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But what the next step is to create architecture for our model, we will be using almost the same architecture.

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We will be using sequential as they appear.

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And we will be adding four on layer.

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We will start with 32 filters.

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Then 64 filters.

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Then 128 filters.

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Then again, 128 filters.

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And after each of this con led, we will.

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We are also playing with willingly.

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And as always, the activation function is the RELU for all these layers.

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And after that, this thing, we are also playing Grop Otilia.

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So what this lawyer will do is it will deactivate 50 percent off neurons during each epoch.

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It will randomly pick for people sent off our neurons and it pretty directly with them.

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And we will be training model with the remaining 50 percent off neurons during each people.

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So for each epoch, we are activating randomly 50 percent of our total neurons.

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We are using Grabbled here because Rubbled is a very effective leader to avoid overfitting in our more

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than.

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So this is of a modern architecture.

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Now, the next, the surplus to come by.

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For lost function, we will be using binary cross entropy.

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Since we have two different classes.

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If you remember earlier in Emmis, we had 10 different classes and there we were using spots categorical

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across entropy.

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But here, since we have one the two classes, we are using Leonetti, Cross and Groppi for optimize

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it.

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We are using Oremus prop with learning rate of zero point zero zero one.

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And since this is a classification problem, we are calculating accuracy metrics as well.

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The next step is to create our modern.

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Since we are taking our data from Katrina and Rita, we have to use for generator to fit our model.

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So we'll be using model dot for gender to Dan Crane, gender to.

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This dirty doesn't matter, which will continuously generate butat in the batches of 32 images.

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And then here we are using Insteps but Epoch as hundred.

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Earlier in our last model, we were using a bad size of 20 steps.

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But Epoch as hungry because we only had to pose an immediate.

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For training purposes.

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But this time, since we are randomly generating images from this transformation, we can use more than

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2000 images as well.

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This time we are using a bed size of potato and steps, but epoch as hundred.

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So overall, in each epoch we are feeding at all three thousand two hundred images.

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The number of epochs this time is hundred.

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And similarly, we will use validation generator to get the validation data.

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Now, since we are running this four hundred a box, if you are using this system with less than 16

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DGP of RAM and without any graphics card, it may take up to one and half to two or worse to create

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this model.

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That's what I have already train this model and I have the data off or one hundred epochs here.

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You can see that our validation accuracy is increasing with each book and at their own ninety two hundred

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book.

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We are getting the validation accuracy between 80 to 84 percent and.

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A training accuracy of it before a debate, but say.

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So if you compare in our last model, we were getting training accuracy of our own 95 to 98 percent

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and a significantly lower valuation accuracy of our own 79 percent.

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In this model, we are getting almost same regulation and lower training accuracy from it before person.

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So you can see that with what image processing and creating dummy images we have created or what fitting

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in our modern.

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After running this, you can save your model by model lot save method and lets us create this graph

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to see how our valuation, accuracy and training accuracy are changing with each epoch.

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So this orange and red lines are for accuracy.

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This orange line is for training accuracy and red line is for validation accuracy.

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You can see here that the validation accuracy is more than 80 percent as well as the training.

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Accuracy is also more than 80 percent.

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And both are moving together.

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So there are no evidence of overfitting.

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And then what model?

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And this is still increasing.

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So if you run it for, say, 40 or 50 more Reeboks, the validation accuracy may reach around 85, 86

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percent as well.

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So that's all for this review.

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v.C that by augmenting our initial dataset, by applying sheer rotation, will shift, hide, shift

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and flips.

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We can treat overfitting fitting in our data and we can get a higher valuation accuracy from our model.

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Thank you.