Function conv3d_transpose

tensorflow/python/keras/backend.py:5052–5108

3D deconvolution (i.e. transposed convolution). Arguments: x: input tensor. kernel: kernel tensor. output_shape: 1D int tensor for the output shape. strides: strides tuple. padding: string, "same" or "valid". data_format: string, `"channels_last"` or `"chann

(x,
                     kernel,
                     output_shape,
                     strides=(1, 1, 1),
                     padding='valid',
                     data_format=None)

def conv3d_transpose(x,
                     kernel,
                     output_shape,
                     strides=(1, 1, 1),
                     padding='valid',
                     data_format=None):
  """3D deconvolution (i.e.

  transposed convolution).

  Arguments:
      x: input tensor.
      kernel: kernel tensor.
      output_shape: 1D int tensor for the output shape.
      strides: strides tuple.
      padding: string, "same" or "valid".
      data_format: string, `"channels_last"` or `"channels_first"`.

  Returns:
      A tensor, result of transposed 3D convolution.

  Raises:
      ValueError: if `data_format` is neither `channels_last` or
      `channels_first`.
  """
  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 isinstance(output_shape, (tuple, list)):
    output_shape = array_ops.stack(output_shape)

  x, tf_data_format = _preprocess_conv3d_input(x, data_format)

  if data_format == 'channels_first' and tf_data_format == 'NDHWC':
    output_shape = (output_shape[0], output_shape[2], output_shape[3],
                    output_shape[4], output_shape[1])
  if output_shape[0] is None:
    output_shape = (array_ops.shape(x)[0],) + tuple(output_shape[1:])
    output_shape = array_ops.stack(list(output_shape))

  padding = _preprocess_padding(padding)
  if tf_data_format == 'NDHWC':
    strides = (1,) + strides + (1,)
  else:
    strides = (1, 1) + strides

  x = nn.conv3d_transpose(
      x,
      kernel,
      output_shape,
      strides,
      padding=padding,
      data_format=tf_data_format)
  if data_format == 'channels_first' and tf_data_format == 'NDHWC':
    x = array_ops.transpose(x, (0, 4, 1, 2, 3))
  return x