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**KNIME 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.

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

KNIME 4.3

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