‫So now our training is almost complete.
‫You can see on the 19th Epoch, we were getting a validation accuracy of .74 and a training
‫accuracy of 92 percent
‫And in the last epoch, that is the 20th epoch, we are getting the validation accuracy of 73 percent.
‫Now, let's just plot all this accuracy values on a 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 oscillating 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 there is an over fitting
‫In our model
‫With each epoch our training accuracy is increasing.
‫But we are not able to increase our validation accuracy.
‫This is a clear sign of overfitting in our model.
‫Now, to fix this over fitting, 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 valuation accuracy.
‫Now, before doing that, let's save our model
‫We will use model, dot save and then the file 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 lecture in the next lecture
‫We'll modify our data and we will again train this modern.
‫Thank you.