Alternating Trainer

Source: /trainers/alternating.py​
d_grads = self.calculate_gradients(D)
self.train_d(d_grads)
g_grads = self.calculate_gradients(G)
self.train_g(g_grads)
examples
Configurations: /trainers/alternating_trainer/​
"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)

Components - Previous

Trainer

Simultaneous Trainer

Last updated 3 months ago

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)