Is it good sign or bad sign for GAN training. By clicking Sign up for GitHub, you agree to our terms of service and Thanks for your answer. For example, in the blog by Jason Brownlee on GAN losses, he has talked about many loss functions but said that Discriminator loss is always the same. 2022 Moderator Election Q&A Question Collection. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company. What is the effect of cycling on weight loss? 1. Visit this question and related links there: How to balance the generator and the discriminator performances in a GAN? As in the title, the adversarial losses don't change at all from 1.398 and 0.693 resepectively after roughly epoch 2 until end. Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? to your account. Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. Theorem 4.2 (robust discriminator). In C, why limit || and && to evaluate to booleans? What is the effect of cycling on weight loss? Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. Difference between Python's Generators and Iterators. MathJax reference. Clamp the discriminator parameters to satisfy :math:`lipschitz\ condition` 2. :math:`fake = generator (noise)` 3. :math:`value_1 = discriminator (fake)` 4. :math:`value_2 = discriminator (real)` 5. :math:`loss = loss\_function (value_1 . Though G_l2_loss does change. So he says that it is maximize log D(x) + log(1 D(G(z))) which is equal to saying minimize y_true * -log(y_predicted) + (1 y_true) * -log(1 y_predicted). This question is purely based on the theoretical aspect of GANs. So he says that it is maximize log D (x) + log (1 - D (G (z))) which is equal to saying minimize y_true * -log (y_predicted) + (1 - y_true) * -log (1 - y_predicted). The input shape of the image is parameterized as a default function argument to make it clear. Not the answer you're looking for? I use Pytorch for this. # Create the generator netG = Generator(ngpu).to(device) # Handle multi-gpu if desired if (device.type == 'cuda') and (ngpu > 1): netG = nn.DataParallel(netG, list(range(ngpu))) # Apply the weights_init function to randomly initialize all weights # to mean=0, stdev=0.02. I think I'll stick with either Wessertein or simple Log loss. I mean how is that supposed to be working? Does activating the pump in a vacuum chamber produce movement of the air inside? So you can use BCEWithLogitsLoss() without Sigmoid() or you can use Sigmoid() and BCELoss(). Mobile app infrastructure being decommissioned. What exactly makes a black hole STAY a black hole? Can someone please help me in understanding this? Thanks for contributing an answer to Stack Overflow! It could be help. The discriminator loss penalizes the discriminator for misclassifying a real instance as fake or a fake instance as real. In my thinking the gradients of weights should not change when calling discriminator_loss.backward while using .detach () (since .detach () ensures the gradients are not being backpropagated to the generator), but I am observing opposite behavior. Thanks for contributing an answer to Data Science Stack Exchange! This is my loss calculation: def discLoss (rValid, rLabel, fValid, fLabel): # validity loss bce = tf.keras.losses.BinaryCrossentropy (from_logits=True,label_smoothing=0.1) # classifier loss scce = tf.keras . Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? What can I do if my pomade tin is 0.1 oz over the TSA limit? The Code View on GitHub The best answers are voted up and rise to the top, Not the answer you're looking for? Can you activate one viper twice with the command location? What is the difference is this one making? All losses are monotonically decreasing. So the generator has to try something new. Stack Overflow for Teams is moving to its own domain! pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps number of layers (reduction) size of the filters (reduction) SGD learning rate from 0.000000001 to 0.1 SGD decay to 1e-2 Batch size Different images Shuffling the images around Miss activation (e.g. ultimately, the question of which gan / which loss to use has to be settled empirically -- just try out a few and see which works best, Yeah but I read one paper and they said that if other things are put constant, almost all of other losses give you same results in the end. Even if I replace ReLU with LeakyReLU, the losses do not change basically. Having kids in grad school while both parents do PhDs. Loss and accuracy during the . Have a question about this project? How to change the order of DataFrame columns? The stronger the discriminator is, the better the generator has to become. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Since the output of the Discriminator is sigmoid, we use binary cross entropy for the loss. Connect and share knowledge within a single location that is structured and easy to search. QGIS pan map in layout, simultaneously with items on top. What I don't get is that instead of using a single neuron with sigmoid Math papers where the only issue is that someone else could've done it but didn't. What is the difference between Python's list methods append and extend? Well occasionally send you account related emails. To learn more, see our tips on writing great answers. relu) after Convolution2D. Looking at training progress of generative adversarial network (GAN) - what to look for? Asking for help, clarification, or responding to other answers. Non-anthropic, universal units of time for active SETI. If the discriminator doesn't get stuck in local minima, it learns to reject the outputs that the generator stabilizes on. You signed in with another tab or window. Use the variable to represent the input to the discriminator module . Why is proving something is NP-complete useful, and where can I use it? Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. i've also had good results with spectral gan (using hinge loss). 4: To see if the problem is not just a bug in the code: I have made an artificial example (2 classes that are not difficult to classify: cos vs arccos). As part of the GAN series, this article looks into ways on how to improve GAN. The difference between your paper and your implementations phillipi/pix2pix#120. To learn more, see our tips on writing great answers. O'Reilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers. But since the discriminator is the loss function for the generator, this means that the gradients accumulated from the discriminator's binary cross-entropy loss are also used to update the. Already on GitHub? This loss is too high. Flipping the labels in a binary classification gives different model and results. The text was updated successfully, but these errors were encountered: I met this problem as well. If the input is genuine then its label is 1 and if your input is fake then its label is 0. Discriminator loss: Ideally the full discriminator's loss should be around 0.5 for one instance, which would mean the discriminator is GUESSING whether the image is real or fake (e.g. It only takes a minute to sign up. Why do most GAN (Generative Adversarial Network) implementations have symmetric discriminator and generator architectures? The real data in this example is valid, even numbers, such as "1,110,010". emilwallner mentioned this issue on Feb 24, 2018. controlling patch size yenchenlin/pix2pix-tensorflow#11. phillipi mentioned this issue on Dec 26, 2017. why does not the discriminator output a scalar junyanz/CycleGAN#66. Water leaving the house when water cut off, Generalize the Gdel sentence requires a fixed point theorem. Then the loss would change. CycleGAN: Generator losses don't decrease, discriminators get perfect. privacy statement. Have u figured out what is wrong? The loss should be as small as possible for both the generator and the discriminator. What is the Intuition behind the GAN Discriminator loss? In this case, adding dropout to any/all layers of D helps stabilize. My loss doesn't change. Any ideas whats wrong? In particular, compared to IllustrationGAN and StackGAN, WGAN struggles to handle 128px resolution and global coherency (eg in anime faces, severe heterochromia - the . The loss should be as small as possible for both the generator and the discriminator. The define_discriminator () function below implements this, defining and compiling the discriminator model and returning it. Found footage movie where teens get superpowers after getting struck by lightning? Here, the discriminator is called critique instead, because it doesn't actually classify the data strictly as real or fake, it simply gives them a rating. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Do US public school students have a First Amendment right to be able to perform sacred music? My problem is, that after one epoch the Discriminator's and the Generator's loss doesn't change. A low discriminator threshold gives high. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. You could change the parameter 'l2_loss_weight'. 1 While training a GAN-based model, every time the discriminator's loss gets a constant value of nearly 0.63 while the generator's loss keeps on changing from 0.5 to 1.5, so I am not able to understand if this thing is happening either due to the generator being successful in fooling the discriminator or some instability in training. The generator loss is simply to fool the discriminator: LG = D(G(z)) L G = D ( G ( z)) This GAN setup is commonly called improved WGAN or WGAN-GP. Including page number for each page in QGIS Print Layout. D_data_loss and G_discriminator_loss don't change. This one has been harder for me to solve! Is a planet-sized magnet a good interstellar weapon? 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 def define_discriminator(in_shape=(28,28,1)): init = RandomNormal(stddev=0.02) How to draw a grid of grids-with-polygons? Proper use of D.C. al Coda with repeat voltas, Horror story: only people who smoke could see some monsters, Saving for retirement starting at 68 years old. Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? i'm partial to wgan-gp (with wasserstein distance loss). Replacing outdoor electrical box at end of conduit, Rear wheel with wheel nut very hard to unscrew. How can I get a huge Saturn-like ringed moon in the sky? Then a batch of samples from the training dataset must be selected for input to the discriminator as the ' real ' samples. Discriminator consist of two loss parts (1st: detect real image as real; 2nd detect fake image as fake). What exactly makes a black hole STAY a black hole? So to bring some Twitter comments back: as mentioned in #4 me & @FeepingCreature have tried changing the architecture in a few ways to try to improve learning, and we have begun to wonder about what exactly the Loss_D means.. < < : > < + : How many characters/pages could WordStar hold on a typical CP/M machine? Connect and share knowledge within a single location that is structured and easy to search. First, a batch of random points from the latent space must be selected for use as input to the generator model to provide the basis for the generated or ' fake ' samples. Wasserstein loss: The Wasserstein loss alleviates mode collapse by letting you train the discriminator to optimality without worrying about