tf.keras.layers.RandomBrightness

A preprocessing layer which randomly adjusts brightness during training.

Inherits From: Layer, Operation

tf.keras.layers.RandomBrightness(
    factor, value_range=(0, 255), seed=None, **kwargs
)

This layer will randomly increase/reduce the brightness for the input RGB images. At inference time, the output will be identical to the input. Call the layer with training=True to adjust the brightness of the input.

Args
`factor`	Float or a list/tuple of 2 floats between -1.0 and 1.0. The factor is used to determine the lower bound and upper bound of the brightness adjustment. A float value will be chosen randomly between the limits. When -1.0 is chosen, the output image will be black, and when 1.0 is chosen, the image will be fully white. When only one float is provided, eg, 0.2, then -0.2 will be used for lower bound and 0.2 will be used for upper bound.
`value_range`	Optional list/tuple of 2 floats for the lower and upper limit of the values of the input data. To make no change, use `[0.0, 1.0]`, e.g., if the image input has been scaled before this layer. Defaults to `[0.0, 255.0]`. The brightness adjustment will be scaled to this range, and the output values will be clipped to this range.
`seed`	optional integer, for fixed RNG behavior.

Inputs: 3D (HWC) or 4D (NHWC) tensor, with float or int dtype. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255])

Output: 3D (HWC) or 4D (NHWC) tensor with brightness adjusted based on the factor. By default, the layer will output floats. The output value will be clipped to the range [0, 255], the valid range of RGB colors, and rescaled based on the value_range if needed.

Example:

random_bright = keras.layers.RandomBrightness(factor=0.2)

# An image with shape [2, 2, 3]
image = [[[1, 2, 3], [4 ,5 ,6]], [[7, 8, 9], [10, 11, 12]]]

# Assume we randomly select the factor to be 0.1, then it will apply
# 0.1 * 255 to all the channel
output = random_bright(image, training=True)

# output will be int64 with 25.5 added to each channel and round down.

array([[[26.5, 27.5, 28.5] [29.5, 30.5, 31.5]] [[32.5, 33.5, 34.5] [35.5, 36.5, 37.5]]], shape=(2, 2, 3), dtype=int64)

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.

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`