tf.keras.layers.Cropping2D

Cropping layer for 2D input (e.g. picture).

Inherits From: Layer, Operation

tf.keras.layers.Cropping2D(
    cropping=((0, 0), (0, 0)), data_format=None, **kwargs
)

It crops along spatial dimensions, i.e. height and width.

Example:

input_shape = (2, 28, 28, 3)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
y = keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
y.shape
(2, 24, 20, 3)

Args
cropping	Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. If int: the same symmetric cropping is applied to height and width. If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: (symmetric_height_crop, symmetric_width_crop). If tuple of 2 tuples of 2 ints: interpreted as ((top_crop, bottom_crop), (left_crop, right_crop)).
data_format	A string, one of "channels_last" (default) or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch_size, height, width, channels) while "channels_first" corresponds to inputs with shape (batch_size, channels, height, width). When unspecified, uses image_data_format value found in your Keras config file at ~/.keras/keras.json (if exists). Defaults to "channels_last".

Input shape
4D tensor with shape: If data_format is "channels_last": (batch_size, height, width, channels) If data_format is "channels_first": (batch_size, channels, height, width)

Output shape
4D tensor with shape: If data_format is "channels_last": (batch_size, cropped_height, cropped_width, channels) If data_format is "channels_first": (batch_size, channels, cropped_height, cropped_width)

Attributes
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

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

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

Returns
An operation instance.