def load_policy_model(model_dir="model/test_nlg_no_warm_up_with_nlu.pkl"):
# f.seek(0)
model = torch.load(model_dir)
net = Net(state_dim=dialog_config.STATE_DIM, num_actions=dialog_config.SYS_ACTION_CARDINALITY, config=config).to(device)
net.load_state_dict(model)
net.eval()
return net
def load_policy_model(model_dir):
model = torch.load(model_dir)
net = Net(state_dim=dialog_config.STATE_DIM,
num_actions=dialog_config.SYS_ACTION_CARDINALITY,
config=config).to(device)
net.load_state_dict(model)
net.eval()
return net
def load_policy_model(model_dir="model/test_nlg_no_warm_up_with_nlu.pkl"):
model = torch.load(model_dir)
net = Net(state_dim=dialog_config.STATE_DIM,
num_actions=dialog_config.SYS_ACTION_CARDINALITY,
config=config).to(device)
net.load_state_dict(model)
net.eval()
return net
if config.resume:
policy_net = load_policy_model(config.resume_rl_model_dir)
else:
policy_net = Net(state_dim=state_dim,
num_actions=num_actions,
config=config).to(device) #
# optimizer = optim.RMSprop(policy_net.parameters())
optimizer = optim.Adam(lr=config.lr,
params=policy_net.parameters(),
weight_decay=5e-5)
# net.optimizer = optim.Adam(params=net.parameters(), lr=5e-4, weight_decay=1e-3)
# net.lr_scheduler = optim.lr_scheduler.StepLR(net.optimizer, step_size=500, gamma=0.95)
# net.loss_func = nn.CrossEntropyLoss()
pg_reinforce = PolicyGradientREINFORCE(
optimizer=optimizer,
policy_network=policy_net,
state_dim=state_dim,
num_actions=num_actions,