tf.keras.layers.RandomInvert

tf.keras.layers.RandomInvert(
    factor=1.0, value_range=(0, 255), seed=None, data_format=None, **kwargs
)

This layer randomly inverts the colors of input images with a specified
probability range. When applied, each image has a chance of having its
colors inverted, where the pixel values are transformed to their
complementary values. Images that are not selected for inversion
remain unchanged.

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).

Args:
    factor: A single float or a tuple of two floats.
        `factor` controls the probability of inverting the image colors.
        If a tuple is provided, the value is sampled between the two values
        for each image, where `factor[0]` is the minimum and `factor[1]` is
        the maximum probability. If a single float is provided, a value
        between `0.0` and the provided float is sampled.
        Defaults to `(0, 1)`.
    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)`.
    seed: Integer. Used to create a random seed.

Example:

layer = keras.layers.RandomInvert(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, 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
)