tf.keras.layers.MixUp

See Stable See Nightly

MixUp implements the MixUp data augmentation technique.

Inherits From: Layer, Operation

tf.keras.layers.MixUp(
    alpha=0.2, data_format=None, seed=None, **kwargs
)

Note: This layer is safe to use inside a tf.data or grain pipeline (independently of which backend you're using).

References
MixUp paper. MixUp for Object Detection paper.

Args
alpha	Float between 0 and 1. Controls the blending strength. Smaller values mean less mixing, while larger values allow for more blending between images. Defaults to 0.2, recommended for ImageNet1k classification.
seed	Integer. Used to create a random seed.

Example:

(images, labels), _ = keras.datasets.cifar10.load_data()
images, labels = images[:8], labels[:8]
labels = keras.ops.cast(keras.ops.one_hot(labels.flatten(), 10), "float32")
mix_up = keras.layers.MixUp(alpha=0.2)
output = mix_up({"images": images, "labels": labels})

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

convert_weight

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convert_weight(
    weight
)

Convert the weight if it is from the a different backend.

from_config

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@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

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

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.

get_random_transformation

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

transform_bounding_boxes

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transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images

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

transform_labels

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transform_labels(
    labels, transformation, training=True
)

transform_segmentation_masks

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transform_segmentation_masks(
    segmentation_masks, transformation, training=True
)

transform_single_bounding_box

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transform_single_bounding_box(
    bounding_box, transformation, training=True
)

transform_single_image

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transform_single_image(
    image, transformation, training=True
)

transform_single_label

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transform_single_label(
    label, transformation, training=True
)

transform_single_segmentation_mask

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transform_single_segmentation_mask(
    segmentation_mask, transformation, training=True
)