tf.keras.layers.RandomBrightness

A preprocessing layer which randomly adjusts brightness during training.

Inherits From: Layer, Module

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.

Note that different brightness adjustment factors will be apply to each the images in the batch.

For an overview and full list of preprocessing layers, see the preprocessing guide.

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. Defaults to [0.0, 255.0]. Can be changed to e.g. [0.0, 1.0] if the image input has been scaled before this layer. 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.

Sample usage:

random_bright = tf.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.
tf.Tensor([[[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)

auto_vectorize Control whether automatic vectorization occurs.

By default the call() method leverages the tf.vectorized_map() function. Auto-vectorization can be disabled by setting self.auto_vectorize = False in your __init__() method. When disabled, call() instead relies on tf.map_fn(). For example:

class SubclassLayer(BaseImageAugmentationLayer):
  def __init__(self):
    super().__init__()
    self.auto_vectorize = False