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Keras Batch Normalization Layer

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

Normalize the layer input at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1. Corresponds to the Keras Batch Normalization 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.
Axis
The axis that should be normalized (typically the features axis). For instance, after a Conv2D layer with data_format="channels_first" , set axis=1 in BatchNormalization.
Momentum
Momentum for the moving mean and the moving variance.
Epsilon
Small number added to variance to avoid dividing by zero.
Center
If selected, add offset of beta to normalized tensor. If not selected, beta is ignored.
Scale
If selected, multiply by gamma. If not selected, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer.
Beta initializer
Initializer for the beta weight.
Gamma initializer
Initializer for the gamma weight.
Moving mean initializer
Initializer for the moving mean.
Moving variance initializer
Initializer for the moving variance.
Beta regularizer
Optional regularizer for the beta weight.
Gamma regularizer
Optional regularizer for the gamma weight.
Beta constraint
Optional constraint for the beta weight.
Gamma constraint
Optional constraint for the gamma weight.

Input Ports

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The Keras deep learning network to which to add a Batch Normalization layer.

Output Ports

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The Keras deep learning network with an added Batch Normalization layer.

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Installation

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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