1 00:00:01,020 --> 00:00:09,710 ‫In the last couple of lectures we learned how to train and create what models one think. 2 00:00:09,720 --> 00:00:15,240 ‫You have noticed as each time we opened that you buy the notebook. 3 00:00:15,240 --> 00:00:24,500 ‫We started creating and training our models from scratch but we don't have to do that. 4 00:00:24,520 --> 00:00:27,690 ‫There is a very simple way to save your model. 5 00:00:27,820 --> 00:00:35,880 ‫After you have trained it killed us will save both the model architecture including the every layer 6 00:00:36,020 --> 00:00:45,380 ‫but parameters and the value of all the model parameters for each layer that is biases and weights value 7 00:00:47,860 --> 00:00:54,670 ‫so instead of creating the model each time you open your department notebook or initiate a new session 8 00:00:55,240 --> 00:01:03,280 ‫you can just load the model that you have previously saved to get all the details of that more than 9 00:01:05,420 --> 00:01:07,950 ‫now saving model is very easy. 10 00:01:08,000 --> 00:01:15,330 ‫You just have to write to add more than name so in the last lecture we created a model with functional 11 00:01:15,330 --> 00:01:18,360 ‫API and object name was model. 12 00:01:18,630 --> 00:01:23,870 ‫So I am saving that same object so I can write model Dot. 13 00:01:23,880 --> 00:01:25,480 ‫Save save is the keyword. 14 00:01:26,100 --> 00:01:31,800 ‫And in the parenthesis you have to write the name that you want to give to that file. 15 00:01:33,180 --> 00:01:37,930 ‫All these models are saved in as deep a file format. 16 00:01:38,250 --> 00:01:49,600 ‫The extension of that is dot at five so if you just run this you have saved your model and new at present 17 00:01:49,660 --> 00:01:52,340 ‫working Barrick tree. 18 00:01:52,600 --> 00:02:03,590 ‫So if I open my Barrick tree this is this and you can see my model is here. 19 00:02:03,670 --> 00:02:12,200 ‫The name of Martin is and why func underscored more than is the same as I have mentioned. 20 00:02:12,490 --> 00:02:14,780 ‫You can see your present working directory. 21 00:02:14,950 --> 00:02:24,120 ‫With this come I just have to write but send this symbol and b w Li a sentence for present working better. 22 00:02:24,380 --> 00:02:31,810 ‫But if you run this you will get the directory location and in that directory you can find the model 23 00:02:31,870 --> 00:02:32,740 ‫that you have saved. 24 00:02:34,480 --> 00:02:41,650 ‫Now in case you want to change your or indirectly you can do it with this keyword. 25 00:02:41,800 --> 00:02:48,200 ‫You have to write percentage then CDE and then the new directory name. 26 00:02:48,200 --> 00:02:53,510 ‫So as we have seen I have this more than pleasant in my flat. 27 00:02:53,560 --> 00:02:57,010 ‫Now let's just delete the more diluted even 28 00:03:00,430 --> 00:03:02,430 ‫so I have deleted the model. 29 00:03:02,710 --> 00:03:06,040 ‫And I am also clearing the session off get us 30 00:03:08,860 --> 00:03:09,190 ‫now. 31 00:03:09,190 --> 00:03:17,920 ‫Again I am loading this model with Lord underscore model my code and the head also you have to just 32 00:03:17,920 --> 00:03:26,680 ‫provide the name of the file and I am saving this and another object known as more than let's just try 33 00:03:26,890 --> 00:03:31,800 ‫loading the model that we have recently saved. 34 00:03:32,140 --> 00:03:34,120 ‫Now we can call the summary matter. 35 00:03:34,120 --> 00:03:38,740 ‫This should give me the summary that we saw earlier. 36 00:03:38,750 --> 00:03:49,420 ‫By creating regression neural network with functional Libya tests run this you can see that we have 37 00:03:49,420 --> 00:03:59,650 ‫the same somebody remember we believe data were more than and then we loaded it using the same version. 