CSCI-599 Assignment 1

The objective of this assignment

• Implement the forward and backward passes as well as the neural network training procedure
• Implement the widely-used optimizers and training tricks including dropout
• Get familiar with TensorFlow by training and designing a network on your own
• Learn how to fine-tune trained networks
• Visualize the learned weights and activation maps of a ConvNet
• Use Grad-CAM to visualize and reason why ConvNet makes certain predictions

Work on the assignment

Working on the assignment in a virtual environment is highly encouraged. Please see below for executing a virtual environment.

cd assignment1
sudo pip install virtualenv # If you didn't install it
virtualenv -p python3 /your/path/to/the/virtual/env
source  /your/path/to/the/virtual/env/bin/activate
pip install -r requirements.txt  # Install dependencies
# Note that this does NOT install TensorFlow,
# which you need to do yourself.
# Work on the assignment
deactivate # Exit the virtual environment

Please clone or download as .zip file of this repository.

Work with IPython Notebook

To start working on the assignment, simply run the following command to start an ipython kernel.

# port is only needed if you want to work on more than one notebooks
jupyter notebook --port=/your/port/

and then work on each problem with their corresponding .ipynb notebooks.

In this assignment, please use Python 2.7. You will need to make sure that your virtualenv setup is of the correct version of python is used.

Problems

Problem 1: Basics of Neural Networks (35 points)

The IPython Notebook Problem_1.ipynb will walk you through implementing the basic neural networks.

Problem 2: Getting familiar with TensorFlow (25 points)

The IPython Notebook Problem_2.ipynb will help you have a better understanding of implementing a simple ConvNet in Tensorflow.

Problem 3: Training and Fine-tuning on Fashion MNIST and MNIST (15 points)

The IPython Notebook Problem_3.ipynb will walk you through training a neural network from scratch on a datase and fine-tuning on another dataset.

Problem 4: Visualizations and CAM (25 points)

The IPython Notebook Problem_4.ipynb will help you have a better understanding of the skills of visualizing neural networks.

How to submit

Run the following command to zip all the necessary files for submitting your assignment.

sh collectSubmission.sh

This will create a file named assignment1.zip, please submit this file through the Google form. Do NOT create your own .zip file, you might accidentally include non-necessary materials for grading. We will deduct points if you don't follow the above submission guideline.

Questions?

If you have any question or find a bug in this assignment (or even any suggestions), we are more than welcome to assist.

Again, NO INDIVIDUAL EMAILS WILL BE RESPONDED.

PLEASE USE PIAZZA TO POST QUESTIONS (under folder assignment1).