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:

PART 1. PART 2

Download data: Once you have the starter code, you will need to download the CIFAR-10 dataset. Run the following from the assignment2 directory:

cd 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. From the assignment2/cs682 directory, run the following command:

python setup.py build_ext --inplace

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

Start Jupyter Notebook

After you have the CIFAR-10 data, 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.

Part1:

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.

Part2:

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. NOTE: You would need to add code you wrote for part1 of this assignment in their analougus fils in codebase for part2. Specifically, you would need to add the forward and backward code you wrote for ‘ReLU’, ‘Batchnorm’ and ‘Affine’ layers in the ‘layers.py’ file in folder ‘cs682’ in part2 codebase.

Open up either PyTorch.ipynb or Tensorflow.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. Also, you shuld submit part1 and part2 seperately. Use the following scripts for packaging your code : Submission Scripts

Dowload the the scripts from the above link and place them inside each assignment folder.

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.