38 00:03:59,980 --> 00:04:03,750 ‫Now somebody is just the architecture of that model. 39 00:04:03,910 --> 00:04:12,250 ‫You can also predict using this model because it already have the optimized weights and biases when 40 00:04:12,280 --> 00:04:15,870 ‫you so you can use it for prediction. 41 00:04:15,870 --> 00:04:25,650 ‫Also you can see this is the same prediction that we were getting earlier with functional Libya but 42 00:04:26,100 --> 00:04:37,590 ‫you can say one model only after the completion of its training but usually we have a very large dataset 43 00:04:38,370 --> 00:04:45,870 ‫which can take up to eight to 10 hours just to train the model for that scenario. 44 00:04:45,870 --> 00:04:49,920 ‫We can use callbacks. 45 00:04:50,340 --> 00:04:59,550 ‫You can think of callbacks as check points which will allow you to save your model of after each epoch. 46 00:05:00,540 --> 00:05:04,030 ‫So you don't have to wait for the come to training. 47 00:05:04,110 --> 00:05:07,930 ‫You can save your model after each epoch as well. 48 00:05:09,810 --> 00:05:18,480 ‫Let's just again delude this model and the station here. 49 00:05:18,620 --> 00:05:24,710 ‫I'm just taking a small example of a sequential model to densely and when output layer. 50 00:05:24,790 --> 00:05:26,330 ‫I wouldn't be using the same data. 51 00:05:27,310 --> 00:05:29,050 ‫Let's just compiling. 52 00:05:29,260 --> 00:05:33,310 ‫We are using the same but I meant that as a. 53 00:05:34,030 --> 00:05:42,340 ‫Now you can provide this callback parameter while training the more than So earlier we were on deployed 54 00:05:42,350 --> 00:05:46,060 ‫in creating data e-books and validation dataset. 55 00:05:46,420 --> 00:05:51,230 ‫Now along with this we can also put away callbacks. 56 00:05:51,550 --> 00:06:01,150 ‫So you have to write callbacks and then here you can provide the list of callbacks that you have created. 57 00:06:01,150 --> 00:06:03,330 ‫This is how you create a callback. 58 00:06:03,370 --> 00:06:10,990 ‫So this is what a variable name that is checkpoint callback and you have to write gave us the callbacks 59 00:06:11,240 --> 00:06:12,160 ‫not models. 60 00:06:12,180 --> 00:06:23,760 ‫Checkpoint here I can write the finally but since we are running this model for banning books that means 61 00:06:23,820 --> 00:06:27,480 ‫we are generating 10 fighters for each epoch 62 00:06:30,170 --> 00:06:38,240 ‫so if I provide a constant file name such as my gate on Morton it will just overwrite the fight for 63 00:06:38,240 --> 00:06:46,590 ‫each of this penny box so at then I would be only getting a single file of my 10 people. 64 00:06:49,210 --> 00:06:54,430 ‫Now to resolve this problem we can use these variables. 65 00:06:54,550 --> 00:07:04,130 ‫So for one epoch I want to save my file as model dash 0 1 dot at 5. 66 00:07:04,270 --> 00:07:09,820 ‫For the second book I want to save it as model best 0 2 that's fine. 67 00:07:10,360 --> 00:07:12,810 ‫And so on the last 10 people. 68 00:07:13,180 --> 00:07:17,730 ‫So you can use such variable names in your file name also. 69 00:07:18,040 --> 00:07:21,880 ‫So you don't have to manually light each file name 70 00:07:24,870 --> 00:07:28,590 ‫so here model is the name that I have given. 71 00:07:28,770 --> 00:07:36,300 ‫And this is the variable name which is kept in the loop record so I can write a book and I want the 72 00:07:36,420 --> 00:07:38,130 ‫two digits of each book. 73 00:07:38,310 --> 00:07:40,450 ‫So for one I want 0 1. 74 00:07:40,500 --> 00:07:41,880 ‫So that's why 0 2. 75 00:07:41,880 --> 00:07:44,790 ‫D If I wanted three digits. 