Balanced Trainer
fake, real = self.gan.forward_discriminator()
if d_real < (fake+config.imbalance):
self.train_d()
else:
self.train_g()
{
"class": "class:hypergan.trainers.balanced_trainer.BalancedTrainer",
"imbalance": 0.06,
"pretrain_d": 1000,
"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": 2e4,
"loss": ["d"]
},
{
"class": "function:hypergan.train_hooks.initialize_as_autoencoder.InitializeAsAutoencoder",
"steps": 10000,
"optimizer": {
"class": "class:torch.optim.Adam",
"lr": 1e-4,
"betas":[0.9,0.999]
},
"encoder": {
"class": "class:hypergan.discriminators.configurable_discriminator.ConfigurableDiscriminator",
"layers":[
"conv 32 stride=1", "adaptive_avg_pool", "relu",
"conv 64 stride=1", "adaptive_avg_pool", "relu",
"conv 128 stride=1", "adaptive_avg_pool", "relu",
"conv 256 stride=1", "adaptive_avg_pool", "relu",
"conv 512 stride=1", "adaptive_avg_pool", "relu",
"conv 512 stride=1", "adaptive_avg_pool", "relu",
"flatten",
"linear 256 bias=false", "tanh"
]
}
}
]
}
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) |
pretrain_d | First N steps only trains D | Integer (optional) |
imbalance | Threshold distance for G training. Defaults to 0.1 | Float (optional) |
d_fake_balance | Changes conditional to d_fake(t) > d_fake(t-1) | Boolean (optional) |
Last modified 2yr ago