I am previous session, we already discussed changing different train parameters.
Now let's check this part to recalculate training, validation and test performance.
If I remember in advance of script number one, I just wrote in a come in window, which was separating
all the indexes that has been used for training the network and also all the indexes and samples which
we use for training and validation.
And we even calculated the error of them.
Let's do the same here.
This time I'm going to create a Wii both for each training, validation and testing so we can call them
whenever we need to.
For the start, let's just separate these three parts.
I will show it as I come in like training plant.
Next, we need validation part and we need another session for testing control X and pay citya control
V for training for validation.
Do the same.
Let's create another variable and name to train index in a train index, I want to put all the training
samples that has been used.
I'm going to use, find function, find every team that is not ñan.
They're in train MSK t are dot train mask.
Don't forget to put this let me go now, do the same for validation data and testing data, but don't
forget to call it out for validation.
And of course, this one is from ask here we have testing decks, a variable that we created, name
test index.
You can just give it any other name that you want.
Find what?
Find anything that is not meant from T.
Are it tested very well.
Now you can already run your program by clicking on the run or you can just type the name of your screen.
Here is Advance is Craib number two.
So I'm going to just click on it here.
We can see that the program run.
I'm going to close it.
And this time let's call for the variable that we created, which is try an index.
Oh, OK.
I just had mistype that.
This is in the the good thing about the come in is, is all of this suggesting a correct word for you
to call?
So the variable that I create called trade I n the X, so here is Train I, D, I and X, just click
on Enter.
These are all the samples that has been used for our training and then the same.
You can just call for that.
Let's see them here.
We have only six samples and as well as for validation index.
OK, here it is.
Now let's back here and change this train together a little bit.
I really don't like this multiplication.
Let me change it to all the rows and from the columns.
Choose that Troian index or the index says that has been used for training in apprentice's, close,
open and close parentheses type here.
All the arrows to mention all the arrows, we can just put a double column and then next parameter.
Here is other columns for the columns.
Which columns?
The train index columns.
This variable that I created here now Velda, to run the program to make sure that we did everything
correct and call for train targets.
Here we can see the list of train targets.
That's better.
Now, let's do the same for validation.
Target open and close parentheses, put a double column and then here instead of train.
This one is of our index.
Please do the same for all test index.
OK, here we go.
We can do the same for inputs, for output, the output of our neural network.
Let's see how would it happen here.
Just copy this part pasted again for inputs and pasted it one more time for this time outputs and let
me change the name.
This is train inputs.
Train inputs.
This one is train out puts my inputs in this program called X and my outputs are called Y.
Y is the output of neural network.
Please do the same for your validation and testing.
OK, here we go.
I already changed them for validation and testing.
I just added test inputs, test outputs as well as validation inputs and validation outputs.
Just copy, paste them and change them into X and Y.
What else can we do here?
Actually, there are lots of things to do.
For example, we can even calculate the train errors for the training errors.
I'm going to define another variable I'm calling train errors.
This one is equal to train targets.
This variable minus the train outputs the output of our neural network variable.
You can calculate the error for each of them.
You can just do it for the validation and you can do it for the testing.
So there we have the validation errors, we have the test errors, and this one is validation targets
minus the validation out, which is the same as testing.
Now, let me run the program to see if we did everything correct.
OK, it turns out we have a problem here.
Line 81, let's check the problem together now is undefined function or variable.
I just missed time that this one is out p you t is now I everything correct.
Let's see.
Yeah, here we have.
OK, now let's call for the test errors to see one or two errors for the test here we have the errors
but we have train errors.
We can't see them here and we can have the validation errors.
So you can just separate your data for better analysis.
We have over outputs, we have the inputs.
Let me just show the train inputs.
These are the samples that we used for input to train our network and then you can just call for any
of them.
So we already covered them, recalculating training, validation and test performance for the test performance.
You have your codes here.
If you need them, just put a semicolon at the end of each one and then uncommented them.
Next part would be plotts.
I'm going to just give a similar explanation for diplomats in the next video.
So you there?