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
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.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

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
)