1 00:00:00,160 --> 00:00:00,540 OK. 2 00:00:00,570 --> 00:00:04,550 So welcome to 8.7 where we start training at ossify. 3 00:00:04,650 --> 00:00:09,500 So let's switch into Python lookbook now and get started actually in doing some training. 4 00:00:09,900 --> 00:00:10,290 OK. 5 00:00:10,290 --> 00:00:15,150 So welcome back to Python the book where we're going to start training our model. 6 00:00:15,270 --> 00:00:17,240 So let's go through this line by line. 7 00:00:17,280 --> 00:00:17,850 All right. 8 00:00:18,120 --> 00:00:23,530 So remember sites which is basically how many images we process in one batch. 9 00:00:23,760 --> 00:00:29,610 Effectively this depends on how much RAM of RAM you have available because this is a small data set. 10 00:00:29,610 --> 00:00:33,480 You can probably even get away with going up to 128. 11 00:00:33,520 --> 00:00:39,300 However let's put it at 32 to be safe in case you have little specs on your system. 12 00:00:39,330 --> 00:00:44,670 Epoxy usually we can use ten or twenty five even but for this demo let's just use one. 13 00:00:44,670 --> 00:00:48,140 So you can actually see it around and actually see what happens at the end. 14 00:00:48,480 --> 00:00:54,150 So now let's talk about model that fit and you have something called History equal model not fit. 15 00:00:54,240 --> 00:00:56,760 Now ignore history for a bit for a second. 16 00:00:56,810 --> 00:00:58,550 The key part here is model that fit. 17 00:00:58,560 --> 00:01:01,740 You don't actually need this history stuff here. 18 00:01:01,740 --> 00:01:04,710 This history is mainly needed to plot or graphs afterward. 19 00:01:05,100 --> 00:01:08,360 But for now you can you can create a model without doing this. 20 00:01:08,370 --> 00:01:11,040 You can simply run this line and it trains. 21 00:01:11,040 --> 00:01:18,480 So let's look at what inputs we need into model a lot of that we need to training data we need the training 22 00:01:18,480 --> 00:01:21,280 labels in the format we specified above. 23 00:01:21,570 --> 00:01:27,090 We need to specify OBOT size which is to to POCs which would be one vobis. 24 00:01:27,270 --> 00:01:30,000 Basically that just tells us how much information we want to see. 25 00:01:30,040 --> 00:01:37,590 While training I recommend Vivus one because it's nice to look at and provide more information and less 26 00:01:37,590 --> 00:01:41,190 lead validation data which would be extra us in the White us. 27 00:01:41,550 --> 00:01:46,410 And this has also appeared in a tuple as you can see just brackets basically make sure it's in shorts 28 00:01:46,410 --> 00:01:50,270 a tuple and that's what we fit a model on. 29 00:01:50,280 --> 00:01:52,620 And lastly let's take a look at this line. 30 00:01:52,650 --> 00:01:57,440 This is where we get the evaluation metrics at end of our training evaluate basically just gives us 31 00:01:57,440 --> 00:02:01,810 a score at the end at law school and accuracy accuracy score. 32 00:02:02,100 --> 00:02:05,390 So let's now run this and visualize how training is done. 33 00:02:07,900 --> 00:02:08,390 Amazing. 34 00:02:08,470 --> 00:02:12,800 So now I mean I find this quite interesting to watch but you may be bored. 35 00:02:12,880 --> 00:02:19,660 But right now this is basically going to add images here how many images we have seen so far in this 36 00:02:19,660 --> 00:02:26,040 data set the estimated time to completion two in two and a half minutes left a loss. 37 00:02:26,050 --> 00:02:30,460 And you can see it continuously going down slowly as she is pretty fast. 38 00:02:30,520 --> 00:02:34,280 To be fair and accuracy This is a part of like to watch too as well. 39 00:02:34,450 --> 00:02:38,130 Oh accuracy is slowly going up the more and more they do we fito model. 40 00:02:38,320 --> 00:02:42,010 And this is on one epoch and we're really at 60 percent accuracy. 41 00:02:42,070 --> 00:02:45,940 So if you wait to sell it I'm going to Fasold a video while we watch this. 42 00:02:46,270 --> 00:02:52,810 You can actually see after one book we actually reach quite good politician and training accuracy. 43 00:02:52,810 --> 00:02:58,390 However you don't see the validation accuracy and validation loss just yet because right now what's 44 00:02:58,390 --> 00:03:05,110 happening is that we're passing a data set of a training data set to a model and back propagating it 45 00:03:05,440 --> 00:03:06,530 and updating the weights. 46 00:03:06,640 --> 00:03:08,560 And that is how these things are improving. 47 00:03:08,620 --> 00:03:09,590 Losses going down. 48 00:03:09,640 --> 00:03:17,140 Accuracy going up and once one epoch is complete then we passed a test data into it and then we see 49 00:03:17,200 --> 00:03:23,840 a test of validation loss or Test loss and validation accuracy or test accuracy. 50 00:03:23,860 --> 00:03:25,250 So let's wait until it's done. 51 00:03:30,790 --> 00:03:31,670 OK good. 52 00:03:31,670 --> 00:03:32,110 There we go. 53 00:03:32,120 --> 00:03:33,040 We're finished. 54 00:03:33,260 --> 00:03:38,090 So now let's look at the results here and try to figure out how we analyze what had just happened. 55 00:03:38,090 --> 00:03:46,590 So as you can see after we fed all 60 images 60000 images into a model and train that updated awaits. 56 00:03:46,820 --> 00:03:52,580 We now have a loss that is decently low point five nine Well accuracy on our training data set. 57 00:03:52,580 --> 00:03:56,870 That's a point 8 1 4 5 8 1 and 1/2 percent roughly. 58 00:03:57,070 --> 00:04:02,850 And but interestingly our validation loss is even much lower than what treating loss and validation 59 00:04:02,900 --> 00:04:05,140 accuracy is 93 percent. 60 00:04:05,150 --> 00:04:09,030 Now that's not normal but it's a good problem to have. 61 00:04:09,050 --> 00:04:15,310 It means that our model has generalized very well and is actually quite accurate on our test data. 62 00:04:15,320 --> 00:04:22,170 Now this doesn't always happen usually always on most cases have higher accuracy and training then when 63 00:04:22,340 --> 00:04:24,410 the validation set. 64 00:04:24,530 --> 00:04:26,270 However let's wouldn't complain about this. 65 00:04:26,270 --> 00:04:27,850 This is a good problem to have. 66 00:04:28,280 --> 00:04:33,150 And basically we have a loss and test accuracy printed at the summary here. 67 00:04:33,470 --> 00:04:37,820 It's also the same values here but we just run this again just to basically see it. 68 00:04:37,870 --> 00:04:43,460 And if you have many ebox as well this is a summary at the end of the mall Okay. 69 00:04:43,730 --> 00:04:47,670 So now we're going to move on to plotting or loss accuracy charts. 70 00:04:47,900 --> 00:04:53,020 Basically what you should do is run this for maybe 10 epoxy can actually have nice graphs.