layer_random_brightness: A preprocessing layer which randomly adjusts brightness...

In keras: R Interface to 'Keras'

A preprocessing layer which randomly adjusts brightness during training

Description

A preprocessing layer which randomly adjusts brightness during training

Usage

layer_random_brightness(
  object,
  factor,
  value_range = c(0, 255),
  seed = NULL,
  ...
)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
factor	Float or a list 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 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.
...	standard layer arguments.

Details

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.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomBrightness

• https://keras.io/api/layers

Other image augmentation layers: layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_rotation(), layer_random_translation(), layer_random_width(), layer_random_zoom()

Other preprocessing layers: layer_category_encoding(), layer_center_crop(), layer_discretization(), layer_hashing(), layer_integer_lookup(), layer_normalization(), layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_rotation(), layer_random_translation(), layer_random_width(), layer_random_zoom(), layer_rescaling(), layer_resizing(), layer_string_lookup(), layer_text_vectorization()

keras documentation built on May 29, 2024, 3:20 a.m.