Deep Learning Quick Reference

Benefits of convolutional layers

So, now that you hopefully have an idea how convolutional layers work, let's talk about why we did all this crazy math. Why would we use a convolutional layer instead of the normal layers we have previously been using?

Let's say that we did use a normal layer, to get the same output shape we talked about previously. We started with a 32 x 32 x 3 image, so that's 3,072 values total. We were left with a 30 x 30 x 64 matrix. That's 57,600 values in total. If we were to use a fully connected layer to connect these two matrices, that layer would have 176,947,200 trainable parameters. That's 176 million.

However, when we use the convolutional layer above, we used 64 3 x 3 x 3 filters, which results in 1,728 learnable ...