The input tensor, of rank 4 or rank 3, of shape
[batch, height, width, inChannels]. If rank 3, batch of 1 is
assumed.
The depthwise filter tensor, rank 4, of shape
[filterHeight, filterWidth, inChannels, channelMultiplier]. This is
the filter used in the first step.
The pointwise filter tensor, rank 4, of shape
[1, 1, inChannels * channelMultiplier, outChannels]. This is
the filter used in the second step.
The strides of the convolution: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.
The type of padding algorithm.
same and stride 1: output will be of same size as input,
regardless of filter size.valid: output will be smaller than input if filter is larger
than 1x1.Optional dilation: number | [number, number]Optional dataFormat: "NHWC" | "NCHW"
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.
See https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d for more details.