Package org.deeplearning4j.nn.conf.layers.recurrent

Class Summary

Class	Description
Bidirectional	Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional. Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks.
Bidirectional.Builder
LastTimeStep	LastTimeStep is a "wrapper" layer: it wraps any RNN layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).
SimpleRnn	Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.
SimpleRnn.Builder

Enum Summary

Enum

Description

Bidirectional.Mode

This Mode enumeration defines how the activations for the forward and backward networks should be combined. ADD: out = forward + backward (elementwise addition) MUL: out = forward * backward (elementwise multiplication) AVERAGE: out = 0.5 * (forward + backward) CONCAT: Concatenate the activations. Where 'forward' is the activations for the forward RNN, and 'backward' is the activations for the backward RNN.