So now our training is almost complete.
You can see on the 1980 book we were getting a validation accuracy of points and four and a training
accuracy of mine did over the same
and in the last epoch.
That is the 20th epoch we are getting no 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 lost and training loss respectively
one thing here is to notice that there is a large difference between validation accuracy and training
accuracy validation accuracy is oscillating around 73 to 74 percent whereas training accuracy is increasing
with each epoch currently it is like 93 percent.
And it is increasing this graph such as that that is then what if waiting and no more than with each
epoch or training accuracy is increasing.
But we are not able to increase our validation accuracy.
Does that clear sign of or what fitting in our model now to fix this awful thing we will create some
dummy data.
We will modify our existing data into different forms by applying zoom shear rotation etc. and we will
again train our model after applying all this modifications
then we will compare its impact on our validation accuracy.
Our hypothesis is that by modifying our data and generating some more dummy data we will be able to
increase our validation accuracy
now before doing that.
Let's save over more than we will lose model dot safe than the fight in which we want to save
now.
Let's delude this model.
And clear the session so that we can proceed on with our next model that's all for this lecture in the
next lecture.
We'll modify our data and we will again train this than thank you.