1 00:00:00,780 --> 00:00:07,960 ‫In the last lecture, we saw how to create, how to compile and how to train our classification model 2 00:00:08,220 --> 00:00:09,780 ‫In tensorflow keras 3 00:00:11,590 --> 00:00:17,170 ‫In this video, we will build and train a regression model using keras 4 00:00:18,860 --> 00:00:26,510 ‫For this, we will be using the very popular regression dataset that is California housing data set. 5 00:00:28,560 --> 00:00:33,300 ‫This dataset is available in a sklearn database library. 6 00:00:35,140 --> 00:00:42,460 ‫The objective here is to predict the prices of house using eight different independent variables. 7 00:00:43,540 --> 00:00:44,980 ‫So let's get started. 8 00:00:45,790 --> 00:00:52,080 ‫First, we are importing some basic liabilities, such as numpy, pandas and matplotlib. 9 00:00:54,920 --> 00:00:57,770 ‫Then we are importing tensorflow and keras 10 00:01:00,250 --> 00:01:08,050 ‫And then since this data is available in sklearn dataset, we are also importing California housing 11 00:01:08,230 --> 00:01:09,570 ‫from sklearn dataset. 12 00:01:10,930 --> 00:01:15,760 ‫And we are saving this database into an another variable called housing. 13 00:01:18,090 --> 00:01:23,670 ‫I also want to share one small shortcut to access the help of any function. 14 00:01:23,910 --> 00:01:32,310 ‫So if you just click inbetween of any function parenthesis and then hold the shift key and click on tab. 15 00:01:32,670 --> 00:01:39,900 ‫So if you hit shift plus tab , it will open the help or documentation of that function. 16 00:01:41,260 --> 00:01:46,530 ‫If you hit shift and tab two times, it will expand the documentation. 17 00:01:48,050 --> 00:01:50,990 ‫You can see here this function will return. 18 00:01:51,080 --> 00:01:54,380 ‫Different parameters such as dot data. 19 00:01:54,780 --> 00:01:57,260 ‫It will give us the independent variables. 20 00:01:57,490 --> 00:02:00,600 ‫Dot target will give us the dependent variable. 21 00:02:01,760 --> 00:02:05,270 ‫And the feature name will give us the details of features. 22 00:02:07,130 --> 00:02:08,810 ‫Let's just close this. 23 00:02:09,590 --> 00:02:16,850 ‫So if you have any doubt regarding any function, just click between parentheses and hit shift plus tab 24 00:02:19,410 --> 00:02:24,330 ‫So I have already listed some of the information about this database. 25 00:02:25,920 --> 00:02:29,840 ‫So in this database, there are around 20000 records. 26 00:02:30,780 --> 00:02:33,240 ‫There are eight independent variables. 27 00:02:34,230 --> 00:02:37,930 ‫We have first variable as med inc 28 00:02:38,730 --> 00:02:41,910 ‫This is the medium income in that particular block. 29 00:02:41,970 --> 00:02:43,090 ‫Warehouse is located. 30 00:02:44,100 --> 00:02:46,940 ‫Then we have a second variable that is house age. 31 00:02:47,430 --> 00:02:50,550 ‫This is the median house age in that block. 32 00:02:51,570 --> 00:02:55,560 ‫Then we have average rooms, which is the average number of rooms. 33 00:02:56,010 --> 00:03:01,050 ‫And then we have average bedrooms for average number of bedrooms. 34 00:03:01,710 --> 00:03:03,660 ‫Next, we have population variable. 35 00:03:04,080 --> 00:03:05,700 ‫That is the block population. 36 00:03:07,680 --> 00:03:10,260 ‫Then we have average occupancy. 37 00:03:10,470 --> 00:03:12,650 ‫That is the average house occupancy. 38 00:03:13,260 --> 00:03:21,090 ‫And then latitude and longitude of that house block using this eight independent variables. 39 00:03:21,510 --> 00:03:24,540 ‫We want to predict the value of the house. 40 00:03:25,020 --> 00:03:27,810 ‫The values are in hundred thousand. 41 00:03:28,080 --> 00:03:30,660 ‫So suppose if our y variable is 5 42 00:03:30,720 --> 00:03:32,850 ‫That means the value of that house is 43 00:03:33,360 --> 00:03:34,980 ‫Five hundred thousand dollars. 