tf.keras.layers.experimental.preprocessing.Rescaling
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Multiply inputs by scale
and adds offset
.
Inherits From: PreprocessingLayer
, Layer
, Module
tf.keras.layers.experimental.preprocessing.Rescaling(
scale, offset=0.0, name=None, **kwargs
)
For instance:
To rescale an input in the [0, 255]
range
to be in the [0, 1]
range, you would pass scale=1./255
.
To rescale an input in the [0, 255]
range to be in the [-1, 1]
range,
you would pass scale=1./127.5, offset=-1
.
The rescaling is applied both during training and inference.
Arbitrary.
Output shape:
Same as input.
Arguments |
scale
|
Float, the scale to apply to the inputs.
|
offset
|
Float, the offset to apply to the inputs.
|
name
|
A string, the name of the layer.
|
Methods
adapt
View source
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. This argument may not be relevant to all
preprocessing layers: a subclass of PreprocessingLayer may choose to
throw if 'reset_state' is set to False.
|
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Last updated 2021-02-18 UTC.
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