HyperGAN
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Alternating Trainer

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

examples

"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"]
}
]
}

options

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)