The objectives

• Implement the forward and backward passes as well as the neural network training procedure for Recurrent Neural Networks (RNNs)
• Learn the basic concepts of language modeling and how to apply RNNs
• Implement popular generative model, Generative Adversarial Networks (GANs)

Work on the project

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

Working on the project in a virtual environment is highly encouraged. In this project, 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 <wd>
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 project
deactivate # Exit the virtual environment

Work with IPython Notebook

To start working on the project, 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".

Problems

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.

Problem 1:

The IPython Notebook Problem_1.ipynb will walk you through implementing a recurrent neural network (RNN) from scratch.

Problem 2: Generative Adversarial Networks

The IPython Notebook Problem_2.ipynb will help you through implementing a generative adversarial network (GAN) using TensorFlow.