Hello, welcome back to Motlop into session.
We are going to learn how to save data to work in space for a new beginning.
That's a story generating some data X equals two in a space again from zero to two PI, just like what
we did before.
And then I want 50 samples because that's what is giving us the best result for a designer's function
and volumes equal to sign as a function of X.
Finally, plot X and Y.
OK, and called for an F to.
Here we are.
Let's just quickly choose your inputs X and Y, they are in the Matrix column and OK, that's fine.
Let's just move on next.
Three layers would be nice if we also calculated the formula for a dollar.
You can just check it out and find a best number of hidden nephrons here in this example.
It's just three and then click on next.
Click on train.
OK, here.
Oh, that was interesting, actually, let me just show it to you again.
This time the gradient is stopped over training and if you want to see the feet, let's check it very
quickly.
OK, that's a very good fit.
Now, we don't need this one.
Let me close it and click on next.
And this is fine.
Let's click on Next.
And here we are, application development.
Here we have different parts.
We have Matlab function.
If we want to generate some code, you can use Matlab, Imitrex only function.
And here we have Simelane deployment.
There are also some explanation about each which you can read.
But let's just click on next.
And here we are saved data to work space.
We can save network to matlab network object and we can give you the name the default.
Here is Nant.
You can just choose any any name based on your data.
You can save your performance and dataset any information.
You can save the output of Matrix.
You can save the error and inputs and target and more or all of them and you can just give the name
result.
But let me just I'll take them here.
Let's see what would happen if we save the network to Matlab, the network object name Nant.
Look at this workspace in this park you can only see X and Y my input in my output.
But if I click on save result here, I shall see some output, some other output which is net.
So here is our object net.
If you double click on this net, you can see some information, Nancy.
The object with different features here you can see the way the bias values and more.
Oh, let me close this one in back to overcome in window.
But what can we do with this net?
Do use net to approximate a scientist, for example.
I'm going to right here, sinus of Pi over to we all know what's the answer to answer this one.
Now let's try to approximate the output using this net function net and then in parentheses we have
pi over to OK, it's one point zero zero zero one.
It's almost one.
It shows that we train our network very well and approximate output of net function is almost the same
with our Sina's function, which is one.
We can do different things here.
We can define y hat as nets function net of X.
Here is the data of my hand, let me just call it again.
But this time puts that semicolon.
So boy hat is the output for my net function which we trained already with training between neurons
in a hidden layer and fifty samples.
Let's just compare the output of our Sina's function Y and the output of our neural network, which
is here net, and we just put it on a Y hat.
So I'm going to plot X and Y and I want to show it with the color of black.
You can put K for the color of black.
And then here I'm going to show X and each time Y hat the output of our neural network and I'm going
to show it reads color for it.
But let's just make one of them dotted line so we can compare them better.
If you put it here than it would D-line, this red line would be.
OK, zoom and see.
We shall see two two lines.
They are very close and that shows that our train network has a very good output.
This dotted line is the output of over y hat of our neural network and this black line is forever Sina's.
OK, it was a very good feat.
Now close it.
We can even show the errors, define another variable name and e I want to e to showed errors between
why and why hat.
You can also plot.
X and E.
OK, securities Netty is a function with different objects and features back to over fitting tool and
check out this simple a script.