to process inputs we have a mixer to encode inputs with previous outputs and we have a decoder to generate outputs a fundamental component of each of these parts is depth wise separable convolution it works effectively in such large networks next up we have exception a convolution neural network architecture based entirely on depth wise separable convolution layers it has shown the state-of-the-art performance on large datasets like Google's jft image data set it's a repository of 350 million images with 17,000 class labels