Keras Dropout Layer

Applies dropout to the layer input. Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting. Corresponds to the Keras Dropout 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.
Drop rate: Fraction of the input units to drop.
Noise shape: The shape of the binary dropout mask that will be multiplied with the input. The noise shape has to include the batch dimension which means in case of 2D images with shape [height, width, channels], the noise shape must must have rank 4 i.e. [batch, height, width, channels]. In order to reuse the dropout mask along specific dimensions set those to '1' in the noise shape other dimensions can be set to '?'. Spatial dropout where whole feature maps are dropped can be achieved by setting noise shape to '?, 1, 1, ?'.
Random seed: Random seed to use for the dropping.

Input Ports

: The Keras deep learning network to which to add a Dropout layer.

Output Ports

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

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Installation

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:

v5.3

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

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.3.0.v202312081534

On NodePit since: 2024-07-09

Last update: 2024-11-21

KNIME versions: Since v3.6

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