RNN (Deep Java Library 0.20.0 API specification)

java.lang.Object
- ai.djl.nn.AbstractBaseBlock
- - ai.djl.nn.AbstractBlock
  - - ai.djl.nn.recurrent.RecurrentBlock
    - - ai.djl.nn.recurrent.RNN

All Implemented Interfaces:

Block
```
public class RNN
extends RecurrentBlock
```
RNN is an implementation of recurrent neural networks which applies a single-gate recurrent layer to input. Two kinds of activation function are supported: ReLU and Tanh.
Current implementation refers the [paper](https://crl.ucsd.edu/~elman/Papers/fsit.pdf), Finding structure in time - Elman, 1988.
The RNN operator is formulated as below:
With ReLU activation function: \(h_t = relu(W_{ih} * x_t + b_{ih} + W_{hh} * h_{(t-1)} + b_{hh})\)
With Tanh activation function: \(h_t = \tanh(W_{ih} * x_t + b_{ih} + W_{hh} * h_{(t-1)} + b_{hh})\)

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`static class`	`RNN.Activation`	An enum that enumerates the type of activation.
`static class`	`RNN.Builder`	The Builder to construct a `RNN` type of `Block`.

Nested classes/interfaces inherited from class ai.djl.nn.recurrent.RecurrentBlock
RecurrentBlock.BaseBuilder<T extends RecurrentBlock.BaseBuilder>

Field Summary
- Fields inherited from class ai.djl.nn.recurrent.RecurrentBlock
  batchFirst, bidirectional, dropRate, gates, hasBiases, numLayers, returnState, stateSize
- Fields inherited from class ai.djl.nn.AbstractBlock
  children, parameters
- Fields inherited from class ai.djl.nn.AbstractBaseBlock
  inputNames, inputShapes, version

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static RNN.Builder`	`builder()`	Creates a builder to build a `RNN`.
`protected NDList`	`forwardInternal(ParameterStore parameterStore, NDList inputs, boolean training, ai.djl.util.PairList<java.lang.String,java.lang.Object> params)`	A helper for `Block.forward(ParameterStore, NDList, boolean, PairList)` after initialization.

Methods inherited from class ai.djl.nn.recurrent.RecurrentBlock
beforeInitialize, getNumDirections, getOutputShapes, loadMetadata, prepare

Methods inherited from class ai.djl.nn.AbstractBlock
addChildBlock, addChildBlock, addChildBlockSingleton, addParameter, getChildren, getDirectParameters

Methods inherited from class ai.djl.nn.AbstractBaseBlock
cast, clear, describeInput, forward, forward, forwardInternal, getInputShapes, getParameters, initialize, initializeChildBlocks, isInitialized, loadParameters, readInputShapes, saveInputShapes, saveMetadata, saveParameters, setInitializer, setInitializer, setInitializer, toString

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface ai.djl.nn.Block
forward, freezeParameters

- Method Detail
  - forwardInternal
```
protected NDList forwardInternal(ParameterStore parameterStore,
                                 NDList inputs,
                                 boolean training,
                                 ai.djl.util.PairList<java.lang.String,java.lang.Object> params)
```
    A helper for Block.forward(ParameterStore, NDList, boolean, PairList) after initialization.
    
    Specified by:
    
    forwardInternal in class AbstractBaseBlock
    
    Parameters:
    
    parameterStore - the parameter store
    
    inputs - the input NDList
    
    training - true for a training forward pass
    
    params - optional parameters
    
    Returns:
    
    the output of the forward pass
  - builder
```
public static RNN.Builder builder()
```
    Creates a builder to build a RNN.
    
    Returns:
    
    a new builder