76 00:07:44,810 --> 00:07:50,910 ‫So suppose if I was running for 300 bucks I would want three digits. 77 00:07:50,920 --> 00:07:56,270 ‫Zero zero one or hundred or two hundred and fifty two ninety nine. 78 00:07:56,310 --> 00:07:59,490 ‫In that case I would have written zero D 79 00:08:02,380 --> 00:08:05,160 ‫so let's just create this check point. 80 00:08:05,230 --> 00:08:06,820 ‫Callback again. 81 00:08:06,850 --> 00:08:12,330 ‫Get us or callbacks thought model check point and then the variable name and inside variable name all 82 00:08:12,360 --> 00:08:17,920 ‫so we can get variables you mentioned a friend epoch values. 83 00:08:18,310 --> 00:08:22,340 ‫Yes run this and now just drain the mortar. 84 00:08:22,500 --> 00:08:24,960 ‫We are using the same method. 85 00:08:25,060 --> 00:08:30,080 ‫Wonder Lord fit grinning people validation data and then the quarterback 86 00:08:35,490 --> 00:08:43,350 ‫so with each of this epoch I am also saving a model file for that epoch. 87 00:08:43,350 --> 00:08:50,640 ‫So if we just go back to our directory we should have and model files. 88 00:08:50,640 --> 00:09:00,260 ‫Here you can see we have more than less 0 1 model illustrator to model base than this for first epoch. 89 00:09:00,260 --> 00:09:04,120 ‫Secondly both totally book and so on builder 10 people. 90 00:09:04,660 --> 00:09:13,120 ‫If I would ever use the single file name such as more that than I did then I wouldn't be getting on 91 00:09:13,120 --> 00:09:18,670 ‫the single file because of that e.g. bug that I was or what I could by the next book. 92 00:09:18,670 --> 00:09:27,290 ‫When you so remember to use the variable names of each book while creating a modified for each epoch. 93 00:09:27,400 --> 00:09:34,530 ‫Now I can load this files similar to the previous time. 94 00:09:34,700 --> 00:09:41,500 ‫You just have to write it as thought model Lord Lord modern and the file which we want to load. 95 00:09:43,120 --> 00:09:50,860 ‫So just to demonstrate that Let's delete the model and clear the session now. 96 00:09:50,920 --> 00:09:59,070 ‫So suppose if I want a file of my ten people a model law. 97 00:10:02,320 --> 00:10:08,540 ‫So this is the model I want to export. 98 00:10:09,320 --> 00:10:16,680 ‫You can see if important that model so in future also if I am opening the department notebook after 99 00:10:16,700 --> 00:10:23,060 ‫three or four days I can just write this model that gave us thought model dot load model and then the 100 00:10:23,070 --> 00:10:25,560 ‫modelling to load that model. 101 00:10:25,610 --> 00:10:33,350 ‫There is absolutely no need of creating compiling and creating the model once again. 102 00:10:33,680 --> 00:10:36,140 ‫We can also use evaluate time. 103 00:10:36,380 --> 00:10:42,010 ‫You can see this model object is working now. 104 00:10:42,320 --> 00:10:48,830 ‫There is no need of saving the model at each epoch. 105 00:10:49,310 --> 00:10:56,910 ‫If you are using the validation set during the training you can set the save best only equal to group 106 00:10:57,060 --> 00:10:58,970 ‫by creating a checkpoint callback 107 00:11:01,660 --> 00:11:07,560 ‫that will only save your model when it's performance on the validation set is best. 108 00:11:10,210 --> 00:11:20,410 ‫So suppose all of this then epoch values we were getting the best validation is scored I suppose epoch 109 00:11:21,390 --> 00:11:27,040 ‫then in the final fight we will be getting the model corresponding to epoch 8. 