update cw2

.gitignore

@@ -1,6 +1,6 @@
-
 #dropbox stuff
 *.dropbox*
+.idea/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -26,7 +26,7 @@ var/
 *.egg-info/
 .installed.cfg
 *.egg
-etc/
+*.tar.gz
 
 # PyInstaller
 #  Usually these files are written by a python script from a template
@@ -64,14 +64,23 @@ target/
 # Pycharm
 .idea/*
 
-# Notebook stuff
+#Notebook stuff
 notebooks/.ipynb_checkpoints/
 
-# Data folder
+#Google Cloud stuff
+/google-cloud-sdk
+.ipynb_checkpoints/
+data/cifar-100-python/
+data/MNIST/
 solutions/
-
-# Latex stuff
 report/mlp-cw1-template.aux
 report/mlp-cw1-template.out
 report/mlp-cw1-template.pdf
 report/mlp-cw1-template.synctex.gz
+.DS_Store
+report/mlp-cw2-template.aux
+report/mlp-cw2-template.out
+report/mlp-cw2-template.pdf
+report/mlp-cw2-template.synctex.gz
+report/mlp-cw2-template.bbl
+report/mlp-cw2-template.blg

README.md

@@ -1,6 +1,6 @@
 # Machine Learning Practical
 
-This repository contains the code for the University of Edinburgh [School of Informatics](http://www.inf.ed.ac.uk) course [Machine Learning Practical](http://www.inf.ed.ac.uk/teaching/courses/mlp/).
+This repository contains the code for the University of Edinburgh [School of Informatics](http://www.inf.ed.ac.uk) course Machine Learning Practical.
 
 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.
 
@@ -9,7 +9,11 @@ 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.
 
-## Coursework 2
+## Remote working
-This branch contains the python code and latex files of the first coursework. The code follows the same structure as the labs, in particular the mlp package, and a specific notebook is provided to help you run experiments.
+
- * Detailed instructions are given in MLP2024_25_CW2_Spec.pdf (see Learn, Assessment, CW2).
+If you are working remotely, follow this [guide](notes/remote-working-guide.md).
- * The [report directory](https://github.com/VICO-UoE/mlpractical/tree/mlp2024-25/coursework2/report) contains the latex files that you will use to create your report.
+
+## 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.
+

install.sh

@@ -0,0 +1 @@
+conda install tqdm

mlp/errors.py

@@ -57,7 +57,7 @@ class BinaryCrossEntropyError(object):
             Scalar error function value.
         """
         return -np.mean(
-            targets * np.log(outputs) + (1. - targets) * np.log(1. - outputs))
+            targets * np.log(outputs) + (1. - targets) * np.log(1. - ouputs))
 
     def grad(self, outputs, targets):
         """Calculates gradient of error function with respect to outputs.

run_vgg_08_default.sh

@@ -0,0 +1 @@
+python pytorch_mlp_framework/train_evaluate_image_classification_system.py --batch_size 100 --seed 0 --num_filters 32 --num_stages 3 --num_blocks_per_stage 0 --experiment_name VGG_08_experiment --use_gpu True --num_classes 100 --block_type 'conv_block' --continue_from_epoch -1

run_vgg_38_default.sh

@@ -0,0 +1 @@
+python pytorch_mlp_framework/train_evaluate_image_classification_system.py --batch_size 100 --seed 0 --num_filters 32 --num_stages 3 --num_blocks_per_stage 5 --experiment_name VGG_38_experiment --use_gpu True --num_classes 100 --block_type 'conv_block' --continue_from_epoch -1

scripts/secure-notebook-server.sh

@@ -23,7 +23,7 @@ fi
 # Get user to enter notebook server password
 echo "Getting notebook server password hash. Enter password when prompted ..."
 printf $SEPARATOR
-HASH=$(python -c "from jupyter_server.auth import passwd; print(passwd());")
+HASH=$(python -c "from notebook.auth import passwd; print(passwd());")
 printf $SEPARATOR
 echo "... got password hash."
 # Generate self-signed OpenSSL certificate and key file

setup.py

@@ -7,7 +7,7 @@ setup(
     author = "Pawel Swietojanski, Steve Renals, Matt Graham and Antreas Antoniou",
     description = ("Neural network framework for University of Edinburgh "
                    "School of Informatics Machine Learning Practical course."),
-    url = "https://github.com/VICO-UoE/mlpractical",
+    url = "https://github.com/CSTR-Edinburgh/mlpractical",
     packages=['mlp']
 )