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 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.
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:
- TensorFlow
When using the Theano backend:
- Theano
To install Keras, cd to the Keras folder and run the install command:
sudo python setup.py installYou can also install Keras from PyPI:
sudo pip install kerasBy default, Keras will use TensorFlow as its tensor manipulation library. Follow these instructions to configure the Keras backend.