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► Keras 3 API documentation / Layers API / Preprocessing layers / Image augmentation layers / RandomPosterization layer

RandomPosterization layer

[source]

RandomPosterization class

keras.layers.RandomPosterization(
    factor, value_range=(0, 255), data_format=None, seed=None, **kwargs
)

Reduces the number of bits for each color channel.

**Note:** This layer is safe to use inside a [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) or `grain` pipeline
(independently of which backend you're using).

References:
- [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://arxiv.org/abs/1909.13719)

# Arguments
    value_range: a tuple or a list of two elements. The first value
        represents the lower bound for values in passed images, the second
        represents the upper bound. Images passed to the layer should have
        values within `value_range`. Defaults to `(0, 255)`.
    factor: integer, the number of bits to keep for each channel. Must be a
        value between 1-8.

# Example

layer = keras.layers.RandomPosterization(value_range=(0, 255))
images = np.random.randint(0, 255, (8, 224, 224, 3), dtype="uint8")

labels = keras.ops.one_hot(
    np.array([0, 1, 2, 0, 1, 2, 0, 1]),
    num_classes=3
)

segmentation_masks = np.random.randint(0, 3, (8, 224, 224, 1), dtype="uint8")

output = layer(
    {
        "images": images,
        "labels": labels,
        "segmentation_masks": segmentation_masks
    },
    training=True
)
RandomPosterization layer
RandomPosterization class