HyperGAN
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Simultaneous Trainer

d_grads,g_grads = self.calculate_gradients(D, G)
self.train_d(d_grads)
self.train_g(g_grads)

examples

{
"class": "function:hypergan.trainers.simultaneous_trainer.SimultaneousTrainer",
"optimizer": {
"class": "function:torch.optim.Adam",
"lr": 1e-4,
"betas":[0.0,0.999]
},
"hooks": [
{
"class": "function:hypergan.train_hooks.adversarial_norm_train_hook.AdversarialNormTrainHook",
"gamma": 100,
"loss": ["d"]
},
{
"class": "function:hypergan.train_hooks.negative_momentum_train_hook.NegativeMomentumTrainHook",
"gamma": 0.33
}
]
}

options

attribute
description
type
optimizer
Optimizer configuration
Config (required)
hooks
Train Hooks
Array of configs (optional)