tf.keras.layers.RandomContrast

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A preprocessing layer which randomly adjusts contrast during training.

Inherits From: Layer, Operation

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

This layer will randomly adjust the contrast of an image or images by a random factor. 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 pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and in integer or floating point dtype. By default, the layer will output floats.

Input shape
3D	unbatched) or 4D (batched) tensor with shape (..., height, width, channels), in "channels_last" format.

Output shape
3D	unbatched) or 4D (batched) tensor with shape (..., height, width, channels), in "channels_last" format.

Args
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]. For any pixel x in the channel, the output will be (x - mean) * factor + mean where mean is the mean value of the channel.
seed	Integer. Used to create a random seed.

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

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@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

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symbolic_call(
    *args, **kwargs
)