Here we have validation and test data, it will set aside some samples for validation and testing during
our training process as we are going to divide over samples, we have a total of 20 samples which will
be divided into three sets.
One set is for training the network.
We need to give some problem and some solution.
We are also showing the solution to our network.
This just looked like when we are teaching in a classroom and we will give some example to students
and we solve those examples and we will try to do it as much as we can here.
It's 14 samples.
We will do it for 70 percent of data and 14 samples.
We can't really change the 70 percent.
But here we have two more option validation and testing.
Testing, as the name explain itself is testing like final exam.
At the end of the semester, we will conduct a final examination for a student to check it out.
If this training either if this course was useful, did the students learn something or not?
We will put 15 percent of data for over testing.
However, we can just adjust these numbers based on our data and training a strategy.
I'm going to get to them later, but testing will happen at the end of the semester.
And if 90 percent of the students fail, we cannot do anything.
We did something wrong during the training, but it's already too late.
And phrase that we use is training students or you see this next have been overtrained.
What horrible validation here at the end of the semester, if we realize 90 percent of the students
fail, it's already too late.
What is strategy to understand and to find a problem earlier is conducting some quizzes during the semester,
which we call them here validation.
So validation are some testing and some quizzes during the training process.
These are used to measure network generalization and to how training visualization a stop improving
during the quizzes.
If we realize that each quiz result is just worse than the previous one, we need to give up on those
students.
We need to change the training to agree to training a strategy.
And there is no point for continue this training because we are getting the negative result.
We shall stop the training.
Let me just explain it with a better example.
Let's take a look at these two networks.
I'm going to choose color of blue for showing the result of training.
This is our era.
This is over.
And just showing it with E!
This is the error.
And this is over time during the time first.
This is the error.
And then in this system, in this net for the error is going to almost zero, very close to zero.
Our goal is to make the error zero, which means the system is a very good system and the error is zero.
But in this system, in other hand, the error having to start from here and the same point, it's going
towards zero, but not very good samples.
It's going to be something like that.
And then I'm going to show the validation data with the color of green.
So this is my validation data.
The validation will start from this error.
This amount of error and you wouldn't get time is just improving.
Very good.
Oh, what happened here?
It's just not going well.
Now, the error here at this point is start to increasing.
So it's not a very good team to training.
Something went wrong.
The training was good at the beginning.
They were start to go towards zero.
But now the error is not very good, meaning these students or this network is getting over trained.
But on the other hand, in this network here, we have the validation data.
It's just going good and the other one is over testing.
You're going to just show it with a color of red to match the Matlab fine for the testing of this one.
The testing is going actually good.
We might get a good result for testing, but validation is not really good and we can't really trust
it.
And in this network, this is, let's say, our testing.
So if you want to choose a product with this result, which one would you choose?
The first one or the second one?
That's what is happening here.
Suppose we have two products, they are cooling systems, one of them has an error of minus plus 12
percent error.
This war is giving us an error around to person, which is very good, minus or plus two percent error,
but sometimes it has eighty five percent error.
So which product would you choose if you want to set your temperature to plus six?
This network will give you something around six wheat, 12 percent of error.
This system is giving you a better result in most of the time, but sometimes it might have 80 percent
error.
This part, of course, we cannot have this product.
We cannot trust it because the behavior of the system is not predictable.
We cannot predict the behavior of the system.
That's why we can't really trust this network.
And this network has the better result for us and we can trust this product more.
So meaning these steps forward is just showing the better meniscal error.
It has a better miniskirt error, but in a matter of demoralisation, it's not doing so well.
The product.
Number two, regarding the meter squared error, it's not as good as product one, but it's trustworthy.
We can trust it because it's not going to change its behavior.
This is a very important point that we need to keep in mind while we are training and we're all network.
Now, let's back to Matlab.