In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The goals of this assignment are as follows:

  • understand Neural Networks and how they are arranged in layered architectures
  • understand and be able to implement (vectorized) backpropagation
  • implement various update rules used to optimize Neural Networks
  • implement batch normalization for training deep networks
  • implement dropout to regularize networks
  • effectively cross-validate and find the best hyperparameters for Neural Network architecture
  • understand the architecture of Convolutional Neural Networks and train gain experience with training these models on data

Setup

Get the code as a zip file here.

Please familiarize yourself with the recommended workflow before starting the assignment.

The assignment would be using Python = 3.10

Note. Ensure you are periodically saving your notebook (File -> Save) so that you don’t lose your progress if you step away from the assignment and the Colab VM disconnects.

Once you have completed all Colab notebooks except collect_submission.ipynb, proceed to the submission instructions.

Colab: If using colab, make a new folder, (ex. cs682) in google drive and upload the assignment2 folder. After opening any notebook in colab you will need to uncomment and run the first code cell. This will mount google drive and cd into the assignment2 directory. Do not forget to answer the inline questions. To write your response click on the text and then click the edit pencil icon that appears in the top right.

Download data: Once you have the starter code, you will need to download the CIFAR-10 dataset.

Inside a colab notebook, you can create a new cell (Insert -> Code cell) and run the following:

%cd ./cs682/datasets
!bash get_datasets.sh
%cd ../../

Alternatively, on your local comptuer run the following from the assignment2 directory:

cd ./cs682/datasets
./get_datasets.sh

Compile the Cython extension: Convolutional Neural Networks require a very efficient implementation. We have implemented of the functionality using Cython; you will need to compile the Cython extension before you can run the code.

Inside a colab notebook, you can create a new cell (Insert -> Code cell) and run the following:

%cd cs682
!python setup.py build_ext --inplace
%cd ..

Alternatively, on your local comptuer run the following from the assignment2/cs682 directory:

python setup.py build_ext --inplace

NOTE: Check this page if you are using windows and having the “unable to find vcvarsall.bat” error.

Jupyter Notebook: After you have the CIFAR-10 data, if you are not using colab you should start the Jupyter Notebook server from the assignment2 directory. If you are unfamiliar with Jupyter, you should read our Jupyter tutorial.

NOTE: If you are working in a virtual environment on OSX, you may encounter errors with matplotlib due to the issues described here. You can work around this issue by starting the Jupyter server using the start_jupyter_osx.sh script from the assignment2 directory; the script assumes that your virtual environment is named .env.

Q1: Fully-connected Neural Network (16 points)

The notebook FullyConnectedNets.ipynb will introduce you to our modular layer design, and then use those layers to implement fully-connected networks of arbitrary depth. To optimize these models you will implement several popular update rules.

Q2: Batch Normalization (34 points)

In the notebook BatchNormalization.ipynb you will implement batch normalization, and use it to train deep fully-connected networks.

Q3: Dropout (10 points)

The notebook Dropout.ipynb will help you implement Dropout and explore its effects on model generalization.

Q4: ConvNet on CIFAR-10 (30 points)

In the notebook ConvolutionalNetworks.ipynb you will implement several new layers that are commonly used in convolutional networks. You will train a (shallow) convolutional network on CIFAR-10, and it will then be up to you to train the best network that you can.

Q5: PyTorch (10 points)

For this last part, you will be working in PyTorch, a popular deep learning framework.

Open up either PyTorch.ipynb. There, you will learn how the framework works, culminating in training and convolutional network of your own design on CIFAR-10 to get the best performance you can.

Submitting your work

Important. Please make sure that the submitted notebooks have been run and the cell outputs are visible.

Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your work:

To make sure everything is working properly, remember to do a clean run (“Kernel -> Restart & Run All”) after you finish work for each notebook and submit the final version with all the outputs.

1. Generate a zip file of your code (.py and .ipynb) called <UmassID>.zip (For email address arnaik@umass.edu - zip file name is arnaik.zip). Please ensure you donot include the dataset folder in the zip.

2. Convert all notebooks (.ipynb files) into a single PDF file.

3. Please submit .zip and the pdf to Gradescope.

If you run code on your local machine on Linux or macOS, you can run the provided collectSubmission.sh script from assignment2/ to produce a file <UmassID>.zip.