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‫In the last lecture we have created the structure for our monthly parts of from more than now before

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‫dreaming this model we need to set up the learning processes and to do that.

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‫We will use the combined method we will first give the lost function.

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‫Then we will give the optimizer and then the metrics we want to calculate to judge the performance of

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‫our model.

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‫We are using lost function as sparse categorical cross and copy.

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‫We are using this because our y data is available in the form of labels in our data.

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‫We have specific labels for ten different items and that's why we are using this as sparse categorical

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‫cross and copy.

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‫If instead we had probabilities for a class in our Y variable then we had to use categorical cross entropy.

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‫But since we have labels we are using sparse categorical cross and copy and suppose we had binary labels

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‫such as Yes or no or true or false.

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‫In that case we had to use binary cross and copy you can get details of all these lost functions in

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‫the official cross documentation.

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‫I have provided the link of that documentation so if you open it you will get details of all the parameters

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‫that this compiling method can take.

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‫You can look at all other optimizations and lost function and metrics in the following documentations

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‫then for optimize it we are using as duty as duties simply stands for sarcastic clearly and designed.

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‫In other words we are just telling us to perform back propagation algorithm and for metrics we are using

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‫accuracy since we are building a classifier.

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‫We have to use accuracy if you are using the regression model.

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‫You can use mean squared.

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‫Edit and so on.

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‫So basically we have to provide this information before fitting or training data.

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‫So just on this Come on you are giving three parameters.

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‫Combining

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‫snow we have compiled our modern Lexus step this to fit extremely inviting data in this model.

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‫This does an index of fitting the model we are calling dot fake matter.

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‫And then we are providing extreme right train the number of epochs.

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‫I hope you remember what epochs are.

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‫We have discussed it in our two re lectures and by default the epochs value is set to 1.

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‫So if you've blown mentioned epoch by default the value is 1 and then since we have validation data

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‫as well so we are providing X valid and Y valid datasets that we have created in our previous lectures.

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‫We are storing this object in another object which we are calling your model history.

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‫So let's run this.

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‫You can see at each epoch during the training chaos displayed a number of instances.

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‫Process so far

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‫you can see there is a progress bar and we are getting information of each epochs and then we are also

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‫getting the loss accuracy validation loss and validation accuracy during each epoch

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‫so it will take some time depending on your system configurations.

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‫So I'm just fast forwarding this

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‫now the training is complete.

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‫You can see that the loss on our training is zero point zero.

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‫Accuracy zero point nine seven for our validation site the loss is zero point three day and accuracy

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‫is zero point eighty eight.

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‫So if you just converted the first epoch value the accuracy on what the regulations say during our first

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‫epoch was that upon date night

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‫now you can see what validation accuracy is oscillating our training accuracy so during the first epoch

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‫the accuracy score was point 9 5 2 and after the last book the accuracy score is zero point nine seven.

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‫So in each epoch the training accuracy is increasingly developing.

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‫So now we have trained no data there are few more parameters that are available with fit my code.

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‫One important parameter is glass weights.

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‫So if you have some uneven distribution of your classes in your Y variable so suppose all of over sixty

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‫thousand records fifty thousand would be shirts and dress of nine categories are spread across the remaining

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‫ten thousand records.

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‫Then we have to use glass suites to give larger weight to underrepresented classes and to give lower

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‫weights to or what we presented classes since in our dataset.

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‫The categories are uniformly spread and there is no uneven distribution of categories.

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‫That's why we are not using class fruits.

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‫But if in your example there is some underrepresentation of some specific classes then you have to use

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‫class weights after fitting your more than you can call different attributes of a modern history object

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‫so you can call parameters.

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‫This will give you information of all the parameters that we have used in creating this model.

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‫We have another parameter that is dot epoch that will give you details of each epoch and the most important

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‫attribute is history.

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‫So a few write your object name and then write dot history.

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‫This will give you all the laws accuracy regulation loss and validation accuracy in the form of dictionary.

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‫So this is the last value on our training set for the Today box.

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‫Then we have the accuracy value on our training set for the Today box.

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‫Then we have the validation loss of today box.

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‫And lastly the validation accuracy for today box so all the information which you were getting while

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‫in training your data.

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‫You can also access that information by using street attribute.

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‫You can also block this information to visualize how our accuracy escorts are changing with each epoch.

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‫So here I am just plotting model history not history.

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‫The information that we have here and then we won the grades in our plot and then we want our y axis

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‫to be which means we don't learn to plot this you will get a graph of the screen on top.

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‫We have an orange line of training accuracy.

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‫Then we have a direct line of validation accuracy.

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‫Then we have a green line of validation loss and then a blue line of training loss.

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‫If you can see with each epoch the training accuracy and the validation accuracy is increasing and the

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‫loss is decreasing.

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‫You can also tell that the model has not converged yet as the validation that accuracy is still going

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‫up and the validation loss is still going low.

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‫So for our next strike we should run it for some more epochs.

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‫And if you call the fit my turn again.

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‫Kira's will continue to train this model where you left off.

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‫So that's why a few just this.

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‫Again the Kira will create this model for today more epochs and you will get graph from here.

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‫So try running it for 30 more epochs in the next will you we will learn how to predict values using

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‫this model.

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‫Thank you.