Notes on MatConvNet(II):vl_simplenn

#Written before
This blog is the second one of the series of Notes on MatConvNet.

1. Notes on MatConvNet(I) – Overview
   Here I will mainly introduce the central of the matcovnet—vl_simplenn.
   This function plays a quite important role in forward-propagation and backward-propagation.
   PS. I have to admit that writing blogs in Chinese would bring much more traffic than writing English blogs. Yet, if I care too much about vain trifles, it is a sad thing.

Something that should be known before

I only introduce BP. How does the derivatives propagate backforward? I sincerely recommend you to read BP Algorithm. This blog is a good beginning of BP. When you have finished reading that, The first blog of this series is also recommended. I will repeat the main computation structure illustrated in Notes(I).

Computation Structure

I make some default rules here.

• y always represents the output of certain layer. That is to say when it comes to layer i, then y is the output of layer i.
• z always represents the output of the whole net, or rather, it represents the output of the final layer n.
• x represents the input of certain layer.

In order to make things more easier, matcovnet takes one simple function as a “layer”. This means when the input goes through a computation strcuture(no matter it is conv structure or just a relu structure), it does computation like the following:
$$\frac{dz}{dx}=f^{{}^{\prime }}(x)\ast d$$