Keras Convolutional LSTM 2D Layer

2D Convolutional Long-Short Term Memory (LSTM) layer. Similar to a normal LSTM, but the input and recurrent transformations are both convolutional. Corresponds to the ConvLSTM2D 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.
Filters: Dimensionality of the output space.
Kernel size: A comma separated pair of integers that describes the size of the convolutional kernel.
Strides: A comma separated pair of positive integers that describes the stride of the network in each spatial dimension. A stride != 1 results in an output downsampled by a factor of 1/stride in the specific dimension. Note that any stride value != 1 is incompatible with specifying any dilation rate value != 1.
Padding: Same padding keeps the spatial size of the input intact (provided no stride or dilation is used) while valid padding reduces the size of the input.
Dilation rate: A comma separated pair of integers specifying the dilation rate to use for dilated convolution. Note that it is not supported to specify a stride value != 1 and a dilation rate value != 1.
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, height, width, channel] otherwise the output will have shape [height, width, channel] (for data format channel_last).
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.
Go backwards: Whether to go backwards in time i.e. read the input sequence backwards.
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.

Initializers

Kernel initializer: Initializer for the convolutional kernel used for the linear transformations of the input. See initializers for details on the available initializers.
Recurrent initializer: Initializer for the convolutional kernel 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 convolutional kernel. See regularizers for details on the available regularizers.
Recurrent regularizer: Regularizer function applied to the convolutional kernel 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 convolutional kernel for the input connection. See constraints for details on the available constraints.
Recurrent constraint: Constraint on the convolutional kernel 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

: The Keras deep learning network to which to add an ConvLSTM2D layer. The shape of the tensor must be [time, height, width, channel] or [time, channel, height, width] for data format channels_last and channels_first respectively.
: An optional Keras deep learning network providing the first initial state for this ConvLSTM2D layer. Note that if this port is connected, you also have to connect the second hidden state port. The shape must be [height, width, channel] or [channel, height, width] depending on data format and the dimensionality of the channel dimension must match the number of filters of this layer.
: > An optional Keras deep learning network providing the second initial state for this ConvLSTM2D layer. Note that if this port is connected, you also have to connect the first hidden state port. The shape must be [height, width, channel] or [channel, height, width] depending on data format and the dimensionality of the channel dimension must match the number of filters of this layer.

Output Ports

: The Keras deep learning network with an added ConvLSTM2D layer.

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To use this node in KNIME, install the extension KNIME Deep Learning - Keras Integration from the below update site following our NodePit Product and Node Installation Guide:

v4.5

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Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 4.5.0.v202108181631

On NodePit since: 2021-12-06

Last update: 2022-05-17

KNIME versions: Since v3.7