tensorflow:: ops:: Conv2D

#include <nn_ops.h>

Computes a 2-D convolution given 4-D input and filter tensors.

Summary

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels] , this op performs the following:

  1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels] .
  2. Extracts image patches from the input tensor to form a virtual tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels] .
  3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format, 

output[b, i, j, k] =
    sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                    filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1 . For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1] .

Args:

  • scope: A Scope object
  • input: A 4-D tensor. The dimension order is interpreted according to the value of data_format , see below for details.
  • filter: A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
  • strides: 1-D tensor of length 4. The stride of the sliding window for each dimension of input . The dimension order is determined by the value of data_format , see below for details.
  • 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, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, 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 : A 4-D tensor. The dimension order is determined by the value of data_format , see below for details.

Constructors and Destructors

Conv2D (const :: tensorflow::Scope & scope, :: tensorflow::Input input, :: tensorflow::Input filter, const gtl::ArraySlice< int > & strides, StringPiece padding)
Conv2D (const :: tensorflow::Scope & scope, :: tensorflow::Input input, :: tensorflow::Input filter, const gtl::ArraySlice< int > & strides, StringPiece padding, const Conv2D::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:: Conv2D:: Attrs

Optional attribute setters for Conv2D .

Public attributes

operation 

Operation operation

output 

::tensorflow::Output output

Public functions

Conv2D 

 Conv2D(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input filter,
  const gtl::ArraySlice< int > & strides,
  StringPiece padding
)

Conv2D 

 Conv2D(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input filter,
  const gtl::ArraySlice< int > & strides,
  StringPiece padding,
  const Conv2D::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
)