Namespace Keras

Classes

Activations

Activations can either be used through an Activation layer, or through the activation argument supported by all forward layers:

Backend

Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle low-level operations such as tensor products, convolutions and so on itself. Instead, it relies on a specialized, well optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. Rather than picking one single tensor library and making the implementation of Keras tied to that library, Keras handles the problem in a modular way, and several different backend engines can be plugged seamlessly into Keras.

Base

Keras

KerasFunction

KerasIterator

Losses

A loss function (or objective function, or optimization score function) is one of the two parameters required to compile a model

Metrics

A metric is a function that is used to judge the performance of your model. Metric functions are to be supplied in the metrics parameter when a model is compiled

NumpyExtension

Shape

Keras Tensor Shape

StringOrInstance

String or instance of a class