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The discriminator has the task of determining whether a given image looks natural (i.e, is an image from the dataset) or looks like it has been artificially created.
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The task of the generator is to create natural looking images that are similar to the original data distribution, images that look natural enough to fool the discriminator network.
The discriminative model has the task of determining whether a given image looks natural (an image from the dataset) or looks like it has been artificially created.
This is basically a binary classifier that will take the form of a normal convolutional neural network (CNN).
The task of the generator is to create natural looking images that are similar to the original data distribution.
The generator is trying to fool the discriminator while the discriminator is trying to not get fooled by the generator.
As the models train through alternating optimization, both methods are improved until a point where the Fake images are indistinguishable from the dataset images.
First Part- Discriminator always wants to maximize its probability of classifying an image correctly as real or fake.
Here, the images are sampled from the original data distribution, which is the real data itself.
Dawnlood mp3 free gotik metalSecond Part- z is the random noise sample and G(z) is the generated image using a noise sample.
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Generator always wants maximize the probability that the discriminator getting fooled by the generated images.
Which means, the generator should want to maximize D(G(z)), so it should minimize 1- D(G(z)) and hence log(1- D(G(z))).
Celebrity Image Generation using GANs Celebrity Image Dataset: CelebA dataset is the collection of over 200,000 celebrity faces with annotations.
Since in this blog, I am just going to generate the faces so I am not taking annotations into consideration.
Getting the Data:- import helper helper.downloadextract(celeba, datadir) I have created the helper.py file through which you can download the CelebA dataset images.
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While running this code snippet, it will download the CelebA dataset.(Source code link is given below).
Preprocessing the images:- Since I am working only on faces so I have resized it down to 2828 in order to get the good results.
I have cropped the portion of image which not includes the image portion.
In order to get the accurate results we should have a good GPU(4GB or above than this), by running this code snippet you can find whether tensorflow is installed with GPU or not.
All transpose convolutions with depths reducing from 1024 all the way down to 3 which represents an RGB color image.
The final layer outputs a 28 x28x3 tensor through the Hyperbolic Tangent ( tanh ) function.
When discriminator sees the differences in the image it sends the gradient signal to the Generator and this signal is flows from discriminator to the generator.
Generator Loss and Discriminator Loss:- Discriminator is receiving the images from both i.e training images and generator,so while calculating discriminators loss we have to add loss due of real images and also due of fake images both networks are trained simultaneously so we need two optimizers for both generator and discriminator both.
I have also got the pre-trained network from here and if you want to run the GAN using this pre-trained networks then use this python file which i am providing here.