Function conv2d_transpose_v2

tensorflow/python/ops/nn_ops.py:2250–2317

The transpose of `conv2d`. This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of `conv2d` rather than an actual deconvolution. Args: input: A 4-D `Tenso

(
    input,  # pylint: disable=redefined-builtin
    filters,  # pylint: disable=redefined-builtin
    output_shape,
    strides,
    padding="SAME",
    data_format="NHWC",
    dilations=None,
    name=None)

@tf_export("nn.conv2d_transpose", v1=[])
def conv2d_transpose_v2(
    input,  # pylint: disable=redefined-builtin
    filters,  # pylint: disable=redefined-builtin
    output_shape,
    strides,
    padding="SAME",
    data_format="NHWC",
    dilations=None,
    name=None):
  """The transpose of `conv2d`.

  This operation is sometimes called "deconvolution" after [Deconvolutional
  Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is
  actually the transpose (gradient) of `conv2d` rather than an actual
  deconvolution.

  Args:
    input: A 4-D `Tensor` of type `float` and shape `[batch, height, width,
      in_channels]` for `NHWC` data format or `[batch, in_channels, height,
      width]` for `NCHW` data format.
    filters: A 4-D `Tensor` with the same type as `input` and shape `[height,
      width, output_channels, in_channels]`.  `filter`'s `in_channels` dimension
      must match that of `input`.
    output_shape: A 1-D `Tensor` representing the output shape of the
      deconvolution op.
    strides: An int or list of `ints` that has length `1`, `2` or `4`.  The
      stride of the sliding window for each dimension of `input`. If a single
      value is given it is replicated in the `H` and `W` dimension. By default
      the `N` and `C` dimensions are set to 0. The dimension order is determined
      by the value of `data_format`, see below for details.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
      the "returns" section of `tf.nn.convolution` for details.
    data_format: A string. 'NHWC' and 'NCHW' are supported.
    dilations: An int or list of `ints` that has length `1`, `2` or `4`,
      defaults to 1. The dilation factor for each dimension of`input`. If a
      single value is given it is replicated in the `H` and `W` dimension. By
      default the `N` and `C` dimensions are set to 1. If set to k > 1, there
      will be k-1 skipped cells between each filter element on that dimension.
      The dimension order is determined by the value of `data_format`, see above
      for details. Dilations in the batch and depth dimensions if a 4-d tensor
      must be 1.
    name: Optional name for the returned tensor.

  Returns:
    A `Tensor` with the same type as `input`.

  Raises:
    ValueError: If input/output depth does not match `filter`'s shape, or if
      padding is other than `'VALID'` or `'SAME'`.
  """
  with ops.name_scope(name, "conv2d_transpose",
                      [input, filter, output_shape]) as name:
    if data_format is None:
      data_format = "NHWC"
    channel_index = 1 if data_format.startswith("NC") else 3

    strides = _get_sequence(strides, 2, channel_index, "strides")
    dilations = _get_sequence(dilations, 2, channel_index, "dilations")