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

Can only be run on GPU, with the TensorFlow back end. Corresponds to the CuDNNLSTM 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.
- 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 state in addition to the layer output. If selected the layer returns two tensors, the normal output and the hidden state of the layer. If sequences are returned, this also applies to the hidden state.

- 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.

- 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
`CuDNN LSTM`

layer. - An optional Keras deep learning network providing the first initial state for this
`CuDNN 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
`CuDNN 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 (39 %)
- Keras Batch Normalization Layer (16 %)
- Keras CuDNN LSTM Layer (13 %)
- Keras Convolution 1D Layer (8 %)
- Keras Reshape Layer (5 %)
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- Keras Dropout Layer (21 %)
- Keras Dense Layer (15 %)
- Keras Network Learner (15 %)
- Keras GRU Layer (13 %)
- Keras CuDNN LSTM Layer (11 %)
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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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