|View source on GitHub|
Feature-wise normalization of the data.
tf.keras.layers.experimental.preprocessing.Normalization( axis=-1, dtype=None, **kwargs )
Used in the notebooks
|Used in the guide|
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
||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.|
adapt( data, reset_state=True )
Fits the state of the preprocessing layer to the data being passed.
||The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.|
Optional argument specifying whether to clear the state of
the layer at the start of the call to