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These are the user uploaded subtitles that are being translated: 1 00:00:00,450 --> 00:00:02,530 Hello and welcome to listen to Soyo. 2 00:00:02,760 --> 00:00:07,520 All right so in the previous Steuerle we took care of the first step to obtain the weight of the neural 3 00:00:07,530 --> 00:00:09,130 network of discriminator. 4 00:00:09,360 --> 00:00:11,700 And now we're going to tackle the second step. 5 00:00:11,700 --> 00:00:15,390 Updating the weights of the neural network of this time the generator. 6 00:00:15,630 --> 00:00:18,830 So it's going to be easier than the first big step. 7 00:00:18,840 --> 00:00:23,710 Break down the area between the real error and fake error to compute the total error. 8 00:00:23,850 --> 00:00:29,490 This time there will only be one error Dallas area between the prediction of the discriminator whether 9 00:00:29,490 --> 00:00:33,900 or not the image generated by the generator should be accepted yes or no. 10 00:00:34,080 --> 00:00:36,550 And the target which will be equal to 1. 11 00:00:36,570 --> 00:00:42,630 Why will this be equal to one that's because this time we want the generator to have some weights that 12 00:00:42,720 --> 00:00:50,400 allow his brain to produce some images that look like real images and therefore we want to push the 13 00:00:50,400 --> 00:00:56,550 production close to a target of one this time we're training the brain of the generator to be able to 14 00:00:56,550 --> 00:00:59,680 generate some images that look like real images. 15 00:00:59,850 --> 00:01:04,680 So that's another key point to understand the target will be equal to one. 16 00:01:04,770 --> 00:01:10,410 Even if this time the image that will be the input of the discriminator will be the fake image of the 17 00:01:10,410 --> 00:01:11,490 generator. 18 00:01:11,490 --> 00:01:12,330 All right. 19 00:01:12,330 --> 00:01:13,350 So let's do this. 20 00:01:13,350 --> 00:01:15,710 It's going to be faster than previously. 21 00:01:15,810 --> 00:01:22,830 We're going to start by initializing the gradient of the generator with respect to the weights to zero. 22 00:01:23,070 --> 00:01:25,270 And let's do that efficiently. 23 00:01:25,380 --> 00:01:30,510 We simply need to take that line of code again because that's the same thing we did for the gradient 24 00:01:30,510 --> 00:01:31,690 of the discriminator. 25 00:01:31,830 --> 00:01:38,370 So I am pasting in here and then replacing a D by of course not g. 26 00:01:38,490 --> 00:01:44,200 We want to initialize the weight of the gradient of the generator this time. 27 00:01:44,200 --> 00:01:45,380 Then next step. 28 00:01:45,630 --> 00:01:49,600 Well the next step would naturally be to get the input. 29 00:01:49,710 --> 00:01:55,590 But the thing is we already have the inputs you know the input is going to be this fake image or should 30 00:01:55,590 --> 00:02:02,580 I say this mini batch of fake images that are going to be again the input of the discriminator. 31 00:02:02,580 --> 00:02:07,560 So we already have the input we already have the fake images of the mini batch and therefore we're directly 32 00:02:07,560 --> 00:02:09,190 going to get the targets. 33 00:02:09,230 --> 00:02:13,200 And so this time according to you what is the toy going to be. 34 00:02:13,200 --> 00:02:17,070 Is it going to be a mini batch of zeros or of ones. 35 00:02:17,340 --> 00:02:23,100 Well as I explained in the beginning of the tutorial this time we want to push the predictions to one 36 00:02:23,250 --> 00:02:28,910 because we want the discriminator to accept that the fake images are real images. 37 00:02:28,950 --> 00:02:35,760 So the target for all the input fake images of the mini batch should be all ones and therefore I'm taking 38 00:02:36,420 --> 00:02:44,810 this line of code copying it and pasting it right here to get my targets of once great. 39 00:02:44,850 --> 00:02:47,630 My target is already wrapped into a variable. 40 00:02:47,700 --> 00:02:48,440 Perfect. 41 00:02:48,510 --> 00:02:50,920 I'm allowed to move on to the next step. 42 00:02:50,940 --> 00:02:52,310 So now what is the next step. 43 00:02:52,500 --> 00:02:59,910 Well the next step is to get the output the output of the discriminator when the input is are fake images. 