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Now, the structure of our model is ready.

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We have to compile the model in compiling.

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We have to tell two things to the model.

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One is the optimizer.

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And the other is loss.

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The model optimize it for this problem is going to be as Didi's.

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Which stands for stochastic gradient descent.

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The lost function is sparse, categorical cross and droopy.

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This is as before.

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I have told you that if we have more than two classes, an observation can belong to only one class.

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So, for example, in our dataset there are ten classes of 10 fashion objects and observation can belong

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to only one of those things.

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It can be either short or good or any good.

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So sense of division can belong to only one class.

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We have to use sparse, categorical, coarse and droopy.

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The other options are listed here.

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So I'm compiling them all.

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We'll be monitoring the accuracy metrics since we are solving a classification problem.

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Now comes the last part that is training the model.

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We train the model using fit function, fit function takes in several parameters.

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The first two parameters are the training images and training labels using which the model is to be

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trained.

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So that is part train images and part train labels.

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Second parameter is number of epochs.

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This is the number of times the entire dataset will be fed into our model.

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I will suggest that we use callbacks and use only stopping callback instead of straightaway running

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this forward today box.

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But for now, I'm going to use epoch value of 30

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years at a bad table 64.

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So in stochastic gradient descent, the observations will go in as a batch of 60 for.

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And we have also listed the validation data so that we can see the validation accuracy on the train

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model also.

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So then I have this line of code to start training the model for today box.

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It may take 10 to 15 minutes for this.

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So I'll pause the video here and skip right to that point of time where my model Listerine.