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Feature-wise normalization of the data.
tf.keras.layers.experimental.preprocessing.Normalization(
axis=-1, dtype=None, **kwargs
)
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 fit
, evaluate
,
or predict
.
Attributes | |
---|---|
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. |
Methods
adapt
adapt(
data, reset_state=True
)
Fits the state of the preprocessing layer to the data being passed.
Arguments | |
---|---|
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'.
|