Function conv2d_transpose

tensorflow/python/keras/backend.py:4765–4834

2D deconvolution (i.e. transposed convolution). Arguments: x: Tensor or variable. 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"`

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

@keras_export('keras.backend.conv2d_transpose')
def conv2d_transpose(x,
                     kernel,
                     output_shape,
                     strides=(1, 1),
                     padding='valid',
                     data_format=None,
                     dilation_rate=(1, 1)):
  """2D deconvolution (i.e.

  transposed convolution).

  Arguments:
      x: Tensor or variable.
      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"`.
      dilation_rate: Tuple of 2 integers.

  Returns:
      A tensor, result of transposed 2D 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))

  # `atrous_conv2d_transpose` only supports NHWC format, even on GPU.
  if data_format == 'channels_first' and dilation_rate != (1, 1):
    force_transpose = True
  else:
    force_transpose = False

  x, tf_data_format = _preprocess_conv2d_input(x, data_format, force_transpose)

  if data_format == 'channels_first' and tf_data_format == 'NHWC':
    output_shape = (output_shape[0], output_shape[2], output_shape[3],
                    output_shape[1])
  if output_shape[0] is None:
    output_shape = (shape(x)[0],) + tuple(output_shape[1:])

  if isinstance(output_shape, (tuple, list)):
    output_shape = array_ops.stack(list(output_shape))

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

  if dilation_rate == (1, 1):
    x = nn.conv2d_transpose(x, kernel, output_shape, strides,
                            padding=padding,