tf.keras.layers.RandomCrop

A preprocessing layer which randomly crops images during training.

Inherits From: Layer, Operation

tf.keras.layers.RandomCrop(
    height, width, seed=None, data_format=None, name=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 integer or floating point dtype. By default, the layer will output floats.

3Dunbatched) or 4D (batched) tensor with shape

(..., height, width, channels), in "channels_last" format.

3Dunbatched) or 4D (batched) tensor with shape

(..., target_height, target_width, channels).

heightInteger, the height of the output shape.
widthInteger, the width of the output shape.
seedInteger. Used to create a random seed.
**kwargsBase layer keyword arguments, such as name and dtype.

inputRetrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

outputRetrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
configA Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)