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

Packages that use org.deeplearning4j.nn.conf.layers.recurrent

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

Classes in org.deeplearning4j.nn.conf.layers.recurrent used by org.deeplearning4j.nn.conf.layers.recurrent

Class and 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
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.
SimpleRnn Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.
SimpleRnn.Builder

Classes in org.deeplearning4j.nn.conf.layers.recurrent used by org.deeplearning4j.nn.params

Class and 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.