tf.keras.layers.experimental.preprocessing.RandomContrast

Adjust the contrast of an image or images by a random factor.

Inherits From: PreprocessingLayer, Layer, Module

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Compat aliases for migration

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tf.compat.v1.keras.layers.experimental.preprocessing.RandomContrast

tf.keras.layers.experimental.preprocessing.RandomContrast(
    factor, seed=None, name=None, **kwargs
)

Contrast is adjusted independently for each channel of each image during training.

For each channel, this layer computes the mean of the image pixels in the channel and then adjusts each component x of each pixel to (x - mean) * contrast_factor + mean.

Input shape:

4D tensor with shape: (samples, height, width, channels), data_format='channels_last'.

Output shape:

4D tensor with shape: (samples, height, width, channels), data_format='channels_last'.

Raise
ValueError	if lower bound is not between [0, 1], or upper bound is negative.

Attributes
factor	a positive float represented as fraction of value, or a tuple of size 2 representing lower and upper bound. When represented as a single float, lower = upper. The contrast factor will be randomly picked between [1.0 - lower, 1.0 + upper].
seed	Integer. Used to create a random seed.
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.