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].

Arguments:

• 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):

• 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.

Public attributes

::tensorflow::Output output

Public functions

 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
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const 

Public static functions

Attrs DataFormat(
  StringPiece x
)

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

Attrs UseCudnnOnGpu(
  bool x
)