tf.keras.layers.experimental.preprocessing.RandomCrop

View source on GitHub

Randomly crop the images to target height and width.

Inherits From: Layer

tf.keras.layers.experimental.preprocessing.RandomCrop(
    height, width, seed=None, name=None, **kwargs
)

This layer will crop all the images in the same batch to the same cropping location. By default, random cropping is only applied during training. At inference time, the images will be first rescaled to preserve the shorter side, and center cropped. If you need to apply random cropping at inference time, set training to True when calling the layer.

Input shape:

4D tensor with shape: (samples, height, width, channels), data_format='channels_last'.

Output shape:

4D tensor with shape: (samples, target_height, target_width, channels).

height Integer, the height of the output shape.
width Integer, the width of the output shape.
seed Integer. Used to create a random seed.
name A string, the name of the layer.