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# tf.nn.depthwise_conv2d

```tf.nn.depthwise_conv2d(
input,
filter,
strides,
rate=None,
name=None,
data_format=None
)
```

Defined in `tensorflow/python/ops/nn_impl.py`.

See the guide: Neural Network > Convolution

Depthwise 2-D convolution.

Given a 4D input tensor ('NHWC' or 'NCHW' data formats) and a filter tensor of shape `[filter_height, filter_width, in_channels, channel_multiplier]` containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies a different filter to each input channel (expanding from 1 channel to `channel_multiplier` channels for each), then concatenates the results together. The output has `in_channels * channel_multiplier` channels.

In detail,

```output[b, i, j, k * channel_multiplier + q] = sum_{di, dj}
filter[di, dj, k, q] * input[b, strides[1] * i + rate[0] * di,
strides[2] * j + rate[1] * dj, k]
```

Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertical strides, `strides = [1, stride, stride, 1]`. If any value in `rate` is greater than 1, we perform atrous depthwise convolution, in which case all values in the `strides` tensor must be equal to 1.

#### Args:

• `input`: 4-D with shape according to `data_format`.
• `filter`: 4-D with shape `[filter_height, filter_width, in_channels, channel_multiplier]`.
• `strides`: 1-D of size 4. The stride of the sliding window for each dimension of `input`.
• `padding`: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the comment here
• `rate`: 1-D of size 2. The dilation rate in which we sample input values across the `height` and `width` dimensions in atrous convolution. If it is greater than 1, then all values of strides must be 1.
• `name`: A name for this operation (optional).
• `data_format`: The data format for input. Either "NHWC" (default) or "NCHW".

#### Returns:

A 4-D `Tensor` with shape according to `data_format`. E.g., for "NHWC" format, shape is `[batch, out_height, out_width, in_channels * channel_multiplier].`