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• Layer normalization layer

Layer normalization layer

This card is a wrapper of this Keras class.

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

• Axes — List of Integer

  The axis or axes to normalize across. Typically this is the features axis/axes. The left-out axes are typically the batch axis/axes. This argument defaults to -1, the last dimension in the input.

• 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.

• 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.