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SnakeGame

Using a Neural Network to train a snake to play the snake game. This project uses the Keras module for the construction of the Neural Netowork and PyGame to create the User Interface.

Keras: Deep Learning library for TensorFlow and Theano

Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

  • Allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
  • Supports both convolutional networks and recurrent networks, as well as combinations of the two.
  • Supports arbitrary connectivity schemes (including multi-input and multi-output training).
  • Runs seamlessly on CPU and GPU.

Read the documentation at Keras.io.

Keras is compatible with: Python 2.7-3.5.

Installation

Keras uses the following dependencies:

  • numpy, scipy
  • pyyaml
  • HDF5 and h5py (optional, required if you use model saving/loading functions)
  • Optional but recommended if you use CNNs: cuDNN.

When using the TensorFlow backend:

When using the Theano backend:

To install Keras, cd to the Keras folder and run the install command:

sudo python setup.py install

You can also install Keras from PyPI:

sudo pip install keras

Switching from TensorFlow to Theano

By default, Keras will use TensorFlow as its tensor manipulation library. Follow these instructions to configure the Keras backend.