Function conv_transpose

tensorflow/python/ops/nn_ops.py:2650–2724

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

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

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

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

  Args:
    input: An N+2 dimensional `Tensor` of shape
      `[batch_size] + input_spatial_shape + [in_channels]` if data_format does
      not start with "NC" (default), or
      `[batch_size, in_channels] + input_spatial_shape` if data_format starts
      with "NC". It must be one of the following types:
      `half`, `bfloat16`, `float32`, `float64`.
    filters: An N+2 dimensional `Tensor` with the same type as `input` and
      shape `spatial_filter_shape + [in_channels, out_channels]`.
    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`, `N` or `N+2`.  The
      stride of the sliding window for each dimension of `input`. If a single
      value is given it is replicated in the spatial dimensions. 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 or None.  Specifies whether the channel dimension of
      the `input` and output is the last dimension (default, or if `data_format`
      does not start with "NC"), or the second dimension (if `data_format`
      starts with "NC").  For N=1, the valid values are "NWC" (default) and
      "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW".
      For N=3, the valid values are "NDHWC" (default) and "NCDHW".
    dilations: An int or list of `ints` that has length `1`, `N` or `N+2`,
      defaults to 1. The dilation factor for each dimension of`input`. If a
      single value is given it is replicated in the spatial dimensions. 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.
    name: A name for the operation (optional). If not specified "conv_transpose"
      is used.

  Returns:
    A `Tensor` with the same type as `value`.
  """
  with ops.name_scope(name, "conv_transpose",
                      [input, filter, output_shape]) as name:
    if tensor_util.is_tensor(output_shape):
      n = output_shape.shape[0] - 2
    elif isinstance(output_shape, collections.Sized):
      n = len(output_shape) - 2