Protopipe

Batch normalization layer

This card is a wrapper of this Keras class.

"Batch normalization layer" card

Note: the backend for building and training neural networks is based on Keras. The documentation of this card is a variant of the documentation of its corresponding class.

Inputs

Axis — Integer

The axis that should be normalized (typically the features axis). For instance, after a Convolution 2D layer with Data format = “Channels first”, set Axis = “1”.
Momentum — Float

Momentum for the moving average.
Epsilon — Float

Small float added to variance to avoid dividing by zero.
Center — Boolean

If “true”, add offset of beta to normalized tensor. If “false”, beta is ignored.
Scale — Boolean

If “true”, multiply by gamma. If “false”, gamma is not used. When the next layer is linear (also e.g. ReLU), this can be disabled since the scaling will be done by the next layer.
Beta initializer — NeuralNetworkInitializer

Initializer for the beta weight. If not specified, then Zeros initializer is used.
Gamma initializer — NeuralNetworkInitializer

Initializer for the gamma weight. If not specified, then Ones initializer is used.
Moving mean initializer — NeuralNetworkInitializer

Initializer for the moving mean. If not specified, then Zeros initializer is used.
Moving variance initializer — NeuralNetworkInitializer

Initializer for the moving variance. If not specified, then Ones initializer is used.
Beta regularizer — NeuralNetworkRegularizer

Optional regularizer for the beta weight.
Gamma regularizer — NeuralNetworkRegularizer

Optional regularizer for the gamma weight.
Beta constraint — NeuralNetworkConstraint

Optional constraint for the beta weight.
Gamma constraint — NeuralNetworkConstraint

Optional constraint for the gamma weight.
Input — NeuralNetworkTensor

Input of this layer.

Outputs

Layer instance — NeuralNetworkLayer

Instance of this layer. It can be wrapped using a Bidirectional or a TimeDistributed wrapper.
Output — NeuralNetworkTensor

Output of this layer.