Keras and TensorFlow installation

Previously, we have seen practical examples of the SkFlow wrapper for TensorFlow applications. For a more sophisticated approach to neural networks and deep learning where we have more control over parameters, we propose Keras (http://keras.io/). This package was originally developed within the Theano framework, but recently is also adapted to TensorFlow. This way, we can use Keras as a higher abstract package on top of TensorFlow. Keep in mind though that Keras is slightly less straightforward than SkFlow in its methods. Keras can run on both GPU and CPU, which makes this package really flexible when porting it to different environments.

Let's first install Keras and make sure that it utilizes the TensorFlow backend.

Installation works simply using pip in the command line:

$pip install Keras

Keras is originally built on top of Theano, so we need to specify Keras to utilize TensorFlow instead. In order to do this, we first need to run Keras once on its default...