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**KNIME Deep Learning - Keras Integration** version **4.3.0.v202012011122** by **KNIME AG, Zurich, Switzerland**

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

- 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.
- 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.

- 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.

- 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.

- 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.

- The Keras deep learning network to which to add an
`LSTM`

layer. The input must have shape [time, features] - 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. - >
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.

- Keras Input Layer (34 %)
- Keras Embedding Layer (28 %)
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- Keras Dropout Layer (4 %)
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To use this node in KNIME, install KNIME Deep Learning - Keras Integration from the following update site:

KNIME 4.3

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

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