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Keras LSTM Layer

DeprecatedKNIME Deep Learning - Keras Integration version 4.2.1.v202008251157 by KNIME AG, Zurich, Switzerland

Long-Short Term Memory (LSTM) layer. Corresponds to the LSTM Keras layer .

Options

Name prefix
The name prefix of the layer. The prefix is complemented by an index suffix to obtain a unique layer name. If this option is unchecked, the name prefix is derived from the layer type.
Input tensor
The tensor to use as input for the layer.
First hidden state tensor
The tensor to use as initial state for the first hidden state in case the corresponding port is connected.
Second hidden state tensor
The tensor to use as initial state for the second hidden state in case the corresponding port is connected.
Units
Dimensionality of the output space.
Activation
The activation function to use on the input transformations.
Recurrent activation
The activation function to use for the recurrent step.
Use bias
If checked, a bias vector will be used.
Return sequences
Whether to return the last output in the output sequence or the full output sequence. If selected the output will have shape [time, units] otherwise the output will have shape [units].
Return state
Whether to return the hidden states in addition to the layer output. If selected the layer returns three tensors, the normal output and the two hidden states of the layer. If sequences are returned, this also applies to the hidden states.
Dropout
Fraction of the units to drop for the linear transformation of the input.
Recurrent dropout
Fraction of the units to drop for the linear transformation of the recurrent state.
Go backwards
Whether to go backwards in time i.e. read the input sequence backwards.
Unroll
Whether to unroll the network i.e. convert it in a feed-forward network that reuses the layer's weights for each timestep. Unrolling can speed up an RNN but it's more memory-expensive and only suitable for short sequences. If the layer is not unrolled, a symbolic loop is used.
Implementation
Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.

Initializers

Kernel initializer
Initializer for the weight matrix used for the linear transformations of the input. See initializers for details on the available initializers.
Recurrent initializer
Initializer for the weight matrix used for the linear transformation of the recurrent connection. See initializers for details on the available initializers.
Bias initializer
Initializer for the bias vector (if a bias is used). See initializers for details on the available initializers.
Unit forget bias
If selected, add 1 to the bias of the forget gate at initialization. Use in combination with bias initializer zeros.

Regularizers

Kernel regularizer
Regularizer function applied to the weight matrix. See regularizers for details on the available regularizers.
Recurrent regularizer
Regularizer function applied to the weight matrix for the recurrent connection. See regularizers for details on the available regularizers.
Bias regularizer
Regularizer function applied to the bias vector. See regularizers for details on the available regularizers.
Activity regularizer
Regularizer function applied to the output of the layer i.e. its activation. See regularizers for details on the available regularizers.

Constraints

Kernel constraint
Constraint on the weight matrix for the input connection. See constraints for details on the available constraints.
Recurrent constraint
Constraint on the weight matrix for the recurrent connection. See constraints for details on the available constraints.
Bias constraint
Constraint on the bias vector. See constraints for details on the available constraints.

Input Ports

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The Keras deep learning network to which to add an LSTM layer. The input must have shape [time, features]
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An optional Keras deep learning network providing the first initial state for this LSTM layer. Note that if this port is connected, you also have to connect the second hidden state port. The hidden state must have shape [units], where units must correspond to the number of units this layer uses.
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> An optional Keras deep learning network providing the second initial state for this LSTM layer. Note that if this port is connected, you also have to connect the first hidden state port. The hidden state must have shape [units], where units must correspond to the number of units this layer uses.

Output Ports

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The Keras deep learning network with an added LSTM layer.

Best Friends (Incoming)

Best Friends (Outgoing)

Workflows

Installation

To use this node in KNIME, install KNIME Deep Learning - Keras Integration from the following update site:

KNIME 4.2

A zipped version of the software site can be downloaded here.

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