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:
- Flattens the filter to a 2-D matrix with shape
[filter_height * filter_width * in_channels, output_channels]
. - 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]
. - 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 ofdata_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 isexplicit_paddings[2 * i]
andexplicit_paddings[2 * i + 1]
, respectively. Ifpadding
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 ofdata_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 ofdata_format
, see below for details.
Constructors and Destructors |
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Conv2D(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input filter, const gtl::ArraySlice< int > & strides, StringPiece padding)
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Conv2D(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input filter, const gtl::ArraySlice< int > & strides, StringPiece padding, const Conv2D::Attrs & attrs)
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Public attributes |
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operation
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output
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Public functions |
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node() const
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::tensorflow::Node *
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operator::tensorflow::Input() const
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operator::tensorflow::Output() const
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Public static functions |
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DataFormat(StringPiece x)
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Dilations(const gtl::ArraySlice< int > & x)
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ExplicitPaddings(const gtl::ArraySlice< int > & x)
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UseCudnnOnGpu(bool x)
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Structs |
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tensorflow:: |
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 )