Next we will apply another \(pooling \) layer with filter size \(f 2 \), and stride \(s 2\) so once again we reduce the size of an image by \(2 \) (as we did with the first \(pooling \) layer). Finally we have \(5\times5\times16\) volume and if we multiply these numbers \(5\times5\times16\) we get \(400\). We reduced dimensions of an image so now we can apply a \(Fully\enspace connected\) layer with \(120\) nodes. Then we apply another \(Fully\enspace connected\) layer with \(84 \) nodes. The final step is to use these \(84 \) features to get the final output, and at the output can take on \(10 \) possible values because we have to recognize \(10 \) different digits (\(0\) to\(9 \)), so at the end we have a \(softmax \) layer with a \(10 \)-way classification output (although back then \(LeNet-5 \) actually used a different classifier at the output layer, one that's useless today).