tensorflow::ops::Conv2DBackpropInput

#include <nn_ops.h>

Computes the gradients of convolution with respect to the input.

Summary

Args:

  • scope: A Scope object
  • input_sizes: An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
  • filter: 4-D with shape [filter_height, filter_width, in_channels, out_channels].
  • out_backprop: 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
  • strides: The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
  • padding: The type of padding algorithm to use.

Optional attributes (see Attrs):

  • explicit_paddings: If padding is "EXPLICIT", the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is explicit_paddings[2 * i] and explicit_paddings[2 * i + 1], respectively. If padding is not "EXPLICIT", explicit_paddings must be empty.
  • data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  • dilations: 1-D tensor of length 4. 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. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.

Returns:

  • Output: 4-D with shape [batch, in_height, in_width, in_channels]. Gradient w.r.t. the input of the convolution.

Constructors and Destructors
Conv2DBackpropInput(const ::tensorflow::Scope & scope, ::tensorflow::Input input_sizes, ::tensorflow::Input filter, ::tensorflow::Input out_backprop, const gtl::ArraySlice< int > & strides, StringPiece padding)
Conv2DBackpropInput(const ::tensorflow::Scope & scope, ::tensorflow::Input input_sizes, ::tensorflow::Input filter, ::tensorflow::Input out_backprop, const gtl::ArraySlice< int > & strides, StringPiece padding, const Conv2DBackpropInput::Attrs & attrs)

Public attributes
operation
output

Public functions
node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

Public static functions
DataFormat(StringPiece x)
Dilations(const gtl::ArraySlice< int > & x)
ExplicitPaddings(const gtl::ArraySlice< int > & x)
UseCudnnOnGpu(bool x)

Structs
tensorflow::ops::Conv2DBackpropInput::Attrs

Optional attribute setters for Conv2DBackpropInput.

Public attributes

operation

Operation operation

output

::tensorflow::Output output

Public functions

Conv2DBackpropInput

 Conv2DBackpropInput(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input_sizes,
  ::tensorflow::Input filter,
  ::tensorflow::Input out_backprop,
  const gtl::ArraySlice< int > & strides,
  StringPiece padding
)

Conv2DBackpropInput

 Conv2DBackpropInput(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input_sizes,
  ::tensorflow::Input filter,
  ::tensorflow::Input out_backprop,
  const gtl::ArraySlice< int > & strides,
  StringPiece padding,
  const Conv2DBackpropInput::Attrs & attrs
)

node

::tensorflow::Node * node() const

operator::tensorflow::Input

 operator::tensorflow::Input() const

operator::tensorflow::Output

 operator::tensorflow::Output() const

Public static functions

DataFormat

Attrs DataFormat(
  StringPiece x
)

Dilations

Attrs Dilations(
  const gtl::ArraySlice< int > & x
)

ExplicitPaddings

Attrs ExplicitPaddings(
  const gtl::ArraySlice< int > & x
)

UseCudnnOnGpu

Attrs UseCudnnOnGpu(
  bool x
)