tf.keras.layers.RandomCrop

A preprocessing layer which randomly crops images during training.

Inherits From: Layer, Module

View aliases

Main aliases

tf.keras.layers.experimental.preprocessing.RandomCrop

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

During training, this layer will randomly choose a location to crop images down to a target size. The layer will crop all the images in the same batch to the same cropping location.

At inference time, and during training if an input image is smaller than the target size, the input will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. If you need to apply random cropping at inference time, set training to True when calling the layer.

Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.

For an overview and full list of preprocessing layers, see the preprocessing guide.

Input shape
3D	unbatched) or 4D (batched) tensor with shape (..., height, width, channels), in "channels_last" format.

Output shape
3D	unbatched) or 4D (batched) tensor with shape (..., target_height, target_width, channels).

Args
height	Integer, the height of the output shape.
width	Integer, the width of the output shape.
seed	Integer. Used to create a random seed.