logger = logging.getLogger()
logger.info('[START]\n{}\n{}'.format(start_time, '=' * 30))
config.saved_path = saved_path
# save configuration
with open(os.path.join(saved_path, 'config.json'), 'w') as f:
json.dump(config, f, indent=4) # sort_keys=True
corpus = corpora.NormMultiWozCorpus(config)
train_dial, val_dial, test_dial = corpus.get_corpus()
train_data = BeliefDbDataLoaders('Train', train_dial, config)
val_data = BeliefDbDataLoaders('Val', val_dial, config)
test_data = BeliefDbDataLoaders('Test', test_dial, config)
evaluator = MultiWozEvaluator('SysWoz')
model = SysPerfectBD2Cat(corpus, config)
if config.use_gpu:
model.cuda()
best_epoch = None
if not config.forward_only:
try:
best_epoch = train(model,
train_data,
val_data,
test_data,
config,
evaluator,
logger = logging.getLogger()
logger.info('[START]\n{}\n{}'.format(start_time, '=' * 30))
config.saved_path = saved_path
# save configuration
with open(os.path.join(saved_path, 'config.json'), 'w') as f:
json.dump(config, f, indent=4) # sort_keys=True
corpus = corpora.NormMultiWozCorpus(config)
train_dial, val_dial, test_dial = corpus.get_corpus()
train_data = BeliefDbDataLoaders('Train', train_dial, config)
val_data = BeliefDbDataLoaders('Val', val_dial, config)
test_data = BeliefDbDataLoaders('Test', test_dial, config)
evaluator = MultiWozEvaluator('Deal')
model = SysPerfectBD2Word(corpus, config)
if config.use_gpu:
model.cuda()
best_epoch = None
if not config.forward_only:
try:
best_epoch = train(model,
train_data,
val_data,
test_data,
config,
evaluator,