MixUp layer
MixUp class
keras_cv.layers.MixUp(alpha=0.2, seed=None, **kwargs)
MixUp implements the MixUp data augmentation technique.
Arguments
- alpha: Float between 0 and 1. Inverse scale parameter for the gamma distribution. This controls the shape of the distribution from which the smoothing values are sampled. Defaults to 0.2, which is a recommended value when training an imagenet1k classification model.
- seed: Integer. Used to create a random seed.
References
Sample usage:
(images, labels), _ = keras.datasets.cifar10.load_data()
images, labels = images[:10], labels[:10]
# Labels must be floating-point and one-hot encoded
labels = tf.cast(tf.one_hot(labels, 10), tf.float32)
mixup = keras_cv.layers.preprocessing.MixUp(10)
augmented_images, updated_labels = mixup(
{'images': images, 'labels': labels}
)
# output == {'images': updated_images, 'labels': updated_labels}