44 00:02:59,910 --> 00:03:07,590 Therefore I'm getting a new variable output and I'm taking my neural network of the discriminator to 45 00:03:07,590 --> 00:03:15,720 needy and I'm feeling this neural network of the discriminator with the Merabet fake input images. 46 00:03:15,840 --> 00:03:21,180 And so for each of these fake images I'm going to get the discrimination that is I'm going to get a 47 00:03:21,180 --> 00:03:26,980 discriminating number between 0 and 1 if this number is close to zero the image will be rejected. 48 00:03:27,180 --> 00:03:31,670 And if this number is close to 1 the image will be accepted. 49 00:03:31,680 --> 00:03:38,660 Now something important remember that in the previous output we detached the gradient of fake. 50 00:03:38,670 --> 00:03:40,290 This time we're not going to do it. 51 00:03:40,290 --> 00:03:41,180 Why is that. 52 00:03:41,280 --> 00:03:45,960 Because we want to keep the gradient of fake We want to keep the Great in effect because we're going 53 00:03:45,960 --> 00:03:49,410 to update the weights of the neural network and the generator. 54 00:03:49,500 --> 00:03:53,410 And two of these weight will actually need the gradient AFAIK. 55 00:03:53,540 --> 00:03:57,210 So that's why it's important here not to detach it. 56 00:03:57,210 --> 00:03:57,870 All right. 57 00:03:57,870 --> 00:04:01,520 Next step now that we have the output and the target. 58 00:04:01,620 --> 00:04:07,770 Well we are ready to get the error of prediction but this time this error of prediction is going to 59 00:04:07,770 --> 00:04:14,100 be the error related to the generator because we will back propagate this error back into the neural 60 00:04:14,100 --> 00:04:22,210 network of the generator as opposed to before where we back propagated the total error back to the discriminator. 61 00:04:22,410 --> 00:04:27,270 So that's important now to understand that this error is related to the generator and therefore I'm 62 00:04:27,270 --> 00:04:31,540 going to call it the r r g e r r g. 63 00:04:31,680 --> 00:04:38,310 Then I'm going to get from my criterion that is going to compute the last error between the outputs 64 00:04:38,940 --> 00:04:45,120 and the target the output which is the output of the discriminator when the input is the fake image 65 00:04:45,480 --> 00:04:48,590 and the target which is the mini Becci full of watts. 66 00:04:48,690 --> 00:04:55,450 All right so now that we have the error we can back propagate it in the neural network of the generator. 67 00:04:55,590 --> 00:04:57,200 So I'm taking this error. 68 00:04:57,320 --> 00:04:59,620 R R G than that. 69 00:04:59,670 --> 00:05:07,280 And then applying the backward function which keep in mind so far only compute the gradients but then 70 00:05:07,760 --> 00:05:13,370 we are going to use the optimizer of the generator to make sure that this time it's going to be the 71 00:05:13,370 --> 00:05:16,580 weight of the generator that will be updated. 72 00:05:16,580 --> 00:05:25,910 Therefore I'm exactly going to take the optimizer of the generator optimizer g to update the weights 73 00:05:26,450 --> 00:05:29,040 of the neural network of the generator. 74 00:05:29,270 --> 00:05:30,210 And here we go. 75 00:05:30,230 --> 00:05:31,640 The second step is done. 76 00:05:31,910 --> 00:05:33,130 So congratulations. 77 00:05:33,140 --> 00:05:36,130 Now basically the difficult part of the training is done. 78 00:05:36,200 --> 00:05:37,400 Now it's time for fun. 79 00:05:37,410 --> 00:05:43,010 We're gonna print the losses inside the loop so we're going to stay in the loop then we're going to 80 00:05:43,010 --> 00:05:49,520 save the real images and also of course the fake images and then eventually after the training the fake 81 00:05:49,520 --> 00:05:55,800 images and the real image will appear in this results folder that will contain the final results. 82 00:05:55,850 --> 00:05:58,080 So let's do all this fun stuff. 83 00:05:58,130 --> 00:06:02,480 In the last tutorial of this module I'm super excited to show you the result. 84 00:06:02,510 --> 00:06:06,560 It's going to be something it's going to be pure computer vision creation. 85 00:06:06,560 --> 00:06:12,170 So prepare yourself a good coffee or a good tea sit comfortably in your chair and get ready for the 86 00:06:12,170 --> 00:06:13,200 final results. 87 00:06:13,250 --> 00:06:15,080 Until then enjoy computer vision. 9399