Hands-On Convolutional Neural Networks with TensorFlow

Depthwise separable convolution

This new convolution block (tf.layers.separable_conv2d) consists of two main parts: a depthwise convolution layer, followed by a 1x1 pointwise convolution layer. This block differs from the normal convolution in a couple of ways:

• In the normal convolution layer, each filter F will be applied to all channels on the input channel at the same time (F is applied to each channel and then summed)
• This new convolution F is applied on each channel separately, and the results get concatenated to some intermediate tensor (how much is controlled by the Depth Multiplier, DM parameter)

The depthwise convolution is extremely efficient relative to standard convolution. However, it only filters input channels, and it does ...