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In this video, we are going to learn about how to save and restore our model.

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We have created several models in the past few lectures.

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Now, if you want to see the detail for model that can be saved in a separate file on our system.

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And whenever we want to retrieve or restore our model using that saved information, we can do it by

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reading that filing model information.

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Basically consists of three things.

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One is model architecture.

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Second model configuration.

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And thirdly, DVDs of the train model.

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So when we save a model, all this information will be stored in a file.

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The file has a format called its D.F. File or its file.

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Both of these things stand for the same thing.

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When you say, well, fine, you can save it with its DFI format, Dolto or HFA format.

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Also, then you save a model like this.

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This model will have the entire information of architecture, configuration and Digweed of this model.

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So in the last video, when we fitted our model using functional EPA, we then this line of code.

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And this model func is now a train model.

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If you want to store all the information that is in this model, you can run this save model and just

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put its DFI.

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Come on.

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And given name to the fight.

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This will be the name of the file with which it will be stored in your working directory.

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So if you go to these files that here you will see this Driton here.

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This is the working directory for your obsession.

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If you want to change the working directory, you just click here, select wherever you want to go,

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open that particular location.

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And then you go into the more options and click on Settings Working Directory.

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This will set that particular address as your working directory for our decision.

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So I have said this location as my working directory

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when I ran this line of code.

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It will create a new file title.

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My model dot.

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It's for you.

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And this has the information of my entire neural network.

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No, whenever I want to create a new model containing the same architecture and the same weight, I

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can load all the information from this file using the Lord underscore model underscored a deep faith

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function.

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So my new model will have the exactly same information that was saved in the model function.

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Also, if you look at the summary of dysfunction.

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It has input layer to the layers when concatenation layer and one output layer.

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Similarly, a new model also has input led to hidden layers.

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When concatenation layer and one output layer, if you check the performance of both of these model

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on new data, it will also be exactly same.

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Another thing we will cover here is use of callbacks we saw when we were putting our model.

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We gave a barometer called Epochs Epoch suggested the number of times our model went through the entire

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training dataset.

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Now, at the end of each epoch, we saw that our model has a set value of offbeat corresponding to which

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there is a particular value of accuracy and loss.

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Now, sometimes when we have a lot of epochs, our model overfit on the training data.

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So we may want to store the information at the end of all the epochs and Lawder model from in-between

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the e-books.

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So, for example, we can run the model for the P box and probably load the weight of the model from

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the 30th book or the 28 epoch.

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This can be achieved using callbacks.

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Let me show you how in these first few lines I am creating a directory where this file will be saved.

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So basically I am creating a new folder called Check Point in this working directory.

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So when I run this command, this has created a new variable called Checkpoint Day containing the name

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Checkpoint.

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And when I run directory create command, it will create a new folder in my working directly with the

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name Checkpoint.

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Now I am creating a variable called file.

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But this file but variable is containing the information of where the files will be stored and what

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will be the name of the fight.

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So file but function has two parameters.

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The first parameter is the location of the folder.

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We want to store defined and the second is the name of divides.

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Now, since we will be running the model for multiple epochs such as 30 or 50 bucks, each file should

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have a different name.

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Otherwise, the same file will be overwritten multiple times to have a different name for each file.

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We can use something like this.

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So the name of the file that I am suggesting is name will be Epoch Hyphen.

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And this part, which is in the curly bracket, is basically suggesting that it is a variable containing

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the number of epoch and the number should be in the two digit format.

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That is the first epoch will be the DNS epoch hyphen zero one.

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The second will be written as Epoch hyphen zero two and so on.

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Now, with the part variable ready, we create a new variable called S.P. Underscore Callback, which

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uses dysfunction callback model checkpoint.

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And it has one important parameter.

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This is mandatory that is defined but but should have both things.

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One is the directory and what is the name.

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So that information we have stored in file but variable already.

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So we run these lines of code.

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Also filed by Terry Mills, creator and S.P. Underscore Callback Wytheville is also created.

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Now before we create a new model on the same data, because the model information is stored in model

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func variable plus all the information is also stored in the data session that is running the background.

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We can clear that information.

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It will have two helpful impact.

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One is it will clear the memory that is being used in our system so our system will perform better.

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Secondly, when we train our new model, it will start afresh and it will not start with the weight

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that we have been previously trained with.

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So to clear the history, we removed the previously trained variable using the item function and we

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clear decision using gay underscore, clear and association function.

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This will clear all the background information that we have stored in the Get US library.

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Now we'll be training a new model.

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This model I'm calling as model underscore callback.

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First, we define the architecture, using it as underscored moral function, then we configured our

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model using the combined function.

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Then we train our model using different function.

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But when we are training our model, we have to include one new parameter.

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This barometer is called callbacks.

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And in this, we have to specify the callback variable, which contains the information of when that

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file is to be created and where it is to be created.

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So this checkpoint is for each epoch and at each epoch a file will be saved at this file.

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But when I run different function.

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At the end of each epoch, a new file is being saved.

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Let us go to the files tab, open the checkpoint and you can see that we have here 30 files corresponding

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to each book.

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Each of these files contains the information of the neural network.

