tf.keras.layers.RandomContrast

A preprocessing layer which randomly adjusts contrast during training.

Inherits From: Layer, Module

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. The output value will be clipped to the range [0, 255], the valid range of RGB colors.

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

3D unbatched) or 4D (batched) tensor with shape

(..., height, width, channels), in "channels_last" format.

3D unbatched) or 4D (batched) tensor with shape

(..., height, width, channels), in "channels_last" format.

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.

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