44 00:03:37,370 --> 00:03:38,960 ‫So this is our dataset. 45 00:03:39,440 --> 00:03:44,150 ‫We have this eight independent variable and one target variable as price 46 00:03:45,650 --> 00:03:50,960 ‫If you want some more detail about this dataset, you can click on this documentation link. 47 00:03:52,700 --> 00:03:57,550 ‫This will open the official sklearn documentation of this dataset. Here 48 00:03:58,090 --> 00:04:01,220 ‫You will get to know about all the parameters that you can give. 49 00:04:02,040 --> 00:04:05,360 ‫And what are included in this database? 50 00:04:06,620 --> 00:04:07,910 ‫Let's just go back. 51 00:04:10,990 --> 00:04:15,160 ‫So this housing is in the form of a dictionary. 52 00:04:17,650 --> 00:04:20,950 ‫We have one key value pair as feature name 53 00:04:21,860 --> 00:04:25,100 ‫So just look at the feature names first. 54 00:04:26,680 --> 00:04:31,120 ‫You can see these are the eight variable names that we have discussed already. 55 00:04:33,720 --> 00:04:41,950 ‫Now to access, the independent data we have to use housing.data and to access the independent data set. 56 00:04:42,330 --> 00:04:44,540 ‫We have to use housing dor target. 57 00:04:47,430 --> 00:04:56,190 ‫So in this line of code, we are splitting our data first into train full and test dataset. 58 00:04:57,450 --> 00:05:04,890 ‫Then we add further dividing this train full dataset inti x train and X validation dataset. 59 00:05:08,200 --> 00:05:16,240 ‫We will be using test train split from sklearn model selection, and then we will use test train, 60 00:05:16,330 --> 00:05:19,240 ‫the split method to divide our data. 61 00:05:20,860 --> 00:05:24,760 ‫We are not giving any additional parameter for test size. 62 00:05:25,330 --> 00:05:31,080 ‫That's because by default, the test size is 25 percent of the total data. 63 00:05:32,200 --> 00:05:40,120 ‫So 25 percent of the total data that is around 20000 rows will go into test set. 64 00:05:40,630 --> 00:05:45,780 ‫And then the remaining 75 percent will go into training set 65 00:05:47,160 --> 00:05:49,320 ‫All of that 75 percent. 66 00:05:49,920 --> 00:05:56,890 ‫Again, 25 percent will go into validation set and rest of 75 percent will go into our training set. 67 00:05:58,270 --> 00:05:59,800 ‫Let's run this. 68 00:06:02,700 --> 00:06:11,430 ‫Next step is to process our data and we will be using the standard scalar from sklearn to standardize 69 00:06:11,430 --> 00:06:11,930 ‫our data. 70 00:06:14,180 --> 00:06:20,490 ‫In standardizing, we subtract the mean of each variable from their individual values. 71 00:06:21,020 --> 00:06:30,080 ‫And then we also divide it by the variance because at the end we want all the variables with mean as 72 00:06:30,080 --> 00:06:33,080 ‫zero and their variance as one 1 73 00:06:34,340 --> 00:06:42,230 ‫This is a standard procedure to create any machine learning model the steps here are very simple. 74 00:06:42,530 --> 00:06:47,270 ‫First, we are importing standard scalar from sklearn pre processing. 75 00:06:48,320 --> 00:06:53,900 ‫Then we are creating the scalar object using standard scalar method. 76 00:06:55,150 --> 00:06:56,780 ‫Then we are training. 77 00:06:56,780 --> 00:07:00,140 ‫This scalar Object using our x train data 78 00:07:01,920 --> 00:07:09,230 ‫So on our x train data, this scalar will find the values to subtract, as a mean, and to divide 79 00:07:09,230 --> 00:07:10,020 ‫as a variance. 80 00:07:10,620 --> 00:07:12,480 ‫Then we will use those values. 81 00:07:12,660 --> 00:07:18,120 ‫Or this is scalar object to standardize our validation and test sets as well. 82 00:07:19,550 --> 00:07:22,200 ‫Just so repeat, we are fitting. 83 00:07:22,260 --> 00:07:24,780 ‫This is scalar object on our training data. 84 00:07:25,320 --> 00:07:29,070 ‫And we are transforming our validation and test set using. 