So now our training is almost complete.
You can see on the 1980 book, we were getting a validation, accuracy of points and forward and a training
accuracy of mine to do the same.
And in the last epoch, that is the 20th epoch, we are getting the validation accuracy of 73 percent.
Now, let's just blot all this accuracy values on the graph.
Now, the orange line here is our training accuracy and the red line is validation, accuracy and green
and blue lines are validation, loss and training loss respectively.
One thing here is to notice that there is a large difference between relevation accuracy and the training
accuracy.
Validation accuracy is also lurking around 73 to 74 percent.
Whereas training accuracy is increasing with each epoch.
Currently it is 93 percent and it is increasing.
This graph suggests that that isn't what we're doing.
And there were more than.
With each epoch or training, accuracy is increasing.
But we are not able to increase our valuation accuracy.
This is a clear sign of overfitting in our modern.
Now, to fix this awful thing, we will create some dummy data.
We will modify what existing data and two different forms by applying zoom shear.
Rotation acceptor.
And we will again train our Morton after a plane.
All this modifications.
Then we will compare its impact on our valuation accuracy.
Our hypothesis is that by modifying our data and generating some more dummy data, we will be able to
increase lower valuation accuracy.
Now, before doing that, let's say, well, what a.
We will use model, dot, save and then the fight in which we want to save.
Now, let's dilute this model and clear the session so that we can proceed on with our next model.
That's all for this letter in the Legacy Lecture.
We'll modify our data and we will again train this modern.
Thank you.