• 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

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

Working on the assignment in a virtual environment is highly encouraged. In this assignment, please use Python 3.5 (or 3.6). You will need to make sure that your virtualenv setup is of the correct version of python.

Please see below for executing a virtual environment.

cd CSCI599-Assignment1
pip3 install virtualenv # If you didn't install it
virtualenv -p $(which python3) /your/path/to/the/virtual/env
source  /your/path/to/the/virtual/env/bin/activate

# Install dependencies
pip3 install -r requirements.txt

# install tensorflow (cpu version, recommended)
pip3 install tensorflow

# install tensorflow (gpu version)
# run this command only if your device supports gpu running
pip3 install tensorflow-gpu

# Work on the assignment
deactivate # Exit the virtual environment

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

# add your virtual environment to jupyter notebook
python -m ipykernel install --user --name=/your/path/to/the/virtual/env

# 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. Check the python environment you are using on the top right corner. If the name of environment doesn't match, change it to your virtual environment in "Kernel>Change kernel".

In each of the notebook file, we indicate TODO or Your Code for you to fill in with your implementation. Majority of implementations will also be required under lib with specified tags.

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

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

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

The IPython Notebook Problem_4.ipynb will gain you insights with what neural networks learn with the skills of visualizing them.

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 rename it with your usc student id (eg. 4916525888.zip), and 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.

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).