85 00:07:29,070 --> 00:07:33,150 ‫This is scalar object that we have fitted it on our x train data. 86 00:07:35,840 --> 00:07:40,190 ‫To fit will be using fit underscore transform method. 87 00:07:40,440 --> 00:07:44,700 ‫And we will be using x train as parameter to transform. 88 00:07:44,760 --> 00:07:51,990 ‫We will be using dort transform method of this scalar object and we will be using relevant datasets 89 00:07:51,990 --> 00:07:52,220 ‫here. 90 00:07:55,720 --> 00:07:59,210 ‫So let's create a standardized datasets. 91 00:08:00,430 --> 00:08:03,400 ‫We are saving this object in their original name only. 92 00:08:03,760 --> 00:08:08,110 ‫So we are replacing the ordinal x train with the standardized version of x train. 93 00:08:09,280 --> 00:08:14,370 ‫Origional x validation set into standardized version of X validation set 94 00:08:14,470 --> 00:08:17,420 ‫And same for test set as well. Let's just. 95 00:08:17,830 --> 00:08:22,340 ‫Run this if you want some more information on this scaling. 96 00:08:22,690 --> 00:08:25,900 ‫You can always refer to sklearn documentation 97 00:08:26,100 --> 00:08:27,640 ‫Abiut standard scalars. 98 00:08:29,380 --> 00:08:31,990 ‫Next step is to set random seeds. 99 00:08:33,830 --> 00:08:37,760 ‫This is to generate the same result every time we run this modern. 100 00:08:42,220 --> 00:08:49,630 ‫Now, as I said earlier, our initial data set was of around 20000 rows ir records 101 00:08:50,440 --> 00:08:55,060 ‫Now let us see the shape of our x train dataset. 102 00:08:57,560 --> 00:09:04,580 ‫Here you can see we have eight columns and around eleven thousand six hundred records. 103 00:09:05,480 --> 00:09:05,960 ‫In our x train dataset 104 00:09:06,020 --> 00:09:06,980 ‫. 105 00:09:08,950 --> 00:09:17,710 ‫We should around five thousand records in our X test dataset and around 4000 in valuation set. 106 00:09:20,820 --> 00:09:26,220 ‫Now, let's create the structure for our regression neural network. 107 00:09:28,890 --> 00:09:31,590 ‫Here we will be first having an input layer. 108 00:09:32,960 --> 00:09:37,520 ‫Then we will be having the first dense layer with 30 neurons. 109 00:09:38,180 --> 00:09:42,920 ‫Then we want to create second dense layer with another 30 neurons. 110 00:09:43,760 --> 00:09:51,620 ‫And then since this is a regression problem, we will be having a single output neuron without any 111 00:09:51,620 --> 00:09:52,730 ‫activation function. 112 00:09:54,620 --> 00:09:59,840 ‫A single neuron, since we want a continuous value as our output. 113 00:10:01,370 --> 00:10:04,060 ‫Again, we will be using the sequential api. 114 00:10:05,060 --> 00:10:10,040 ‫We are saving this structure or our model as model. 115 00:10:12,550 --> 00:10:14,800 ‫And then for the first layer 116 00:10:16,850 --> 00:10:17,630 ‫We are writing. 117 00:10:17,690 --> 00:10:19,730 ‫Keras dot layers, dot dense. 118 00:10:20,360 --> 00:10:25,950 ‫Here in parenthesis, we have to provide the number of neurons, which is thirty. 119 00:10:26,280 --> 00:10:31,710 ‫Then, as discussed in our three lectures, we will be using activation function as RELU. 120 00:10:32,450 --> 00:10:40,010 ‫And then since this is our first written layer, we need to provide the input shape since the number 121 00:10:40,010 --> 00:10:43,630 ‫of independent variables in our data is eight. 122 00:10:44,400 --> 00:10:47,240 ‫We will be using input shape equal to 8 123 00:10:52,130 --> 00:10:54,860 ‫You can also use like this input shape. 124 00:10:55,220 --> 00:11:02,680 ‫Equal to x train dot shape and then calling the second and so on, elements of over input shape. 125 00:11:03,860 --> 00:11:09,140 ‫This way you don't have to worry about changing this number every time you change your data base. 126 00:11:09,650 --> 00:11:17,870 ‫You can just write x train dot shape and it will automatically get the number of variables from the 127 00:11:17,870 --> 00:11:20,900 ‫shape attribute of our x train object. 