timwroge/recurrent-whisperer

RecurrentWhisperer - A general class template for training recurrent neural networks using Tensorflow.

Written using Python 2.7.12.

Recommended Installation

The recommended installation is to assemble all dependencies in a virtual environment.

To create a new virtual environment specific to Python 2.7, enter at the command line:

$ virtualenv --system-site-packages -p python2.7 your-virtual-env-name

where your-virtual-env-name is a path to the the virtual environment you would like to create (e.g.: /home/rwhisp). Then activate your new virtual environment:

$ source your-virtual-env-name/bin/activate

Next, install all dependencies in your virtual environment. This step will depend on whether you require Tensorflow with GPU support.

For GPU-enabled TensorFlow, use:

$ pip install -e git+https://github.com/mattgolub/recurrent-whisperer.git@master#egg=v1.0.0[gpu]

For CPU-only TensorFlow, use:

$ pip install -e git+https://github.com/mattgolub/recurrent-whisperer.git@master#egg=v1.0.0[cpu]

When you are finished working in your virtual environment, enter:

$ deactivate

Advanced Users

Advanced Python users may skip the Recommended Installation, opting to instead clone this repository and ensure that compatible versions of the following prerequisites are available:

• TensorFlow version 1.10 (install)
• NumPy, SciPy and Matplotlib (install SciPy stack, contains both)
• PyYaml (install)

Getting started

See FlipFlop.py for an example subclass that inherits from RecurrentWhisperer for the purposes of training an RNN to implement an N-bit memory.