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Randomly crop the images to target height and width.

Inherits From: Layer

    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.