128 00:11:23,680 --> 00:11:29,050 ‫So this is the structure of our first dense layer will be create 129 00:11:29,140 --> 00:11:31,330 ‫Second, dense layer in a similar fashion. 130 00:11:31,780 --> 00:11:34,950 ‫We will be using kreas dot layer dot dense. 131 00:11:35,380 --> 00:11:39,580 ‫And then the number of neurons in the parenthesis, which is 30. 132 00:11:40,030 --> 00:11:42,300 ‫And activation function is relu. 133 00:11:44,070 --> 00:11:49,700 ‫Similarly, for the output layer, we will be using keras, dot layers dense. 134 00:11:50,130 --> 00:11:56,550 ‫And since this is a regression problem, we will be using a single neuron without any activation function. 135 00:11:58,670 --> 00:11:59,570 ‫Just run this. 136 00:12:01,580 --> 00:12:08,000 ‫And again, one important thing you can comment using hash symbol inside the cells. 137 00:12:09,560 --> 00:12:16,980 ‫So python will execute only this part of the code and will not be executing any code which starts with 138 00:12:16,980 --> 00:12:20,050 ‫Hash, hash represent for starting the comment. 139 00:12:21,670 --> 00:12:30,700 ‫So now we have created the structure or architecture of our neural network. Now just to conform and view 140 00:12:30,700 --> 00:12:31,310 ‫The structure. 141 00:12:31,670 --> 00:12:35,860 ‫We can call dot summary method just write model dot 142 00:12:40,890 --> 00:12:47,490 ‫Summary, here you will get the information about the structure that we have created. 143 00:12:47,640 --> 00:12:50,280 ‫So we have first dense layer with 30 neuron 144 00:12:51,330 --> 00:12:53,460 ‫We have second dense, layered with 30 neurons. 145 00:12:53,940 --> 00:12:58,350 ‫And lastly, we have a single output layer with one neuron. 146 00:13:00,450 --> 00:13:02,030 ‫This is what we wanted. 147 00:13:03,180 --> 00:13:06,680 ‫The next step should be to compile this model. 148 00:13:09,280 --> 00:13:14,830 ‫Again, the compile method works similar for both classification and regression model. 149 00:13:15,970 --> 00:13:19,900 ‫First, we have to mention the loss in classification. 150 00:13:19,930 --> 00:13:21,380 ‫We were using Cross entropy 151 00:13:22,360 --> 00:13:29,970 ‫But here, since we are running regression, we have to use mean squared error, also known as mse 152 00:13:31,840 --> 00:13:34,140 ‫The second parameter is optimizer. 153 00:13:36,010 --> 00:13:41,200 ‫Again, here also we are using as sgd, socastic gradient descent. 154 00:13:42,370 --> 00:13:49,870 ‫And here we are also providing the learning rate by default, the value of learning rated zero point 155 00:13:49,870 --> 00:13:52,740 ‫zero one and to change 156 00:13:53,740 --> 00:13:54,850 ‫You can just write. 157 00:13:56,150 --> 00:13:58,330 ‫The new value in the parentheses 158 00:14:00,540 --> 00:14:04,090 ‫We have already discussed what is learning rate in our theory lecture. 159 00:14:05,080 --> 00:14:08,330 ‫So if you have any doubts, just revisit that lecture. 160 00:14:09,940 --> 00:14:13,270 ‫And then the next parameter that we are passing is metrics. 161 00:14:13,600 --> 00:14:15,250 ‫This is an optional parameter. 162 00:14:17,490 --> 00:14:20,060 ‫In classification, we were using accuracy. 163 00:14:21,080 --> 00:14:31,490 ‫But in regression, we can use mean absolute error or mae absolute error is the difference between 164 00:14:31,490 --> 00:14:34,040 ‫the predicted value and the actual value. 165 00:14:35,060 --> 00:14:39,960 ‫Whereas the mean is squared error is the square of that difference. 166 00:14:41,390 --> 00:14:49,160 ‫So we are calculating both mean squared error as our function and nae as the metrics we additionally 167 00:14:49,160 --> 00:14:50,060 ‫want to calculate. 