Alternating Trainer
d_grads = self.calculate_gradients(D)
self.train_d(d_grads)
g_grads = self.calculate_gradients(G)
self.train_g(g_grads)
"trainer": {
"class": "class:hypergan.trainers.alternating_trainer.AlternatingTrainer",
"d_optimizer": {
"class": "class:torch.optim.Adam",
"lr": 1e-4,
"betas":[0.0,0.999]
},
"g_optimizer": {
"class": "class:torch.optim.Adam",
"lr": 1e-4,
"betas":[0.0,0.999]
},
"hooks": [
{
"class": "function:hypergan.train_hooks.adversarial_norm_train_hook.AdversarialNormTrainHook",
"gamma": 1e3,
"loss": ["d"]
}
]
}
attribute | description | type |
g_optimizer | Optimizer configuration for G | Config (required) |
d_optimizer | Optimizer configuration for D | Config (required) |
hooks | Train Hooks | Array of configs (optional) |
train_d_every | train D every N steps | Integer (optional) |
train_g_every | train G every N steps | Integer (optional) |
pretrain_d | First N steps only trains D | Integer (optional) |
Last modified 2yr ago