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Keras Separable Convolution 1D Layer

DeprecatedKNIME Deep Learning - Keras Integration version 3.7.0.v201811132139 by KNIME AG, Zurich, Switzerland

This layer performs convolution in a single dimension with a factorization of the convolution kernel into two smaller kernels. Corresponds to the Keras Separable Convolution 1D 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.
Filters
The dimensionality of the output space (i.e. the number of output filters in the convolution).
Kernel size
The length of the 1D convolution window.
Strides
The stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
Padding
Different padding modes to apply to the spatial dimensions (excluding the batch and channel dimensions) of the inputs before the pooling operation. The padding will be done with zeroes. A detailed explanation of the different modes can be found here .
  • Valid: No padding
  • Same: Padding such that the spatial output dimension do not change.
  • Full: Padding with kernel size - 1
Dilation rate
Specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
Depth multiplier
The number of depthwise convolution output channels for each input channel.
Activation function
The activation function to use.
Use bias?
If checked, a bias vector will be used.
Depthwise initializer
Initializer for the depthwise kernel matrix.
Pointwise initializer
Initializer for the pointwise kernel matrix.
Bias initializer
Initializer for the bias vector.
Depthwise regularizer
Regularizer function applied to the depthwise kernel matrix.
Pointwise regularizer
Regularizer function applied to the pointwise kernel matrix.
Bias regularizer
Regularizer function applied to the bias vector.
Activation regularizer
Regularizer function applied to the output of the layer (its "activation").
Depthwise constraint
Constraint function applied to the depthwise kernel matrix.
Pointwise constraint
Constraint function applied to the pointwise kernel matrix.
Bias constraint
Constraint function applied to the bias vector.

Input Ports

The Keras deep learning network to which to add a Separable Convolution 1D layer.

Output Ports

The Keras deep learning network with an added Separable Convolution 1D layer.

Update Site

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

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