patience = 100
best_loss = 10000000
stop_count = 0
# Main loop
for meta_iteration in range(config.epochs):
## save original weights to make the update
weights_original = deepcopy(meta_net.state_dict())
train_loss_before = []
train_loss_meta = []
#loss accumulate from a batch of tasks
batch_loss = 0
for _ in range(meta_batch_size):
# Get task
if config.fix_dialnum_train:
train_iter, val_iter = p.get_balanced_loader(
persona=tasks_iter.__next__(),
batch_size=config.batch_size,
split='train')
else:
train_iter, val_iter = p.get_data_loader(
persona=tasks_iter.__next__(),
batch_size=config.batch_size,
split='train')
#before first update
v_loss, v_ppl = do_evaluation(meta_net, val_iter)
train_loss_before.append(math.exp(v_loss))
# Update fast nets
val_loss, v_ppl = do_learning_fix_step(
meta_net, train_iter, val_iter, iterations=config.meta_iteration)
train_loss_meta.append(math.exp(val_loss.item()))
batch_loss += val_loss
# log
# clip gradient
nn.utils.clip_grad_norm_(meta_net.parameters(),
config.max_grad_norm)
meta_optimizer.step()
print('Meta_iteration:', meta_iteration)
val_loss_before = []
val_loss_meta = []
weights_original = deepcopy(meta_net.state_dict())
for idx, per in enumerate(p.get_personas('valid')):
#num_of_dialog = p.get_num_of_dialog(persona=per, split='valid')
#for dial_i in range(num_of_dialog):
if config.fix_dialnum_train:
train_iter, val_iter = p.get_balanced_loader(
persona=per,
batch_size=config.batch_size,
split='valid',
fold=0)
else:
train_iter, val_iter = p.get_data_loader(
persona=per,
batch_size=config.batch_size,
split='valid',
fold=0)
# zero shot result
loss, ppl = do_evaluation(meta_net, val_iter)
val_loss_before.append(math.exp(loss))
# meta tuning
val_loss, val_ppl = do_learning_fix_step(
meta_net, train_iter, val_iter, iterations=config.meta_iteration)
# Build model, optimizer, and set states
print("Test model", config.model)
model = Transformer(p.vocab, model_file_path=config.save_path, is_eval=False)
fine_tune = []
iter_per_task = []
iterations = 11
weights_original = deepcopy(model.state_dict())
tasks = p.get_personas('test')
for per in tqdm(tasks):
num_of_dialog = p.get_num_of_dialog(persona=per, split='test')
for val_dial_index in range(num_of_dialog):
if config.fix_dialnum_train:
train_iter, val_iter = p.get_balanced_loader(
persona=per,
batch_size=config.batch_size,
split='test',
fold=val_dial_index,
dial_num=config.k_shot)
else:
train_iter, val_iter = p.get_data_loader(
persona=per,
batch_size=config.batch_size,
split='test',
fold=val_dial_index)
logger = do_learning(model,
train_iter,
val_iter,
iterations=iterations)
fine_tune.append(logger)
model.load_state_dict(