Function spatial_3d_padding

tensorflow/python/keras/backend.py:3032–3072

Pads 5D tensor with zeros along the depth, height, width dimensions. Pads these dimensions with respectively "padding[0]", "padding[1]" and "padding[2]" zeros left and right. For 'channels_last' data_format, the 2nd, 3rd and 4th dimension will be padded. For 'channels_first' data_format,

(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None)

@keras_export('keras.backend.spatial_3d_padding')
def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None):
  """Pads 5D tensor with zeros along the depth, height, width dimensions.

  Pads these dimensions with respectively
  "padding[0]", "padding[1]" and "padding[2]" zeros left and right.

  For 'channels_last' data_format,
  the 2nd, 3rd and 4th dimension will be padded.
  For 'channels_first' data_format,
  the 3rd, 4th and 5th dimension will be padded.

  Arguments:
      x: Tensor or variable.
      padding: Tuple of 3 tuples, padding pattern.
      data_format: One of `channels_last` or `channels_first`.

  Returns:
      A padded 5D tensor.

  Raises:
      ValueError: if `data_format` is neither
          `channels_last` or `channels_first`.

  """
  assert len(padding) == 3
  assert len(padding[0]) == 2
  assert len(padding[1]) == 2
  assert len(padding[2]) == 2
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format: ' + str(data_format))

  if data_format == 'channels_first':
    pattern = [[0, 0], [0, 0], [padding[0][0], padding[0][1]],
               [padding[1][0], padding[1][1]], [padding[2][0], padding[2][1]]]
  else:
    pattern = [[0, 0], [padding[0][0], padding[0][1]],
               [padding[1][0], padding[1][1]], [padding[2][0],
                                                padding[2][1]], [0, 0]]
  return array_ops.pad(x, pattern)


@keras_export('keras.backend.stack')