Pretty Tensor

Pretty Tensor allows the developer to wrap TensorFlow operations, to quickly chain any number of layers to define neural networks.

The following is a simple example of the Pretty Tensor capabilities. We wrap a standard TensorFlow object, pretty, into a library compatible object, then we feed it through three fully connected layers, to finally output a softmax distribution:

pretty = tf.placeholder([None, 784], tf.float32) 
softmax = (prettytensor.wrap(examples) 
 .fully_connected(256, tf.nn.relu) 
 .fully_connected(128, tf.sigmoid) 
 .fully_connected(64, tf.tanh) 
 .softmax(10))

The Pretty Tensor installation is very simple; just use the pip installer:

sudo pip install prettytensor

Chaining layers

Pretty Tensor has three modes of operation, which share the ability to chain methods.

Normal mode

In normal mode, every time a method is called, a new Pretty Tensor is created. This allows for easy chaining, and yet you can still use any particular object multiple times. This makes it easy...