Defined in tensorflow/python/ops/

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.


  • 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".


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].