Function atrous_conv2d_transpose

tensorflow/python/ops/nn_ops.py:2321–2469

The transpose of `atrous_conv2d`. This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of `atrous_conv2d` rather than an actual deconvolution. Args

(value,
                            filters,
                            output_shape,
                            rate,
                            padding,
                            name=None)

@tf_export("nn.atrous_conv2d_transpose")
def atrous_conv2d_transpose(value,
                            filters,
                            output_shape,
                            rate,
                            padding,
                            name=None):
  """The transpose of `atrous_conv2d`.

  This operation is sometimes called "deconvolution" after [Deconvolutional
  Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf),
  but is really the transpose (gradient) of `atrous_conv2d` rather than an
  actual deconvolution.

  Args:
    value: A 4-D `Tensor` of type `float`. It needs to be in the default `NHWC`
      format. Its shape is `[batch, in_height, in_width, in_channels]`.
    filters: A 4-D `Tensor` with the same type as `value` and shape
      `[filter_height, filter_width, out_channels, in_channels]`. `filters`'
      `in_channels` dimension must match that of `value`. Atrous convolution is
      equivalent to standard convolution with upsampled filters with effective
      height `filter_height + (filter_height - 1) * (rate - 1)` and effective
      width `filter_width + (filter_width - 1) * (rate - 1)`, produced by
      inserting `rate - 1` zeros along consecutive elements across the
      `filters`' spatial dimensions.
    output_shape: A 1-D `Tensor` of shape representing the output shape of the
      deconvolution op.
    rate: A positive int32. The stride with which we sample input values across
      the `height` and `width` dimensions. Equivalently, the rate by which we
      upsample the filter values by inserting zeros across the `height` and
      `width` dimensions. In the literature, the same parameter is sometimes
      called `input stride` or `dilation`.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
    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 `filters`' shape, or if
      padding is other than `'VALID'` or `'SAME'`, or if the `rate` is less
      than one, or if the output_shape is not a tensor with 4 elements.
  """
  with ops.name_scope(name, "atrous_conv2d_transpose",
                      [value, filters, output_shape]) as name:
    value = ops.convert_to_tensor(value, name="value")
    filters = ops.convert_to_tensor(filters, name="filters")
    if not value.get_shape().dims[3].is_compatible_with(filters.get_shape()[3]):
      raise ValueError(
          "value's input channels does not match filters' input channels, "
          "{} != {}".format(value.get_shape()[3],
                            filters.get_shape()[3]))
    if rate < 1:
      raise ValueError("rate {} cannot be less than one".format(rate))

    if rate == 1:
      return conv2d_transpose(
          value,
          filters,