1
00:00:02,200 --> 00:00:04,630
‫So now our training is almost complete.

2
00:00:07,330 --> 00:00:15,220
‫You can see on the 19th Epoch, we were getting a validation accuracy of .74 and a training

3
00:00:15,310 --> 00:00:17,230
‫accuracy of 92 percent

4
00:00:20,560 --> 00:00:27,700
‫And in the last epoch, that is the 20th epoch, we are getting the validation accuracy of 73 percent.

5
00:00:28,720 --> 00:00:35,410
‫Now, let's just plot all this accuracy values on a graph.

6
00:00:42,260 --> 00:00:51,510
‫Now, the orange line here is our training accuracy and the red line is validation accuracy and green

7
00:00:51,510 --> 00:00:58,890
‫and blue lines are validation loss and training loss respectively.

8
00:01:03,270 --> 00:01:09,480
‫One thing here is to notice that there is a large difference between relevation accuracy and the training

9
00:01:09,480 --> 00:01:10,110
‫accuracy.

10
00:01:12,450 --> 00:01:17,400
‫Validation accuracy is oscillating around 73 to 74 percent.

11
00:01:18,660 --> 00:01:22,770
‫Whereas training accuracy is increasing with each epoch.

12
00:01:23,220 --> 00:01:28,350
‫Currently it is 93 percent and it is increasing.

13
00:01:30,150 --> 00:01:32,960
‫This graph suggests that there is an over fitting

14
00:01:33,320 --> 00:01:34,230
‫In our model

15
00:01:36,600 --> 00:01:39,420
‫With each epoch our training accuracy is increasing.

16
00:01:39,510 --> 00:01:43,110
‫But we are not able to increase our validation accuracy.

17
00:01:43,290 --> 00:01:47,490
‫This is a clear sign of overfitting in our model.

18
00:01:50,110 --> 00:01:54,130
‫Now, to fix this over fitting, we will create some dummy data.

19
00:01:55,420 --> 00:02:03,310
‫We will modify our existing data into different forms by applying zoom, shear.

20
00:02:04,590 --> 00:02:06,120
‫rotation etc

21
00:02:07,840 --> 00:02:11,170
‫And we will again train our Model after applying

22
00:02:11,320 --> 00:02:12,820
‫All this modifications.

23
00:02:15,800 --> 00:02:20,330
‫Then we will compare its impact on our validation accuracy.

24
00:02:20,730 --> 00:02:28,640
‫Our hypothesis is that by modifying our data and generating some more dummy data, we will be able to

25
00:02:28,640 --> 00:02:30,650
‫increase our valuation accuracy.

26
00:02:34,650 --> 00:02:37,700
‫Now, before doing that, let's save our model

27
00:02:38,700 --> 00:02:44,430
‫We will use model, dot save and then the file in which we want to save.

28
00:02:48,670 --> 00:02:56,370
‫Now, let's dilute this model and clear the session so that we can proceed on with our next model.

29
00:02:58,460 --> 00:03:01,390
‫That's all for this lecture in the next lecture

30
00:03:01,920 --> 00:03:06,080
‫We'll modify our data and we will again train this modern.

31
00:03:07,260 --> 00:03:07,640
‫Thank you.