tf.keras.layers.Cropping3D

TensorFlow 1 version View source on GitHub

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

Inherits From: Layer

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

Examples:

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

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 two 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). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "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, depth)
  • If data_format is "channels_first": (batch_size, depth, 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, depth)
  • If data_format is "channels_first": (batch_size, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)