public class RNN extends RecurrentCell
Reference paper: Finding structure in time - Elman, 1988. https://crl.ucsd.edu/~elman/Papers/fsit.pdf
With ReLU activation function: \(h_t = relu(W_{ih} * x_t + b_{ih} + W_{hh} * h_{(t-1)} + b_{hh})\)
With Tanh activation function: \(h_t = \tanh(W_{ih} * x_t + b_{ih} + W_{hh} * h_{(t-1)} + b_{hh})\)
Modifier and Type | Class and Description |
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static class |
RNN.Activation
An enum that enumerates the type of activation.
|
static class |
RNN.Builder
|
RecurrentCell.BaseBuilder<T extends RecurrentCell.BaseBuilder>
dropRate, gates, mode, numDirections, numStackedLayers, parameters, stateOutputs, stateShape, stateSize, useSequenceLength
inputNames, inputShapes
Modifier and Type | Method and Description |
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static RNN.Builder |
builder()
Creates a builder to build a
RNN . |
beforeInitialize, forward, getDirectParameters, getOutputShapes, getParameterShape, isBidirectional, loadParameters, opInputs, saveParameters, updateInputLayoutToTNC, validateInputSize
getChildren, initialize, toString
cast, clear, describeInput, getParameters, isInitialized, readInputShapes, saveInputShapes, setInitializer, setInitializer
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
forward, validateLayout
public static RNN.Builder builder()
RNN
.