If you were looking to generate additional options, an example code, then you must create an advance,
a script.
Let's click here and see what would happen here.
I have my simple a screen.
We already explored that.
Now let's go here and first give it a name and just control s and save your file this time.
This is my advanced.
A script one, and then we can see it here, Atlanta script.
So just like previous one, just like as simple as screen, we need to define our data and don't forget
to add ACLC clear ampoules.
Also, I'm going to copy these codes and just pasted at the beginning of your code.
Here we just generate the data and then this part is the same with the simple cream, if you haven't
watched the video for simplicity, please go back for additional information and the train function,
his train elim back propagation.
And again, you can change it if you want a number of hidden layers generating and that function.
And let's check it here.
This is an important we didn't have it in a simple scrape.
Let's see, what is it giving us notes that input, that process function equals to remove constant
rows.
But what does it mean, remove constant rows?
Let's check our data here in a command box.
I'm going to type S.O.S. then I want to see my data X and Y, X and Y.
Let's just check them here.
We can see some columns and we can see some rows in Matlab find always a sample.
Must be in a column, meaning each row is for and a specific field.
If we have the same data for one field, it means this is a constant data and then it would remove it.
This code will actually tell us go to the net function from the inputs and apply this process function,
remove constant rows.
Let me give you an example.
Here is a Google.
I had this I just conducted a statistical research about the amount of stress level of graduating students.
I gave them a questionnaire with several question.
One of these questions was, what is your year of study?
Of course, they were graduating as students, so they all marked fifth year as students and CS.
All this field in my Excel fine in my data was fifth year.
It actually won't change anything for the output, meaning it doesn't have any affect or it doesn't
change anything for my output.
I can just simply remove it from feeding it to over neural network.
Another example is I have a class which they were 16 students and all of them were female.
Again, I had no choice but to remove all those feel the field of gender here for this specific class
because they were all female, the output wouldn't be any different.
And it's basically the same for this one.
Now, let's check this one.
This is a map.
I mean, Max, this function scales input and target so that they fall in the range of negative one
and one.
This is what we need to do for normalizing the data.
It just says that apply in mapping on a day to also all the output would be between negative one to
one.
This is for pre processing of inputs and this is a post processing for outputs.
It's just doing the same.
Now let's check it here next.
Dot divide function.
Our data would be divided randomly.
And then another field, another function that we have here is divide.
Moad Divide for this example is sample.
Sample is for a static network.
But if you have a dynamic network, then you need to choose time.
So let me just add some explanation as a comment so you can review this for later and you can choose
a different moment based on your data.
It can be time if you have dynamic network.
The other more that we have is sample time.
Sample time, if I'm not wrong, this is also small.
This is to divide.
Targets by both sample and timestep.
The other rule that we have here is all all just means to divide.
To divide up targets by every Eskild value.
OK, and the last mode that we have here is non non means not divide, not divide the time at all,
meaning all the data would be for training.
There won't be any data for validation and testing for it might be useful while you are working with
different data and different samples.
Now, let's just check the next part, which is defined parameters.
70 percent is for training here, 15 percent for validation and 15 percent for testing.
We have this part in the sample script as well.
You can just change them as you need and then let's check it here next.
That perform function is mean square error.
Again, if you need to use another function, you can just go to the help of Matlab and find the name
of different function and add them here.
Next function that we have here is a plot function to see what is a flat function.
Let me show you an example.
I'm going to make this point as a comment by adding a percentage sign.
Go to the editor and click on Ron.
OK, you might already notice the difference here, you don't have actually any plot because I just
removed all of them.
But now let's see what would happen if I had only this board, for example, plot, perform, run again,
and you would see that you have only the performance.
You can see only the performance, but you don't have any other plots that you can just add them.
You can remove them based on your need.
Training of the network will happen in this form.
So before this board, it was just preprocessing and defining the structure.
But we actually didn't train the network in this part.
We can train the network.
I explained it in as simple as Klebb.
Now let's just move on.
We have these portion in a simple script as well.
Twin target tea, which is over target times, it means every element, every sample in the target times
TR, which has some information about overtraining and then here train mask one.
But what is a train mask?
Computer science mask means anything that can give us the altitude of zero and one that may show you
with some example here, T or dot train.
Mask.
OK, Inter, we just make it a little bit bigger so you can see the data.
These are all the data which have been used for training.
You can see these data have been used for training.
This one, we didn't use it for training.
Actually, the neural network didn't use it for the training.
That's why it's a and and I am here means not.
And no, this is not a no.
It hasn't been used.
Of course, it couldn't just put zero because zero is also a number, but it means that we didn't use
these samples for the training process.
If I just click it again and this time instead of train, I just type test, then you should see the
test.
These data, these samples have been used for testing port.
Now let's do something else.
This time I'm going to multiply it by ti our targets and see what would be the output.
Here are the outputs and the targets, outputs, DNA samples and we can see this one, here is one.
So this is giving us an output.
The other thing that we can do here is easier than ti on that test mask and one OK run spot.
Here are all the samples which are none.
But what if we want to see the training only just add and not here not is then this good or let me just
call it here and put it here.
Reverse or not.
These are the items which has been used for training.
The other thing that I can add here is a find.
All not is not test mask data.
In summary, we can see this is number three.
This one is seven three, four, five, six, seven seven, number seven.
And then here is 11.
How many are they?
They are six.
And that's correct, because we have six samples.
If I type here terrorist index, I can save them in a variable.
I just call it a test.
Equals to this can't find just copy this smart.
OK, now it has a name and I just created a variable and I sent all this data that has been selected
for over testing part into this variable.
I can check it all the different training and relevation, let's do it before going to the next part.
If I change this one to train, then I will see the training data.
And again, if I change it to while I can see the validation data or the data that has been selected
for the validation.
And since world of testing and validation are 15 percent.
So in the number of samples that we have, both of them are six can just use these math to separate
the samples and see which one is for test and which one is for training processes.
Now, let's back there in our Advanta scrape.
The other data that we have here are the plotting.
We had this sport in a simple script as well, and that's basically all in the next session, I'm going
to create another advance, a script, and we will try to change the structure.