168 00:14:51,480 --> 00:14:59,370 ‫Again, just remember, if you want to look at the documentation or help, just click inside any of 169 00:14:59,370 --> 00:15:00,840 ‫the parenthesis and. 170 00:15:01,940 --> 00:15:07,550 ‫And press shift plus tab, you will get this kind of documentation. 171 00:15:07,880 --> 00:15:12,850 ‫And here you can see that by default the learning rate value is 0.01. 172 00:15:13,970 --> 00:15:17,420 ‫So in classification, we did not provided any learning rate. 173 00:15:17,750 --> 00:15:22,530 ‫So the learning rate that was used, there was zero point zero one. 174 00:15:24,260 --> 00:15:27,430 ‫But you can always change these values according to your need. 175 00:15:29,450 --> 00:15:30,550 ‫Let's run this. 176 00:15:31,360 --> 00:15:33,140 ‫So we have compiled our model. 177 00:15:35,640 --> 00:15:40,740 ‫The next step, the next step is to train our model using training data. 178 00:15:42,240 --> 00:15:44,340 ‫The method or the process is same. 179 00:15:44,940 --> 00:15:48,760 ‫We are creating an another object model underscoring history for training. 180 00:15:49,530 --> 00:15:57,930 ‫Then we are using model dot fit and we are passing our training dataset number of epochs and the validation 181 00:15:57,980 --> 00:15:59,730 ‫dataset that we have created. 182 00:16:00,890 --> 00:16:02,950 ‫Just run this statement. 183 00:16:05,860 --> 00:16:06,250 ‫Again. 184 00:16:08,740 --> 00:16:14,980 ‫Just like the classification model, you will get the lost value, which is the mean squared error. 185 00:16:15,910 --> 00:16:20,380 ‫You will get the mae value mean absolute terror. 186 00:16:21,340 --> 00:16:25,960 ‫And similarly, you will get these two values for your validation set as well 187 00:16:26,000 --> 00:16:26,380 ‫. 188 00:16:30,070 --> 00:16:37,150 ‫And you can see that the loss on both training set and validation set is decreasing with each epoch. 189 00:16:39,550 --> 00:16:43,900 ‫Now we have these values for our training and validation set. 190 00:16:44,680 --> 00:16:49,600 ‫We can also evaluate performance of this train model on our test set. 191 00:16:50,860 --> 00:16:55,120 ‫And we are going to use the same method as fitted with classification model. 192 00:16:55,540 --> 00:16:58,140 ‫We'll call model DOT evaluate. 193 00:16:58,450 --> 00:17:00,860 ‫And then we will pass our training test. 194 00:17:01,860 --> 00:17:02,500 ‫Run this 195 00:17:04,990 --> 00:17:10,180 ‫You can see on our training dataset loss is zero point three. 196 00:17:10,420 --> 00:17:19,870 ‫That is mse or meann squared error and mae mean absolute error is zero point four four nine three. 197 00:17:22,420 --> 00:17:29,200 ‫Now, just like in classification, we can call model history dot history, that will give us the 198 00:17:29,320 --> 00:17:32,860 ‫values of all this metrices in the form of dictionary. 199 00:17:33,800 --> 00:17:40,210 ‫Run this, here you will get the loss and mae on training dataset. 200 00:17:40,300 --> 00:17:40,990 ‫. 201 00:17:41,130 --> 00:17:43,200 ‫And validation loss validation mae. 202 00:17:43,890 --> 00:17:53,410 ‫The beauty of this is we can plot this dictionary on a plot just like we did for classification, and 203 00:17:53,410 --> 00:17:55,630 ‫that will show us how we're training. 204 00:17:55,630 --> 00:18:03,160 ‫Loss and validation loss are changing with each epoch and whether we have achieved the convergence or 205 00:18:03,160 --> 00:18:03,540 ‫not. 206 00:18:04,770 --> 00:18:05,830 ‫Let's run this. 207 00:18:07,970 --> 00:18:14,480 ‫So you can see we have the loss values and the mae values for our training and validation 208 00:18:14,480 --> 00:18:16,100 ‫set plotted on this graph. 209 00:18:16,880 --> 00:18:24,920 ‫And one thing to notice is this graph is still going down, meaning that if we run some more epochs, 210 00:18:25,700 --> 00:18:31,190 ‫this will further decrease the losses and improve the accuracy of our model. 