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### `tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)` {#conv2d}
Computes a 2-D convolution given 4-D `input` and `filter` tensors.
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:
* <b>`input`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
* <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
* <b>`strides`</b>: A list of `ints`.
1-D of length 4. The stride of the sliding window for each dimension
of `input`. Must be in the same order as the dimension specified with format.
* <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
The type of padding algorithm to use.
* <b>`use_cudnn_on_gpu`</b>: An optional `bool`. Defaults to `True`.
* <b>`data_format`</b>: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`.
Specify the data format of the input and output data. With the
default format "NHWC", the data is stored in the order of:
[batch, in_height, in_width, in_channels].
Alternatively, the format could be "NCHW", the data storage order of:
[batch, in_channels, in_height, in_width].
* <b>`name`</b>: A name for the operation (optional).
##### Returns:
A `Tensor`. Has the same type as `input`.
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