tf.keras.layers.RandomPosterization

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

Note: This layer is safe to use inside a <a href="../../../tf/data"><code>tf.data</code></a> 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)

Args:
    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
)

convert_weight(
    weight
)

@classmethod
from_config(
    config
)

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

get_random_transformation(
    data, training=True, seed=None
)

transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images(
    images, transformation=None, training=True
)

transform_labels(
    labels, transformation, training=True
)

transform_segmentation_masks(
    segmentation_masks, transformation, training=True
)

transform_single_bounding_box(
    bounding_box, transformation, training=True
)

transform_single_image(
    image, transformation, training=True
)

transform_single_label(
    label, transformation, training=True
)

transform_single_segmentation_mask(
    segmentation_mask, transformation, training=True
)