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At the end of that particular epoch to at the end of First Epoch, our model was giving a loss of twenty

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three point forty five with a mean absolute error of three point forward, one nine debates.

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At this particular point are stored in the Epoch File one.

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You can also check the list of files stored in the territory using the list DOT files function, it

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will list down all defiance that you have in this territory.

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And if you want to load the lead in a particular model.

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So if I want to load debate in the 10th model into a new model that I'm calling as 10th model, only

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you can use load model as DFI function, as we saw earlier also.

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And we can specify the flight path where it is located.

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So the Epoch hyphen 10 file is located at the checkpoint barratry.

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And when we done this tent model variable is created.

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And it is a model containing the information of the 10th Epoch.

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So whatever the were at this particular point are now assigned to the Pentagon.

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You can see the summary of this 10th model, although the architecture will be automatically assigned

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as the same architecture that we define for the model on which we were fitting the training data to

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whatever architecture that model called back hard will be the same architecture assigned to any model.

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There is one important feature of this callback process that we can use, instead of saving all the

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30 files, we can save only one file, which has the best model.

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And how do we judge that, which is the best model?

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We can say that whichever model has the least validation laws saved the information of only that model

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and do not save the information of any other model.

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So when we are defining our backward evil using callback, just go model and record check point function,

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we can add two more parameters.

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Five out is mandatory first parameter.

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Do we add it is what values to be monitored.

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Since this is a regression problem, we'll be looking at validation loss.

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If it would have been a classification problem, we would be monitoring the accuracy.

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And the second parameter is still best only is equal to two.

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This means that only the model with best validation loss.

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That is the least validation loss will be saved in this file with the title best.

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And just got a model dot edge for you to recreate this variable first.

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Delete the previous models

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and start building our new model.

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We define the architecture, configure it and frame it.

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So it will again done for all Dieter D-Box.

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But whichever Epoch has the least validation loss which is shown by this green graph here.

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Only that will be seen.

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So if we go back to a working directory, here's our best underscored model dot HFA.

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This contains the information of the model with least validation loss.

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You may also notice here that instead of validation data, I'm just using best data.

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Since this dataset has only 506 observations out of which we have 102 observations at test data instead

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of creating a separate validation set.

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I'm just using deep test dataset as the validation dataset here.

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If you have larger data, I would recommend that you keep a small amount of data separately as a validation

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dataset, which you can use here to monitor the validation loss.

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So whichever is the best model is stored in this file.

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You can load all the information of this model and do your new best model variable.

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And here you have this model.

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You can use this model for predicting in the future.

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The last thing I'm going to discuss in this lecture is the feature of a release stopping in callbacks.

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It is a very important feature.

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The basic concept is instead of running the model for all the epochs, we can specify a large value

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for the epochs randy model and stop it.

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When we do not see much improvement in the accuracy.

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So basically, instead of running it for only 30, we tell that one in four hundred D-Box.

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But stop at that point of time when you stop seeing any improvement in a particular metric that we have

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specified.

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For example, here I'm creating that callback variable in that variable.

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I'm adding two parts.

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One part is the same model checkpoint part, which contains the name of defined in which the information

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will be stored.

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What parameter is to be monitored and whether to save only the best model or save all the models.

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The other part is callback, at least stopping.

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In this part, we tell what parameters to be monitored and how much patience do we have.

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That is, for how many epochs will we see whether there is an improvement or not.

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If there is no improvement for three consecutive epochs in the validation, lost parameter will top

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our model training.

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There are only two basically earlier when we ran our model.

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You can see here that the validation loss is not improving much on liddick training losses decreasing.

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But even that improvement is not significant.

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This is suggesting that we are just overfitting on our model to prevent this.

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What we're doing, we'll be using at least stopping of training our model.

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So we run this code.

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This has created our callbacks list variable.

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We believed the previous model.

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We cleared the gate gas station.

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Then we redefined the architecture of our new model and we configured it.

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And now when we are training our model, we specify a large number of epochs because we know that their

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training will automatically stop when there's no significant improvement in the validation loss.

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But I will do so again in the validation data set.

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I have given the test data set.

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Only callbacks is equal to callbacks list.

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We've done this.

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It starts trading, but it stops at the fifth epoch only because after the first epoch, there was some

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improvement in the validation loss from twenty became nineteen point five.

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But after that, in the next three consecutive epochs, deregulation laws did not decrease.

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So we stopped blaming our model.

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I did 50 Bocconi, too, even though we specified that we will run this model.

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Four hundred epochs.

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We were saved the processing pain and or draining all of our model because we used at least dumping

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this model in the fifth epoch is stored.

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And this best model of stopping or it's five, you can load that model using the load model is DFI function.

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Well, that's all in this lecture.

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We saw how to see all the information of a model into a separate file.

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The benefit of this is you can share this file with your colleagues, with your student or anyone in

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your office.

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And whenever someone loads a model from that file, that model will have the exact same architecture

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and debate that your model had.

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Then we saw the importance of using callbacks.

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Callbacks helped us save model at each epoch.

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Then we saw how to save.

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Then we saw how to use saved best only parameter to save only one file.

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Instead of saving separate files for each book.

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And lastly, we saw the use of at least orbing functionality so that we can prevent overfitting and

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excessive processing time to bring our model.

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Thanks.