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# 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 * i + di, strides * j + dj, q] *
filter[di, dj, q, k]
```

Must have `strides = strides = 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.

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`
`Operation`
`output`
`::tensorflow::Output`

### Public functions

`node() const `
`::tensorflow::Node *`
`operator::tensorflow::Input() const `
``` ```
``` ```
`operator::tensorflow::Output() const `
``` ```
``` ```

### Public static functions

`DataFormat(StringPiece x)`
`Attrs`
`Dilations(const gtl::ArraySlice< int > & x)`
`Attrs`
`ExplicitPaddings(const gtl::ArraySlice< int > & x)`
`Attrs`
`UseCudnnOnGpu(bool x)`
`Attrs`

### 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,
)```

### Conv2D

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

```Attrs ExplicitPaddings(
```Attrs UseCudnnOnGpu(