Cropping3D
classkeras.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
(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)
.((left_dim1_crop, right_dim1_crop), (left_dim2_crop,
right_dim2_crop), (left_dim3_crop, right_dim3_crop))
."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)