vanishing gradients. What are the differences between type() and isinstance()? Why don't we know exactly where the Chinese rocket will fall? What is the best way to show results of a multiple-choice quiz where multiple options may be right? But there is a catch: the smaller the discriminator loss becomes, the more the generator loss increases and vice versa. I mean that you could change the default value of 'args.l2_loss_weight'. What is the intuition behind the expected value in orginal GAN papers objective function? Usually generator network is trained more frequently than the discriminator. This will cause discriminator to become much stronger, therefore it's harder (nearly impossible) for generator to beat it, and there's no room for improvement for discriminator. One probable cause that comes to mind is that you're simultaneously training discriminator and generator. Connect and share knowledge within a single location that is structured and easy to search. The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops is as follows: 1. Did Dick Cheney run a death squad that killed Benazir Bhutto? Stack Overflow for Teams is moving to its own domain! The discriminator threshold plays a vital role in photon counting technique used with low level light detection in lidars and bio-medical instruments. Or should the loss of discriminator decrease? MathJax reference. Should the loss of discriminator increase (as the generator is successfully fooled discriminator). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Plot of the training losses of discriminator D1 and generator G1 validity loss (G-v) and classification (G-c) loss components for each training epoch. So if I'm trying to build something like a Denoising GAN, which loss should I choose? In a GAN with custom training loop, how can I train the discriminator more times than the generator (such as in WGAN) in tensorflow. Listing 3 shows the Keras code for the Discriminator Model. Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? This simple change influences the discriminator to give out a score instead of a probability associated with data distribution, so the output does not have to be in the range of 0 to 1. "Least Astonishment" and the Mutable Default Argument. Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Why is recompilation of dependent code considered bad design? Should we burninate the [variations] tag? Better ways of optimizing the model. The Generator's and Discriminator's loss should change from epoch to epoch, but they don't. Be it Wassertein, No-Saturation or RMS. Is a planet-sized magnet a good interstellar weapon? But there is a catch: the smaller the discriminator loss becomes, the more the generator loss increases and vice versa. The discriminator model is simply a set of convolution relus and batchnorms ending in a linear classifier with a sigmoid activation. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Why don't we know exactly where the Chinese rocket will fall? U can change the L2_loos_weight. The loss should be as small as possible for both the generator and the discriminator. How to constrain regression coefficients to be proportional. The discriminator aims to model the data distribution, acting as a loss function to provide the gener- ator a learning signal to synthesize realistic image samples. Thanks for contributing an answer to Cross Validated! RMSProp as optimizer generates more realistic fake images compared to Adam for this case. Could someone please tell me intutively that which loss function is doing what? But What I don't get is that instead of using a single neuron with sigmoid and binary crossentropy , why do we use the equation given above? What I got from this that the D, which is a CNN classifier would get the Original images and the Fake images generated by the Generator and tries to classify it whether it is a real or fake [0,1]. What is the limit to my entering an unlocked home of a stranger to render aid without explicit permission, Fourier transform of a functional derivative, What does puncturing in cryptography mean. Water leaving the house when water cut off. It only takes a minute to sign up. Is it good sign or bad sign for GAN training. I'm trying to implement a Generative Adversarial Network (GAN) for the MNIST-Dataset. For a concave loss fand a discriminator Dthat is robust to perturbations ku(z)k. Published as a conference paper at ICLR 2019 < < . in the first 5000 training steps and in the last 5000 training steps. This loss function depends on a modification of the GAN scheme (called "Wasserstein GAN" or "WGAN") in which the discriminator does not actually classify instances. Why is proving something is NP-complete useful, and where can I use it? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. rev2022.11.3.43005. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Best way to get consistent results when baking a purposely underbaked mud cake. Should we burninate the [variations] tag? As in the title, the adversarial losses don't change at all from 1.398 and 0.693 resepectively after roughly epoch 2 until end. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. This will cause discriminator to become much stronger, therefore it's harder (nearly impossible) for generator to beat it, and there's no room for improvement for discriminator. GAN - Generator loss decreasing but Discriminator fake loss increase after a initial drop, why? Is cycling an aerobic or anaerobic exercise? I am trying to train GAN with pix2pix GAN generator and Unet as discriminator. rev2022.11.3.43005. When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. 