/TensorFlow Python



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

See the guides: Layers (contrib) > Higher level ops for building neural network layers, Neural Network > Convolution

2-D convolution with separable filters.

Performs a depthwise convolution that acts separately on channels followed by a pointwise convolution that mixes channels. Note that this is separability between dimensions [1, 2] and 3, not spatial separability between dimensions 1 and 2.

In detail,

output[b, i, j, k] = sum_{di, dj, q, r}
    input[b, strides[1] * i + di, strides[2] * j + dj, q] *
    depthwise_filter[di, dj, q, r] *
    pointwise_filter[0, 0, q * channel_multiplier + r, k]

strides controls the strides for the depthwise convolution only, since the pointwise convolution has implicit strides of [1, 1, 1, 1]. 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 Tensor with shape according to data_format.
  • depthwise_filter: 4-D Tensor with shape [filter_height, filter_width, in_channels, channel_multiplier]. Contains in_channels convolutional filters of depth 1.
  • pointwise_filter: 4-D Tensor with shape [1, 1, channel_multiplier * in_channels, out_channels]. Pointwise filter to mix channels after depthwise_filter has convolved spatially.
  • strides: 1-D of size 4. The strides for the depthwise convolution 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'. For example, with data_format="NHWC", shape is [batch, out_height, out_width, out_channels].

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