Cropping3D layer

[source]

Cropping3D class

keras.layers.Cropping3D(
    cropping=((1, 1), (1, 1), (1, 1)), data_format=None, **kwargs
)

Cropping layer for 3D data (e.g. spatial or spatio-temporal).

Example

>>> input_shape = (2, 28, 28, 10, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> y = keras.layers.Cropping3D(cropping=(2, 4, 2))(x)
>>> y.shape
(2, 24, 20, 6, 3)

Arguments

• cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
  • If int: the same symmetric cropping is applied to depth, height, and width.
  • If tuple of 3 ints: interpreted as three different symmetric cropping values for depth, height, and width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop).
  • If tuple of 3 tuples of 2 ints: interpreted as ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_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, spatial_dim1, spatial_dim2, spatial_dim3, channels) while "channels_first" corresponds to inputs with shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). 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

5D tensor with shape: - If data_format is "channels_last": (batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, channels) - If data_format is "channels_first": (batch_size, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)

Output shape

5D tensor with shape: - If data_format is "channels_last": (batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, channels) - If data_format is "channels_first": (batch_size, channels, first_cropped_axis, second_cropped_axis, third_cropped_axis)