Keras Batch Normalization Layer

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.

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Links

• KNIME Deep Learning Keras Integration

  www.knime.com/deeplearning/keras

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