Here we have different plots, we have performance, we have training stage, we have our histogram
and we have regression and feet.
Let's take a look at this performance.
Just click, OK?
Here we can see the performance of our neural network at the beginning.
The validation test result is just good.
It's a good result.
It's going toward zero.
This is a verminous score forever and our goal is actually to go for zero.
The training result actually is good here.
It's just going for zero.
But the problem was validation we had failing for validation.
The validation just failed here once and then it just correct itself again.
Improving, improving.
And here it is.
We have another fail.
It will count the number of failures.
If our system fails six times, then we will disrupt the training.
But the former system for validation failed once here and then improve itself again will continue the
validation and let us start six times.
One, two, three, four, five and six.
Then he just stopped the training process.
Let's take a look at training a state and see what we have here.
We have the gradient, we can see the mill, and this one is a validation check.
So for the validation check here, we had some failure during the process, but it's just correct itself
and try to improve again.
We had a total of 17 EPOP.
This means our system trained itself 17 times each time, change eight weights and then train again.
And here are the results for failure.
We had one failure here two times three, four, five and six.
And I remember we just mentioned that if it just reached six times failure continuously, then I stopped
the training process.
Let's take a look at Şeref.
Histogram error of histogram should be around zero, but we can see some of the testing are not good.
They're not around zero.
So it's not a good error.
Histogram, if you have more data, if you have more samples, then you can have a better result and
you can just understand it better.
Let's check the regression.
OK, here it is, we have a good training for regression.
The goal here is why should we be equal to teach cheese over Target and why is our output?
We can see the output here is almost equal one times the target plus this amount.
And this is upper regression coefficient.
These are our data.
We can see the data here and it just try to feed to the data.
And it was a good training.
We also saw it on our performance that the training gets.
It's doing good.
But the problem was with validation and with testing.
Testing actually is not good at all.
We also saw it in the error histogram that testing is not around zero.
So we can see here it's not a good fit.
It should be toward this line and it should be equal to one for the validation.
Here we have it.
And this is showing the overall performance of our system, which is not bad, but not very good.
Also here we have one data here, one data here.
And for these two data, we don't have a very good fit.
Maybe if we increase the number of samples, then we can have a better result.
And finally, we have fit.
This is a function feed or the output element.
We know that our output is a sign of function.
So these must be look like a sinus function.
But however, it's not very good.
It's almost like a sinus function, but not very good fit, meaning the neural network that we train
was able to come up with this output for a sinus function, which is not very good.
And also it's not a disaster.
It's like sinus function, but not very good.
Fit this part.
The orange line is showing the error and we can see the error is too much here for these two samples.
The error is a lot.
Blue Dot is showing the training target.
The plus is showing the training output and going for validation rate for testing.
In this one is overfitting line.
Here are our errors for the first two samples.
It's actually a lot.