tf.keras.layers.RandomBrightness( factor, value_range=(0, 255), seed=None, **kwargs )
A preprocessing layer which randomly adjusts brightness during training.
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.
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
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.
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)