# 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 ```
``` 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,
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
)```
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[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]