tf.keras.layers.experimental.preprocessing.Rescaling

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:

  1. To rescale an input in the [0, 255] range to be in the [0, 1] range, you would pass scale=1./255.

  2. 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.

Input shape:

Arbitrary.

Output shape:

Same as input.

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.