‫In the last lecture we have created the structure for our monthly parts of from more than now before
‫dreaming this model we need to set up the learning processes and to do that.
‫We will use the combined method we will first give the lost function.
‫Then we will give the optimizer and then the metrics we want to calculate to judge the performance of
‫our model.
‫We are using lost function as sparse categorical cross and copy.
‫We are using this because our y data is available in the form of labels in our data.
‫We have specific labels for ten different items and that's why we are using this as sparse categorical
‫cross and copy.
‫If instead we had probabilities for a class in our Y variable then we had to use categorical cross entropy.
‫But since we have labels we are using sparse categorical cross and copy and suppose we had binary labels
‫such as Yes or no or true or false.
‫In that case we had to use binary cross and copy you can get details of all these lost functions in
‫the official cross documentation.
‫I have provided the link of that documentation so if you open it you will get details of all the parameters
‫that this compiling method can take.
‫You can look at all other optimizations and lost function and metrics in the following documentations
‫then for optimize it we are using as duty as duties simply stands for sarcastic clearly and designed.
‫In other words we are just telling us to perform back propagation algorithm and for metrics we are using
‫accuracy since we are building a classifier.
‫We have to use accuracy if you are using the regression model.
‫You can use mean squared.
‫Edit and so on.
‫So basically we have to provide this information before fitting or training data.
‫So just on this Come on you are giving three parameters.
‫Combining
‫snow we have compiled our modern Lexus step this to fit extremely inviting data in this model.
‫This does an index of fitting the model we are calling dot fake matter.
‫And then we are providing extreme right train the number of epochs.
‫I hope you remember what epochs are.
‫We have discussed it in our two re lectures and by default the epochs value is set to 1.
‫So if you've blown mentioned epoch by default the value is 1 and then since we have validation data
‫as well so we are providing X valid and Y valid datasets that we have created in our previous lectures.
‫We are storing this object in another object which we are calling your model history.
‫So let's run this.
‫You can see at each epoch during the training chaos displayed a number of instances.
‫Process so far
‫you can see there is a progress bar and we are getting information of each epochs and then we are also
‫getting the loss accuracy validation loss and validation accuracy during each epoch
‫so it will take some time depending on your system configurations.
‫So I'm just fast forwarding this
‫now the training is complete.
‫You can see that the loss on our training is zero point zero.
‫Accuracy zero point nine seven for our validation site the loss is zero point three day and accuracy
‫is zero point eighty eight.
‫So if you just converted the first epoch value the accuracy on what the regulations say during our first
‫epoch was that upon date night
‫now you can see what validation accuracy is oscillating our training accuracy so during the first epoch
‫the accuracy score was point 9 5 2 and after the last book the accuracy score is zero point nine seven.
‫So in each epoch the training accuracy is increasingly developing.
‫So now we have trained no data there are few more parameters that are available with fit my code.
‫One important parameter is glass weights.
‫So if you have some uneven distribution of your classes in your Y variable so suppose all of over sixty
‫thousand records fifty thousand would be shirts and dress of nine categories are spread across the remaining
‫ten thousand records.
‫Then we have to use glass suites to give larger weight to underrepresented classes and to give lower
‫weights to or what we presented classes since in our dataset.
‫The categories are uniformly spread and there is no uneven distribution of categories.
‫That's why we are not using class fruits.
‫But if in your example there is some underrepresentation of some specific classes then you have to use
‫class weights after fitting your more than you can call different attributes of a modern history object
‫so you can call parameters.
‫This will give you information of all the parameters that we have used in creating this model.
‫We have another parameter that is dot epoch that will give you details of each epoch and the most important
‫attribute is history.
‫So a few write your object name and then write dot history.
‫This will give you all the laws accuracy regulation loss and validation accuracy in the form of dictionary.
‫So this is the last value on our training set for the Today box.
‫Then we have the accuracy value on our training set for the Today box.
‫Then we have the validation loss of today box.
‫And lastly the validation accuracy for today box so all the information which you were getting while
‫in training your data.
‫You can also access that information by using street attribute.
‫You can also block this information to visualize how our accuracy escorts are changing with each epoch.
‫So here I am just plotting model history not history.
‫The information that we have here and then we won the grades in our plot and then we want our y axis
‫to be which means we don't learn to plot this you will get a graph of the screen on top.
‫We have an orange line of training accuracy.
‫Then we have a direct line of validation accuracy.
‫Then we have a green line of validation loss and then a blue line of training loss.
‫If you can see with each epoch the training accuracy and the validation accuracy is increasing and the
‫loss is decreasing.
‫You can also tell that the model has not converged yet as the validation that accuracy is still going
‫up and the validation loss is still going low.
‫So for our next strike we should run it for some more epochs.
‫And if you call the fit my turn again.
‫Kira's will continue to train this model where you left off.
‫So that's why a few just this.
‫Again the Kira will create this model for today more epochs and you will get graph from here.
‫So try running it for 30 more epochs in the next will you we will learn how to predict values using
‫this model.
‫Thank you.