Function conv1d_transpose

tensorflow/python/ops/nn_ops.py:1793–1871

The transpose of `conv1d`. This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of `conv1d` rather than an actual deconvolution. Args: input: A

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

@tf_export("nn.conv1d_transpose")
def conv1d_transpose(
    input,  # pylint: disable=redefined-builtin
    filters,
    output_shape,
    strides,
    padding="SAME",
    data_format="NWC",
    dilations=None,
    name=None):
  """The transpose of `conv1d`.

  This operation is sometimes called "deconvolution" after [Deconvolutional
  Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf),
  but is really the transpose (gradient) of `conv1d` rather than an actual
  deconvolution.

  Args:
    input: A 3-D `Tensor` of type `float` and shape
      `[batch, in_width, in_channels]` for `NWC` data format or
      `[batch, in_channels, in_width]` for `NCW` data format.
    filters: A 3-D `Tensor` with the same type as `value` and shape
      `[filter_width, output_channels, in_channels]`.  `filter`'s
      `in_channels` dimension must match that of `value`.
    output_shape: A 1-D `Tensor`, containing three elements, representing the
      output shape of the deconvolution op.
    strides: An int or list of `ints` that has length `1` or `3`.  The number of
      entries by which the filter is moved right at each step.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
      See the "returns" section of `tf.nn.convolution` for details.
    data_format: A string. `'NWC'` and `'NCW'` are supported.
    dilations: An int or list of `ints` that has length `1` or `3` which
      defaults to 1. The dilation factor for each dimension of input. If set to
      k > 1, there will be k-1 skipped cells between each filter element on that
      dimension. Dilations in the batch and depth dimensions must be 1.
    name: Optional name for the returned tensor.

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

  Raises:
    ValueError: If input/output depth does not match `filter`'s shape, if
      `output_shape` is not at 3-element vector, if `padding` is other than
      `'VALID'` or `'SAME'`, or if `data_format` is invalid.
  """
  with ops.name_scope(name, "conv1d_transpose",
                      [input, filters, output_shape]) as name:
    # The format could be either NWC or NCW, map to NHWC or NCHW
    if data_format is None or data_format == "NWC":
      data_format = "NHWC"
      spatial_start_dim = 1
      channel_index = 2
    elif data_format == "NCW":
      data_format = "NCHW"
      spatial_start_dim = 2
      channel_index = 1
    else:
      raise ValueError("data_format must be \"NWC\" or \"NCW\".")