Hi and welcome to the chapter on fully connected layers.
We'll explore what it is and what the purpose of a fully connected layer is in the CNN.
So firstly, what is fully connected mean?
Well, simply means all the nodes in one layer are connected to all the outputs in the next layer.
I will describe that to you in the diagram shortly.
However, just so you know it takes two to tree volume output from the previously and flattens it into
a single vector and that's used for the inputs into the next layer.
It's also sometimes called a dense layer, so this text is confusing.
Let's take a look at the diagram and get a better understanding of what the fully connected layer does.
So you can see these are feature maps here.
Remember, we got features.
We get feature maps from after applying the convolution filter to the image, and then we can apply
some school to it and regional as well.
So these are the outputs here.
So what fully connected layer the EFSI layer does?
It simply flattens all of these filters of all of these outputs into a single vector like this, so
you can see one two three one six seven one eight seven hits in a row here and column here.
And then the next filters added.
And then the last filter is added again to the bottom.
So you have this one dimensional vector with all the values from and from the feature maps before,
and every note here is connected to the output nodes.
These output nodes will deal with in the next section, but these output nodes are very important.
And basically, we have all of these up these and this nodes here connected to each output.
That's why there's so many lines here.
So what's the purpose of this fully connected layer?
Well, it compiles the data outputs extracted from the feature maps and previous layers to form the
final output.
But that doesn't tell you much.
So effectively, imagine this it's an easy way of putting neural net to learn the nonlinear combinations
of these features.
Because remember, we have all of these features.
This one could be a whisker.
This one could be a teele, this one from the AI, and we have all of these values of indicates something
we need to know.
Find a way for the neural network to combine different groups of or different activations to and correlated
to which output is which.
That's what output nodes are for in the end.
Don't worry, this will all fit together nicely in the end.
I'll do a review of everything, and it will all make sense, I promise you.
So that's it for the fully connected layer.
Next, we take a look at the final list and then you're in the CNN, which is the soft max layer.
So I'll see you in the next section.
Thank you.