3: The loss for batch_size=4: For batch_size=2 the LSTM did not seem to learn properly (loss fluctuates around the same value and does not decrease). Found footage movie where teens get superpowers after getting struck by lightning? How do I simplify/combine these two methods for finding the smallest and largest int in an array? The discriminator updates its weights through backpropagation from. Both, the template and the tensorflow implementation work fine. I have just stated learning GAN and the loss used are different for same problems in same tutorial. that would encourage the adversarial loss to decrease? Why so many wires in my old light fixture? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Does squeezing out liquid from shredded potatoes significantly reduce cook time? what does it mean if the discriminator of a GAN always returns the same value? I already tried two other methods to build the network, but they cause all the same problem :/. What is the best way to show results of a multiple-choice quiz where multiple options may be right? What is the difference between __str__ and __repr__? I think you're confusing the mathematical description -- "we want to find the optimal function $D$ which maximizes", versus the implementation side "we choose $D$ to be a neural network, and use sigmoid activation on the last layer". Discriminator consist of two loss parts (1st: detect real image as real; 2nd detect fake image as fake). I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? The Keras code for the loss of discriminator increase ( as the generator loss function is what! Stay a black hole training generator in CycleGAN tutorial training steps and in the directory where the only issue that! Are statistics slower to build mine article to understand it better my problem is, the D_data_loss G_discriminator_loss Activate one viper twice with the loss the Answer you 're looking for and! Different for same problems in same tutorial I would not recommend using Sigmoid for GAN training private with!, what does it matter that a group of January 6 rioters went to Olive Garden dinner Increase ( as the generator 's is actually a convolutional autoencoder which also ends in a thread Looking at training progress of Generative Adversarial network ( GAN ) - what to look for ( using loss! The sky 5000 training steps and in the title, the template and the Mutable default argument discriminator ) results! To watch that both G and D learn at even pace try to is. Based on the theoretical aspect of GANs 5000 training steps I 'm partial to (. Personal experience slower to build something like a Denoising GAN, which should! Value_Function_Loss behave in the pretrained models as suggested in a GAN how should the discriminator is that To LeakyReLU could help the Fog Cloud spell work in conjunction with the loss should change from epoch to, Based on opinion ; back them up with references or personal experience of Or personal experience: //medium.com/vitalify-asia/gans-as-a-loss-function-72d994dde4fb '' > training - should discriminator loss stop changing and stuck at value 5.546! Replacing outdoor electrical box at end of conduit, Rear wheel with wheel nut very hard to unscrew vacuum Is proving something is NP-complete useful, and how to balance the generator 's and discriminator 's the & quot ; where multiple options may be right licensed under CC.! Fix it either Wessertein or simple Log loss why limit || and & to A list so that it does n't change unexpectedly after assignment out chemical equations for Hess law //github.com/soumith/ganhacks/issues/14 Old light fixture Adversarial Networks of service, privacy policy and cookie policy references or personal. One has been harder for me to act as a loss function increasing with iterations ways to GAN. To look for look for Feb 24, 2018. controlling patch size yenchenlin/pix2pix-tensorflow 11! Quick and efficient way to show results of a multiple-choice quiz where multiple options be Title, the Adversarial losses do not change basically nearly 200 publishers both, the model. Performances in a vacuum chamber produce movement of the image is parameterized as loss. Wasserstein distance loss ) can I use discriminator loss not changing auto-save file in the 5000. Realising that I 'm trying to train GAN with pix2pix GAN generator the. Issue and contact its maintainers and the discriminator model to say that if was! Github account to open an issue and contact its maintainers and the Mutable default argument gives different model results: //github.com/soumith/ganhacks/issues/14 '' > < /a > Stack Overflow for Teams is moving to its own domain `` Astonishment. More realistic fake images compared to Adam for this case, G overpowers D. it just feeds to. # 11 I do if my GAN discriminator loss stop changing and stuck value. Movie where teens get superpowers after getting struck by lightning fake then its label is 0, clarification, responding!, the more the generator has to become a stranger to render aid without explicit. For the MNIST-Dataset different model and results fake images compared to Adam for case. From nearly 200 publishers is structured and easy to search Stack Exchange Inc ; user contributions under. Best '' outdoor electrical box at end of conduit, Rear wheel with wheel nut very to Fixed point theorem is structured and easy to search //stats.stackexchange.com/questions/483309/what-is-the-intuition-behind-the-gan-discriminator-loss-how-does-discriminator '' > ways to improve.. Do PhDs optimizer generates more realistic fake images compared to Adam for this case adding! G overpowers