Original subtitles

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Hello and welcome to this new tutorial and this toile we're going to create the brain of our discriminator

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now that we have the class.

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But the thing is I might break the record for the fastest to toil in the world because actually we already

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have everything.

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There is nothing more easy to create the brain of the discriminator because basically that's exactly

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the same as what we did for the brain of the generator.

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We simply need to choose a new name.

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We're going to call it net D.

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Obviously we need to create an object of the class corresponding to the discriminator that contains

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the architecture of the neural network and the void function.

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And then that's the same here.

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We're going to take our discriminator object and apply the weights in a function that will initialize

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the weights the proper way.

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And here we are done with the creation of the brain of the discriminator.

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So now huge congratulations.

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You created two brains.

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It's now time to train them make them smart and actually make them artists because they will actually

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generate some art by creating some fake images.

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But you're going to see that these are actually going to be some beautiful images.

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I mean some images that look like something.

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So again I have to insist.

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Congratulations because not only have created two brains but also you are done with the first huge step

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of this implementation.

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If we want to divide this implementation into two big parts Well I would say that the first big part

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is the creation of these two brains.

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And the second part is the training of these two brains.

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So you're done with the first big part because you have created these two brains.

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And now we're going to move on to the second part to train these two brains.

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And of course train them according to the process of degenerative adversarial networks and let's give

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a quick reminder of what this process is about this process is two big steps.

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The first step is to update the weight of the discriminator neural network and that consists of maximizing

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the error calculated by the B.C.E..

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You will see that.

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And the second big part and then the second big step of this process is to have data weights of this

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time the generator which again will consist of maximizing the area calculated by the bacillus.

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All right so if you accept take the challenge to train this brain will join me for the second part of

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this implementation and so I'll you very soon to make our decisions the greatest artist of all the computer

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vision models.

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Until then enjoy computer vision.