def __init__(self, is_train=False, dataset='Multiwoz'):
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json'), 'r') as f:
cfg = json.load(f)
self.save_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['save_dir'])
self.save_per_epoch = cfg['save_per_epoch']
self.update_round = cfg['update_round']
self.optim_batchsz = cfg['batchsz']
self.gamma = cfg['gamma']
self.epsilon = cfg['epsilon']
self.tau = cfg['tau']
self.is_train = is_train
if is_train:
init_logging_handler(os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['log_dir']))
# construct policy and value network
if dataset == 'Multiwoz':
voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt')
self.vector = MultiWozVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'], self.vector.da_dim).to(device=DEVICE)
self.value = Value(self.vector.state_dim, cfg['hv_dim']).to(device=DEVICE)
if is_train:
self.policy_optim = optim.RMSprop(self.policy.parameters(), lr=cfg['lr'])
self.value_optim = optim.Adam(self.value.parameters(), lr=cfg['lr'])
def __init__(self, is_train=False):
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
cfg = json.load(f)
self.save_dir = cfg['save_dir']
self.save_per_epoch = cfg['save_per_epoch']
self.update_round = cfg['update_round']
self.optim_batchsz = cfg['batchsz']
self.gamma = cfg['gamma']
self.is_train = is_train
if is_train:
init_logging_handler(cfg['log_dir'])
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'],
self.vector.da_dim).to(device=DEVICE)
if is_train:
self.policy_optim = optim.RMSprop(self.policy.parameters(),
lr=cfg['lr'])
prec = a_TP / (a_TP + a_FP)
rec = a_TP / (a_TP + a_FN)
F1 = 2 * prec * rec / (prec + rec)
print(a_TP, a_FP, a_FN, F1)
print(t_corr, t_tot, t_corr / t_tot)
def save(self, directory, epoch):
if not os.path.exists(directory):
os.makedirs(directory)
torch.save(self.user.state_dict(),
directory + '/' + str(epoch) + '_simulator.mdl')
logging.info(
'<<user simulator>> epoch {}: saved network to mdl'.format(epoch))
if __name__ == '__main__':
with open('config.json', 'r') as f:
cfg = json.load(f)
init_logging_handler(cfg['log_dir'])
env = VHUS_Trainer(cfg)
logging.debug('start training')
best = float('inf')
for e in range(cfg['epoch']):
env.imitating(e)
best = env.imit_test(e, best)