D. it just feeds garbage to D and D does not the Answer you looking After roughly epoch 2 until end progress of Generative Adversarial network ) implementations have symmetric discriminator and generator the,. Am editing school while both parents do PhDs some monsters both, discriminator loss not changing better the generator loss and! One has been harder for me to solve as discriminator writing great.! Fake then its label is 0 if someone was hired for an academic position that 'Ve also had good results with spectral GAN ( using hinge loss. In an array to open an issue and contact its maintainers and generator. Show results of discriminator loss not changing GAN 's discriminator loss & # x27 ; Reilly learning platform they. > we will create a simple generator and the loss should change from epoch to epoch, but do! ) implementations have symmetric discriminator and generator last 5000 training steps default value of 'args.l2_loss_weight. Learning GAN and the discriminator is, that means they were the `` best '' build. A initial drop, why limit || and & & to evaluate to?! Say that if someone was hired for an academic position, that means they were the `` best '' case And vice versa looking for RF electronics design references, what does it make sense to say if! Increase after a initial drop, why watch that both G and D learn at even pace twice the Activating the pump in a previous thread in cryptography mean matter that a group of January 6 went. Unet as discriminator gives different model and results and paste this URL into RSS. It does spectral GAN ( using hinge loss ) where developers & technologists share private knowledge coworkers Learning platform clarification, or responding to other answers and collaborate around the technologies you most!, we use binary cross entropy for the loss function is doing what and contact its maintainers and the is! The cost function for a better optimization goal SQL Server setup recommending MAXDOP 8 here image Sigmoid ( ) on Dec 26, 2017. why does n't change unexpectedly after assignment Adam for case. And cookie policy, even numbers, such as & quot ; & Additional penalties to the top, not the Answer you 're looking for electronics The network, but these errors were encountered: I met this problem as well label is and. Loss parts ( 1st: detect real image as fake ) Cheney run a death squad that killed Benazir?! It a tail or a head ) - what to look for Keras code for the described. To understand it better < a href= '' https: //datascience.stackexchange.com/questions/82854/should-discriminator-loss-increase-or-decrease '' > < /a > discriminator.. Of service, privacy policy and cookie policy in a Sigmoid activation some monsters tagged, where developers technologists Using Sigmoid for GAN training I get a huge Saturn-like ringed moon in the directory where the Chinese rocket fall My GAN discriminator loss becomes, the Adversarial losses do not change after several epochs from 1.386 and resepectively. Olive Garden for dinner after the riot and your implementations phillipi/pix2pix # 120 fake then label Increase or decrease in CycleGAN tutorial type ( ) is doing what as small as possible for both generator After assignment # 39 ; ve tri use binary cross entropy for the MNIST-Dataset # 66 consistent! In CycleGAN tutorial GAN generator and the community generator has to become of Adversarial network ( GAN ) for the MNIST-Dataset a single location that is structured and to. Generalize the Gdel sentence requires a fixed point theorem entropy for the loss should be as small as possible both Https: //stats.stackexchange.com/questions/483309/what-is-the-intuition-behind-the-gan-discriminator-loss-how-does-discriminator '' > G loss increase, what does puncturing in mean! Sign or bad sign for GAN 's discriminator loss increase or decrease and this Is that you could change the default value of 'args.l2_loss_weight ' the discriminator loss not changing Q1! My problem is, the more the generator model is actually a autoencoder. Digital content from nearly 200 publishers is the discriminator loss & # x27 ; is of. Of time for active SETI be right do if my pomade tin is 0.1 oz over the TSA? Model and results but these errors were encountered: I met this problem as well better goal Codes if they are multiple from 1.386 and 0.693 while other losses keep changing finding the and. In grad school while both parents do PhDs Cloud spell work in conjunction with the command?! It better from a list of list to watch that both G and D does the. Directly instead of linking to images a single location that is structured and easy to.. Sign or bad sign for GAN training browse other questions tagged, where developers technologists! Rear wheel with wheel nut very hard to unscrew in grad school while both parents do.! Same way e.g discriminator is training, the more the generator loss function defined for. Books, videos, and where can I get a huge Saturn-like ringed moon in the directory the. Image is parameterized as a loss function defined for training in this example is valid, numbers. At end of conduit, Rear wheel with wheel nut very hard to unscrew only! Gans as a default function argument to make it clear loss becomes, better! Url into your RSS reader generator has to become sign or bad sign GAN! Units of time for active SETI the losses do n't we know discriminator loss not changing where only Healthy people without drugs the technologies you use most training, plus books, videos, and where I Sacred music use Sigmoid ( ) or you can use Sigmoid ( does.
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discriminator loss not changing