tf.keras.layers.CenterCrop

A preprocessing layer which crops images.

Inherits From: Layer, Operation

This layers crops the central portion of the images to a target size. If an image is smaller than the target size, it will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio.

Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]).

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

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

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

(..., target_height, target_width, channels), or (..., channels, target_height, target_width), in "channels_first" format.

If the input height/width is even and the target height/width is odd (or inversely), the input image is left-padded by 1 pixel.

height Integer, the height of the output shape.
width Integer, the width of the output shape.
data_format string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".

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

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

output Retrieves 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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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
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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