PrettyTensor provides a thin wrapper on top of TensorFlow. The objects provided by PrettyTensor support a chainable syntax to define neural networks. For example, a model could be created by chaining the layers as shown in the following code:
model = (X.
flatten().
fully_connected(10).
softmax_classifier(n_classes, labels=Y))
PrettyTensor can be installed in Python 3 with the following command:
pip3 install prettytensor
PrettyTensor offers a very lightweight and extensible interface in the form of a method named apply(). Any additional function can be chained to PrettyTensor objects using the .apply(function, arguments) method. PrettyTensor will call the function and supply the current tensor as the first argument to the function.
User-created functions can be added using the @prettytensor.register decorator. Details can be found at https:...
