tf.keras.layers.experimental.preprocessing.RandomCrop

Randomly crop the images to target height and width.

Inherits From: PreprocessingLayer, Layer, Module

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Compat aliases for migration

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tf.compat.v1.keras.layers.experimental.preprocessing.RandomCrop

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).

Arguments
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.

Methods

adapt

View source

adapt(
    data, reset_state=True
)

Fits the state of the preprocessing layer to the data being passed.

Arguments
data	The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.
reset_state	Optional argument specifying whether to clear the state of the layer at the start of the call to adapt, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False.