211 00:18:33,220 --> 00:18:40,140 ‫So this is the one way to tell whether you have achieved convergence or not or whether you have to increase 212 00:18:40,140 --> 00:18:41,650 ‫your epoch value or not. 213 00:18:42,700 --> 00:18:46,440 ‫You have to look at this validation, loss and validation mae value. 214 00:18:47,620 --> 00:18:49,090 ‫So this the validation loss. 215 00:18:49,420 --> 00:18:53,410 ‫And you can clearly see that it is going down. 216 00:18:55,490 --> 00:18:59,630 ‫So to improve the accuracy, we can re run this code to 217 00:19:02,060 --> 00:19:04,210 ‫Run it for 20 more epochs. 218 00:19:09,140 --> 00:19:11,240 ‫So just go there. 219 00:19:13,310 --> 00:19:20,780 ‫Now, one important thing about keras is, keras have the weights and Bias's value in the memory. 220 00:19:21,020 --> 00:19:27,850 ‫So if you just re run this whole this statement, again, this will not train the data from start. 221 00:19:28,190 --> 00:19:32,720 ‫But it will start training the data from this position. 222 00:19:34,790 --> 00:19:40,960 ‫So if we run this statement two times that is similar to running a statement with 40 epochs. 223 00:19:42,660 --> 00:19:45,420 ‫If we just re run it one more time. 224 00:19:48,020 --> 00:19:50,450 ‫You can see earlier the lost values were. 225 00:19:51,600 --> 00:19:55,160 ‫Around point seven or point eight for the first epoch 226 00:19:55,660 --> 00:19:57,730 ‫And then gradually decreasing. 227 00:19:58,240 --> 00:20:02,170 ‫But now we have started from the 21st epoch. 228 00:20:07,620 --> 00:20:11,190 ‫Last time, the lost value on our test set was zero point three zero. 229 00:20:11,790 --> 00:20:16,020 ‫Let's see whether we have improved this lost value or not. 230 00:20:18,000 --> 00:20:23,310 ‫You can see the loss is decrease from zero point three to zero point two five. 231 00:20:25,230 --> 00:20:32,260 ‫So our hypothesis was correct that the model was not converged in 20 epoch. 232 00:20:33,000 --> 00:20:38,360 ‫There was room of improvement and B, B, then the whole more than 420 more epochs. 233 00:20:38,460 --> 00:20:40,370 ‫That is a total of 40 epochs. 234 00:20:43,630 --> 00:20:45,300 ‫And we can see this graph. 235 00:20:46,850 --> 00:20:48,160 ‫You can just. 236 00:20:49,050 --> 00:20:51,240 ‫Focus on this validation loss line. 237 00:20:51,870 --> 00:20:56,910 ‫Earlier, it was 0.4 around, re run this. 238 00:20:59,300 --> 00:21:02,780 ‫There is a slight decrease in validation loss. 239 00:21:03,020 --> 00:21:06,320 ‫Now you can see that the line is flattened out. 240 00:21:06,890 --> 00:21:11,060 ‫This means we have achieved the convergence on this model. 241 00:21:12,440 --> 00:21:14,360 ‫So not just with regression. 242 00:21:14,780 --> 00:21:16,880 ‫If you are running classification model as well. 243 00:21:18,050 --> 00:21:22,640 ‫Just look at this graph to identify whether you have achieved convergence or not. 244 00:21:24,250 --> 00:21:30,740 ‫Now, to predict the values on the new dataset, you can always use, dot predict method. 245 00:21:31,400 --> 00:21:34,310 ‫So your object name and dot predict method. 246 00:21:34,760 --> 00:21:36,330 ‫And then the new dataset. 247 00:21:37,310 --> 00:21:38,830 ‫I don't have any new dataset. 248 00:21:38,960 --> 00:21:44,000 ‫So I'm just creating the sample of first three values of my X dataset. 249 00:21:44,120 --> 00:21:53,420 ‫And considering it as my new dataset and then saving the information in Y predicted values and using 250 00:21:53,520 --> 00:21:54,070 ‫model dot 251 00:21:54,080 --> 00:21:59,870 ‫Predict method to predict the values this are the predicted values using this model. 252 00:22:01,760 --> 00:22:04,580 ‫That's all for this lecture. In the next lecture. 253 00:22:04,640 --> 00:22:08,820 ‫We will be looking at the functional api of keras. 254 00:22:09,830 --> 00:22:10,220 ‫Thank you.