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Feature-wise normalization of the data.
See Migration guide for more details.
tf.keras.layers.Normalization( axis=-1, mean=None, variance=None, **kwargs )
This layer will coerce its inputs into a distribution centered around
0 with standard deviation 1. It accomplishes this by precomputing the mean and
variance of the data, and calling
(input - mean) / sqrt(var) at runtime.
What happens in
adapt(): Compute mean and variance of the data and store
them as the layer's weights.
adapt() should be called before
||Integer, tuple of integers, or None. The axis or axes tha|