OK, let's start working the simplest craib, just click on it and then you will have new window here
and your Massola.
This is a simple script and it's giving us some information.
Take a look at some of these informations here.
We have X, which is of our input equals X, the boat input of our system and neural network.
Of course, we are feeding it with the same data.
So there are the same and then teasers then for Target the our target.
We want it to be equal to Y, which is output of our system.
This one is between function and for this particular example, we are using train L.M., which is a
stand for Leverne Berg back propagation.
There are several, I agree, Tim, that you can use for training your function.
If you are looking for any specific algorithm, you can just search in a help box of your matlab and
find a code of Matlab for that algorithm and then copy it here.
Next, information that we have here is number of Naranj and our hidden layer, and that's three.
This is what we said while we were working with the unfitting tool.
You can change it here.
And then the next one is Feed Tonette function.
We are saying that from Freaknik function, get the train function and then number of neurons in each
layer and put them into an object name net this.
It has several features.
One of these features is Divide Parro, which is for dividing the data into the training ratio, validation,
Rafia and test ratio.
And as you can see here, we just didn't change the default, which was 17 15 percent for validation
and 15 percent for testing.
You can change them in the program here as well.
We already design our network.
We have over neural network.
We define the number of neurons.
We defined algorithm to be used, but we have to start training, get from this part.
We will start training our network.
So it's using train.
It gets three inputs net, which is our function of NAV.
This is our object that we created.
And then here we have X inputs and finally why we choose the target outputs.
This one will give us two parameter net, which is our net signal and then another one named TR.
This TR has some information about the training process.
Let me just change you to a space then.
Let's check it here for the test network.
The net of X would be equal to we will send equal here in programming means we will set Y to the net
of X.
It means the data from here goes to our variable Y and we have another variable name E we want this
E to have the G subtract T and Y what.
What is this G subtribe.
Is this then for generalize.
Subtract it, we'll just subtract the two sets of data which have the same format.
For example, here there are just numbers ts for our target and why is the output of our system.
So we want to subtract the output off of our neural network from the output of our system and find out
what is upper error here.
We have another function perform and it gets three variables net target in Y and finally we will send
it to another variable name performance.
This net here, you can call it anywhere in the program and then it will open for you and fifteen to
window before running the program.
We need to do something.
First of all, of course, control is to save your file.
I'm going to complete a simple script.
One.
OK, here we can see over fine here.
Then we need to define our data.
We haven't decided yet.
X is equal to the space.
From zero to two pi, and this time let's try another network with 40 samples and then four, why,
of course this is equal to sign this off X.
The other thing that I'm of recommending to add in a beginning of every program that you have while
working with neural networks First Codicils ACLC to just clear your command window.
The other one is to clear the memory.
If you don't put these in some situation, you might get some error.
Another one is close.
All this one bill pulls all the open graphs.
You can manage your data better now all six.
Let's click on Run.
OK, the first thing that you might notice in that comment window is the performance, the performance.
This is the way she referred to performance.
And here we can see that and fitting tool that we were opening before using the vertical user interface.
This time we just opened up by running the program, which Matlab generated for us.
So it's just giving some information, like in a hidden layer.
We have to inherent in the output layer.
We have one now and we can change number of neurons in the output layer.
Let me close this one here.
You can see that validation check is top overtraining.
Let's see the performance.
OK, and you have some other information, like let's check the feet.
OK, this is a good fit.
It's just like a sinus wave.
And that's what we're looking for from this neural network.
Now, let me close this one.
If you are writing a paper or you want to have some particular report, you probably need different
plotting.
As you might already guess.
This percentage sign will make comment, will make the line as a comment.
If I remove that.
You can see this is now active and it's in the color of black.
So it's active and the active two of them probably we don't need the regression.
However, you might need it for analyzing part of your data so you can just make them comment or uncommon.
But let's see what would happen if I just have the regression and fit this jacket here.
It will first train my network and then it will open to window.
This simple creep has limited features and functions, if you're looking for, modify your network more
and you need to use more features and functions of Matlab.
Then you need to open advanced a script.
In the next session, I'm going to explain advance the script.