110 00:11:27,070 --> 00:11:36,000 ‫There is no need to create a separate model for all this epoch where lose because generally we want 111 00:11:36,000 --> 00:11:39,130 ‫a model corresponding to the best score on the 112 00:11:42,830 --> 00:11:53,410 ‫so let us lead the model in creating this structure and here while creating the callback I am also setting 113 00:11:53,410 --> 00:12:02,720 ‫the parameter value of save best only equal to crew and then I would be getting only a single model 114 00:12:03,100 --> 00:12:03,730 ‫with the name. 115 00:12:03,790 --> 00:12:09,880 ‫Best model does the model which is corresponding to the best value of validation is score. 116 00:12:12,610 --> 00:12:20,110 ‫So let's just create this checkpoint and then we are creating the model. 117 00:12:20,170 --> 00:12:24,340 ‫Again extreme epochs validation set and then the callbacks 118 00:12:36,020 --> 00:12:41,250 ‫now to get the best model we can just import the best model that we have saved. 119 00:12:41,840 --> 00:12:49,640 ‫So if you see in their directory also we have saved this best more than faith. 120 00:12:51,500 --> 00:13:00,110 ‫We are just loading and using that more than do you and your performance on that site. 121 00:13:00,430 --> 00:13:07,070 ‫No one of the best method to screen your model is to use a little scoping. 122 00:13:07,470 --> 00:13:14,280 ‫What I'm referring to a list dropping is that you would run your model for a very large number of epoch 123 00:13:14,280 --> 00:13:15,240 ‫values. 124 00:13:15,240 --> 00:13:25,880 ‫Suppose here I am using 200 e-books and then I will keep an eye on my validation site score so suppose 125 00:13:26,390 --> 00:13:34,730 ‫if after 60 e-books my validation set accuracy is not improving then I will stop the training at that 126 00:13:34,730 --> 00:13:40,860 ‫point and I will be using the model with the best validation is quote so far. 127 00:13:41,630 --> 00:13:43,530 ‫So this is an ideal situation. 128 00:13:43,550 --> 00:13:47,480 ‫We don't have to worry about how many books we want to run. 129 00:13:47,480 --> 00:13:50,220 ‫We just set a very high value of people. 130 00:13:50,300 --> 00:13:59,590 ‫We monitor the validation site school and then select the best more than that we have got so far so 131 00:13:59,600 --> 00:14:07,490 ‫to achieve that we will be needing an another callback which we generally call a list stopping callback 132 00:14:08,360 --> 00:14:17,530 ‫and we will use this callback along with our normal callback of safe best only model. 133 00:14:18,200 --> 00:14:25,280 ‫So in a list topping callback we have to provide the patients value new patients value is the number 134 00:14:25,280 --> 00:14:29,750 ‫of epochs after which our screening will be interrupted. 135 00:14:30,200 --> 00:14:41,360 ‫If there is no progress on the validation set score so suppose after 53 epochs we are not getting any 136 00:14:41,360 --> 00:14:50,820 ‫improvement on our evaluation site discourse then get us will stop the screening at 63 blocks. 137 00:14:51,020 --> 00:14:56,280 ‫So the difference between the 6 2 3 and 4 play 3 is then this is the patients. 138 00:14:56,370 --> 00:15:05,570 ‫This the number of epochs get us will wait for any improvement in validation is score so earlier you 139 00:15:05,570 --> 00:15:08,170 ‫saw value creating our functional model. 140 00:15:08,180 --> 00:15:14,900 ‫We have we have to look at our valuation is score graph to check out whether our model is converging 141 00:15:14,900 --> 00:15:16,020 ‫or not. 142 00:15:16,070 --> 00:15:18,410 ‫Now we don't have to worry about that. 143 00:15:18,500 --> 00:15:27,380 ‫We can use a list stopping along with other callbacks which will automatically save the best model that 144 00:15:27,380 --> 00:15:35,600 ‫is available and it will also train our model for optimal amount of time needed to create that model 145 00:15:35,630 --> 00:15:37,920 ‫good to its best accuracy score. 