Original subtitles

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Step two is basically the same thing.

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But now the networks have been more trained which is going to do this again to reiterate this whole

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process and understand it better.

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So Step Two noise goes into the generator generator generates images which are not.

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They don't look as random anymore.

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They're kind of a bit clearer and we'll understand why in just a second off once we were finishing up

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with Step two we'll understand how they generate or understand these things.

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But basically through the through the back propagation error the declaration of error understood where

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it was making mistakes in the it.

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It's adjusted its weight and now is generating images which are a little bit more like dogs.

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And now we're going to train the discriminator again and for that we need some images of dogs.

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A new batch.

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And now we're going to put all of those into the discriminator along with the images of the dogs and

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it will output some values so I'll put the values now we need to compare the values to the actual numbers

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that we want that we know which ones are those which are not dogs the top ones are not dogs but the

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dogs.

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So we tell that to the discriminator this cremator Kalka is the era that propagates the arrow through

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its network and therefore learns from that and the weights are obtained.

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So next time we'll do a better job at discriminating dogs versus dogs.

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So it's learning.

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So it's now at some level to kind of like metaphorically speaking.

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And now we want to train the generators so the.

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So we want to get rid of that the generator generator and then we're going to use the same image.

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We're going to put them through the network of the discriminator is going to output some values.

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As you can see these values are lower.

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So if I go back you'll see that the values were apparently is open for the point to now the values are

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0.5 inches or 21 so they draw because the discriminator has learned what how that these don't really

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look like dogs.

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But as you can see they don't.

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They didn't drop as low they were like dropping to like a complete zero.

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We'll talk about that later further down when we're doing Step Three.

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But for now just kind of noticed that what do we want to discuss now though is that what happens.

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How does a generator kind of like get better.

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Well I think of that intuitively.

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Well we again we take these values we calculate errors because we want these values to be ones.

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That's what the generator wanted wants to trick the discriminator and this error these errors of backburn

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brigade through to the discriminator and the weights are updated and the way to think of it intuitively

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is these networks are huge What's what we have drawn here is like these are very small just representations

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of those networks just like images to show that this is indeed a network.

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But in reality those areas are much much bigger.

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And the way to think about it is that the there's the communication happening between the general discriminant

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So the generator creates these images.

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Since this criminal says hey this computer I have some images here.

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Do you do you think these are images of dogs or not.

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And what do you think the possibilities are.

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And the distributor looks at them and says oh well you know those don't really look like dogs to me

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I'll give them about 20 percent probability 10 percent probability of 50 percent possibility and then

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that discriminators like Are you OK.

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You're totally right.

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I was trying to trick you.

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These are not dogs but can you tell me what I did wrong.

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You know like what where did I go wrong.

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And the discriminator in this case my look at the images and say this is the back propagation we're

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kind of like this is the intuitive understanding of the back propagation process and gry indecent the

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discriminator might say something like look I checked your images of dogs you know you know the images

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you send me and I really have seen images of dogs.

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So I know that dogs normally for instance have eyes.

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Your dogs have some little black dots which don't really look like eyes to me and you're missing out

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on eyes or you know you're you know we don't have enough paws or you don't have tails and your dogs

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those don't look like dogs to me or they're not.

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They don't look three dimensional enough they look very flat dogs and all three look three dimensional

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to me.

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So and that's this process of back propagation.

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This error is actually that's what it's kind of like overall in human language telling that discriminate

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the generator but in reality of course it's just telling the generator that hey you know you got to

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go to this part of your image doesn't feel right.

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This part of the image doesn't feel right and just basically dealing with individual pixels and how

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it's how they should be updated for these images to better resemble dogs.

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And then the weights of the neural network are up.

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So the next time we'll do a better job the generator.

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So step three.