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► Keras 3 API documentation / Layers API / Reshaping layers / Cropping2D layer

Cropping2D layer

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Cropping2D class

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

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

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)

Arguments

  • 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)

Cropping2D layer
Cropping2D class