tf.keras.layers.Rescaling  |  TensorFlow v2.16.1 (original) (raw)

tf.keras.layers.Rescaling

Stay organized with collections Save and categorize content based on your preferences.

A preprocessing layer which rescales input values to a new range.

Inherits From: Layer, Operation

tf.keras.layers.Rescaling(
    scale, offset=0.0, **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials
Working with preprocessing layers Image classification Load and preprocess images Data augmentation Transfer learning and fine-tuning Transfer learning with TensorFlow Hub

This layer rescales every value of an input (often an image) by multiplying by scale and adding offset.

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. Inputs can be of integer or floating point dtype, and by default the layer will output floats.

Args
scale Float, the scale to apply to the inputs.
offset Float, the offset to apply to the inputs.
**kwargs Base layer keyword arguments, such as name and dtype.
Attributes
input Retrieves the input tensor(s) of a symbolic operation.Only returns the tensor(s) corresponding to the _first time_the operation was called.
output Retrieves the output tensor(s) of a layer.Only returns the tensor(s) corresponding to the _first time_the operation was called.

Methods

from_config

View source

@classmethod from_config( config )

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.
Returns
A layer instance.

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)