146 00:15:39,740 --> 00:15:42,830 ‫So let's just run this. 147 00:15:42,860 --> 00:15:49,550 ‫We are first creating this structure now along with barely stopping callback. 148 00:15:49,550 --> 00:15:53,020 ‫We have to use the normal callbacks also to save the model. 149 00:15:54,380 --> 00:16:00,330 ‫And here we are also using saved best on the barometer as well. 150 00:16:01,760 --> 00:16:09,020 ‫So at the end of all this training we wouldn't be getting on the a single model file on which we would 151 00:16:09,020 --> 00:16:13,650 ‫be getting the best validation service score. 152 00:16:13,670 --> 00:16:17,480 ‫Now our second callback is a list stopping callback. 153 00:16:17,480 --> 00:16:27,470 ‫Here you have to provide patients parameter for the number of e-books run this. 154 00:16:27,710 --> 00:16:29,210 ‫Now we are preparing our more. 155 00:16:29,300 --> 00:16:34,040 ‫We will be passing creating dataset epochs values. 156 00:16:34,040 --> 00:16:42,940 ‫Remember to increase epoch values then the validation data and then callbacks. 157 00:16:43,070 --> 00:16:46,260 ‫Again we are passing callbacks in the form of lists. 158 00:16:46,310 --> 00:16:52,840 ‫We are first passing over a checkpoint callback and then we are passing over our list stopping callback. 159 00:16:52,980 --> 00:16:53,870 ‫Just run this 160 00:17:21,250 --> 00:17:32,630 ‫so their training has stopped after 87 epoch This means that there is no need to train it till 200 epoch 161 00:17:32,700 --> 00:17:34,820 ‫values. 162 00:17:34,950 --> 00:17:41,970 ‫Our model has already converged and there is no way to further decrease the validation loss. 163 00:17:41,970 --> 00:17:51,270 ‫You can see the best value we are getting is that 70 70 book and it waited for 10 more epochs to get 164 00:17:51,710 --> 00:18:01,230 ‫the better value then 0 1 3 2 and you can see after 10 in bulk we lose the value is still hovering over 165 00:18:01,350 --> 00:18:02,810 ‫zero point three three. 166 00:18:02,830 --> 00:18:11,120 ‫That's why it stopped at 80 70 pop value let's just look at our directory. 167 00:18:11,330 --> 00:18:18,440 ‫We should have a model with file name on these top model yet. 168 00:18:18,500 --> 00:18:24,030 ‫You can see us has already saved the best model. 169 00:18:24,770 --> 00:18:28,280 ‫Here with the name Ollie's Top Model. 170 00:18:28,280 --> 00:18:30,020 ‫So now the training has a stop. 171 00:18:30,080 --> 00:18:37,460 ‫We can use this a list top model to predict and evaluate our future values. 172 00:18:37,550 --> 00:18:46,700 ‫You can see the loss on tests that we are getting is 0 1 2 3 1 as compared to our previous lost values 173 00:18:46,760 --> 00:18:49,880 ‫of zero point 4 3 7 8 and so on. 174 00:18:51,830 --> 00:18:59,120 ‫So using only stop callback is a very good way to optimize performance of your model. 175 00:18:59,150 --> 00:19:03,970 ‫You don't have to worry about the number of epochs. 176 00:19:05,330 --> 00:19:09,650 ‫So let's have a quick recap of what we learned in this lecture. 177 00:19:09,660 --> 00:19:18,980 ‫First we learn how to save and load our more than then we use callbacks to save our model. 178 00:19:19,100 --> 00:19:27,950 ‫At EDC POC value then we learned about the parameter that is saved best only which will save the model 179 00:19:28,070 --> 00:19:36,860 ‫for which we are getting the best accuracy score or lowest validation loss and after that we saw how 180 00:19:36,860 --> 00:19:46,830 ‫to use only a stop callback to terminate the training if no progress on validation loss is occurring. 181 00:19:46,970 --> 00:19:48,890 ‫So that's all for this lecture. 182 00:19:48,890 --> 00:19:49,310 ‫Thank you.