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Feature-wise normalization of the data.
This layer will coerce its inputs into a normal 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
axis: Integer or tuple of integers, the axis or axes that should be normalized (typically the features axis). We will normalize each element in the specified axis. The default is '-1' (the innermost axis); 0 (the batch axis) is not allowed.
__init__( axis=-1, dtype=None, **kwargs )
adapt( data, reset_state=True )
Fits the state of the preprocessing layer to the data being passed.
data: The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.
reset_state: Optional argument specifying whether to clear the state of the layer at the start of the call to
adapt, or whether to start from the existing state. Subclasses may choose to throw if reset_state is set to 'False'.