LSGAN solves the following problems: where a, b and c refer to the baseline values for the discriminator. The above equation use a least square loss, under which the discriminator is forced to have designated values (a, b and c) for the real samples and the generated samples, respectively, rather than a probability for the real or fake samples. 2018-06-27 PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein gradient penalty, least squares, deep regret analytic, bounded equilibrium, relativistic, f-divergence, Fisher, and information generative adversarial networks (GANs), and standard, variational, and bounded information rate variational autoencoders (VAEs). I’m investigating the use of a Wasserstein GAN with gradient penalty in PyTorch. I’m heavily borrowing from Caogang’s implementation, but am using the discriminator and generator losses used in this implementation because I get Invalid gradient at index 0 - expected shape[] but got [1] if I try to call .backward() with the one and mone args used in the Caogang implementation. I’m PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein gradient penalty, least squares, deep regret analytic, bounded equilibrium, relativistic, f-divergence, Fisher, and information generative adversarial networks (GANs), and standard, variational, and bounded information rate variational autoencoders (VAEs).

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This repository was re-implemented with reference to tensorflow-generative-model-collections by Hwalsuk Lee I tried to implement this repository as much as possible with tensorflow-generative-model-collections , But some models are a little different. I’m investigating the use of a Wasserstein GAN with gradient penalty in PyTorch. I’m heavily borrowing from Caogang’s implementation, but am using the discriminator and generator losses used in this implementation because I get Invalid gradient at index 0 - expected shape[] but got [1] if I try to call .backward() with the one and mone args used in the Caogang implementation. I’m Motivated by this phenomenon, least-square GAN (LSGAN) replaces a sigmoid cross entropy loss with a least square loss, which directly penalizes fake samples by moving them close to the real data distribution.

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This repository was re-implemented with reference to tensorflow-generative-model-collections by Hwalsuk Lee I tried to implement this repository as much as possible with tensorflow-generative-model-collections , But some models are a little different. torch.optim¶. torch.optim is a package implementing various optimization algorithms. Most commonly used methods are already supported, and the interface is general enough, so that more sophisticated ones can be also easily integrated in the future.

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Join the PyTorch developer community to contribute, learn, and get your questions answered. Developer Resources. Find resources and get questions answered. Forums. A place to discuss PyTorch code, issues, install, research. Models (Beta) Discover, publish, and reuse pre-trained models Prior to PyTorch 1.1.0, the learning rate scheduler was expected to be called before the optimizer’s update; 1.1.0 changed this behavior in a BC-breaking way. If you use the learning rate scheduler (calling scheduler.step() ) before the optimizer’s update (calling optimizer.step() ), this will skip the first value of the learning rate schedule.