tf.keras.layers.Rescaling

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

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.

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.

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

convert_weight

View source

convert_weight(
    weight
)

Convert the weight if it is from the a different backend.

from_config

View source

@classmethod
from_config(
    config, custom_objects=None
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

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

Returns
An operation instance.