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Showing content from https://keras.io/api/layers/preprocessing_layers/image_preprocessing/rescaling below:

Rescaling layer

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

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

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).

Arguments

• scale: Float, int, list, tuple or np.ndarray. The scale to apply to the inputs. If scalar, the same scale will be applied to all features or channels of input. If a list, tuple or 1D array, the scaling is applied per channel.
• offset: Float, int, list/tuple or numpy ndarray. The offset to apply to the inputs. If scalar, the same scale will be applied to all features or channels of input. If a list, tuple or 1D array, the scaling is applied per channel.
• **kwargs: Base layer keyword arguments, such as name and dtype.

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