Cropping3D layer
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, usesimage_data_formatvalue 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)