)
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valid_accuracy = np.zeros(num_epoch)
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valid_error = np.zeros(num_epoch)
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# create session and run training loop
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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step = 0
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for e in range(num_epoch):
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for b, (input_batch, target_batch) in enumerate(train_data):
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# do train step with current batch
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_, summary, batch_error, batch_acc = sess.run(
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[train_step, summary_op, error, accuracy],
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feed_dict={inputs: input_batch, targets: target_batch})
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# add symmary and accumulate stats
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train_writer.add_summary(summary, step)
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train_error[e] += batch_error
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train_accuracy[e] += batch_acc
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step += 1
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# normalise running means by number of batches
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train_error[e] /= train_data.num_batches
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train_accuracy[e] /= train_data.num_batches
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# evaluate validation set performance
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valid_summary, valid_error[e], valid_accuracy[e] = sess.run(
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[summary_op, error, accuracy],
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feed_dict={inputs: valid_inputs, targets: valid_targets})
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valid_writer.add_summary(valid_summary, step)
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# checkpoint model variables
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saver.save(sess, os.path.join(checkpoint_dir, 'model.ckpt'), step)
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# write stats summary to stdout
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print('Epoch {0:02d}: err(train)={1:.2f} acc(train)={2:.2f}'
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.format(e + 1, train_error[e], train_accuracy[e]))
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print(' err(valid)={0:.2f} acc(valid)={1:.2f}'
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.format(valid_error[e], valid_accuracy[e]))
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# close writer and session objects
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train_writer.close()
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valid_writer.close()
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sess.close()
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# save run stats to a .npz file
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np.savez_compressed(
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os.path.join(exp_dir, 'run.npz'),
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train_error=train_error,
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train_accuracy=train_accuracy,
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valid_error=valid_error,
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valid_accuracy=valid_accuracy
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)
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