Recurrent Layers

DL4J equivalents and API reference for Keras recurrent layers — SimpleRNN, LSTM, and associated utilities.

Keras Recurrent Layer Import

Recurrent layer support is implemented in the layers/recurrent package.

Support Summary

Keras Layer	DL4J Equivalent	Supported
SimpleRNN	SimpleRnn	Yes
GRU	—	No
LSTM	LSTM	Yes
ConvLSTM2D	—	No

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KerasSimpleRnn

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Imports a Keras SimpleRNN layer as a DL4J SimpleRnn layer.

Constructor

public KerasSimpleRnn(Integer kerasVersion) throws UnsupportedKerasConfigurationException

Pass-through constructor. kerasVersion is the major Keras version (1 or 2).

getSimpleRnnLayer

public Layer getSimpleRnnLayer()

Returns the configured DL4J SimpleRnn layer.

Parameters (from Keras config):

• layerConfig — dictionary containing Keras layer configuration

getOutputType

public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException

Returns the output InputType for this RNN layer.

Parameters:

• inputType — array of input InputType objects

Returns: output InputType

getNumParams

public int getNumParams()

Returns the number of trainable parameters. For a SimpleRNN with hidden size h and input size i, this is h*(h+i) + h (recurrent weights + input weights + bias).

getInputPreprocessor

public InputPreProcessor getInputPreprocessor(InputType... inputType)
        throws InvalidKerasConfigurationException

Returns the appropriate DL4J InputPreProcessor if a format conversion is needed before the RNN layer (e.g., feed-forward to RNN).

getUnroll

public boolean getUnroll()

Returns whether this SimpleRNN layer should be unrolled for truncated BPTT. Corresponds to the Keras unroll parameter.

setWeights

public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException

Loads weights into the layer. The weight map uses DL4J parameter names derived from the Keras HDF5 structure.

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KerasLSTM

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Imports a Keras LSTM layer as a DL4J LSTM layer. Both Keras 1 and Keras 2 LSTM configurations are supported.

Constructor

public KerasLSTM(Integer kerasVersion) throws UnsupportedKerasConfigurationException

getLSTMLayer

public Layer getLSTMLayer()

Returns the configured DL4J LSTM layer.

getOutputType

public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException

getNumParams

public int getNumParams()

Returns the number of trainable parameters (12 weight matrices for 4 gates × input + recurrent + bias terms).

getInputPreprocessor

public InputPreProcessor getInputPreprocessor(InputType... inputType)
        throws InvalidKerasConfigurationException

setWeights

public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException

Loads LSTM gate weight matrices (kernel, recurrent kernel) and biases from the weight map.

getUnroll

public boolean getUnroll()

Returns whether the LSTM should be unrolled for truncated BPTT.

getGateActivationFromConfig

public IActivation getGateActivationFromConfig(Map<String, Object> layerConfig)
        throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException

Reads the LSTM gate (recurrent) activation function from the Keras layer configuration. Corresponds to the Keras recurrent_activation parameter (default: hard_sigmoid).

Parameters:

• layerConfig — dictionary containing the Keras layer configuration

Returns: DL4J IActivation for the gate activation

getForgetBiasInitFromConfig

public double getForgetBiasInitFromConfig(Map<String, Object> layerConfig, boolean train)
        throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException

Reads the forget gate bias initialization value from the Keras config. Keras 2 uses unit_forget_bias=True by default, which initializes the forget gate bias to 1.

Parameters:

• layerConfig — dictionary containing the Keras layer configuration

• train — whether this is the training phase

Returns: forget gate bias initialization value

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KerasRnnUtils

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Shared utilities for RNN layer import.

getUnrollRecurrentLayer

public static boolean getUnrollRecurrentLayer(KerasLayerConfiguration conf,
        Map<String, Object> layerConfig) throws InvalidKerasConfigurationException

Reads the unroll parameter from the Keras layer configuration. Unrolling converts the RNN to a fixed-length sequence of operations; this can improve speed on short sequences but uses more memory.

getRecurrentDropout

public static double getRecurrentDropout(KerasLayerConfiguration conf,
        Map<String, Object> layerConfig)
        throws UnsupportedKerasConfigurationException, InvalidKerasConfigurationException

Reads the recurrent dropout rate from the Keras layer configuration. Note: non-zero recurrent dropout rates are currently not supported in DL4J. If a model was trained with recurrent dropout, the dropout will be silently ignored at import time (inference behavior is unaffected).

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Input Shape Convention

DL4J RNN layers expect input in [batch, features, timesteps] format (channels-first time axis). Keras RNNs use [batch, timesteps, features]. The importer automatically inserts the necessary RnnToFeedForwardPreProcessor or FeedForwardToRnnPreProcessor at layer boundaries.

When constructing input arrays manually for inference, use [batch, features, timesteps]:

// 8 samples, 10 features, 50 timesteps
INDArray input = Nd4j.rand(8, 10, 50);
INDArray output = model.output(input);

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Unsupported Layers

• GRU: the Keras GRU gate formulation differs from DL4J's. Models using GRU must be redesigned with LSTM or SimpleRNN before import.

• ConvLSTM2D: no DL4J equivalent. Models using ConvLSTM2D cannot currently be imported.

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See Also

• Wrapper Layers (Bidirectional)

• Supported Features