dev_set.init_dataset(target_label)
test_set.init_dataset(target_label)
(
dev_tids, dev_tokens, dev_labels, dev_lens
) = dev_set.get_dataset(max_seq_len, volatile=True, gpu=use_gpu)
(
test_tids, test_tokens, test_labels, test_lens
) = test_set.get_dataset(max_seq_len, volatile=True, gpu=use_gpu)
best_dev_fscore = 0.0
best_test_scores = None
for epoch in range(max_epoch):
epoch_start_time = current_time()
epoch_loss = 0.0
train_set.shuffle_dataset(target_label, balance=True)
batch_num = train_set.batch_num(batch_size)
for batch_idx in range(batch_num):
optimizer.zero_grad()
(
batch_tids, batch_tokens, batch_labels, batch_lens
) = train_set.get_batch(batch_size, gpu=use_gpu)
model_output = model.forward(batch_tokens, batch_lens)
loss = loss_func.forward(model_output, batch_labels)
loss.backward()
optimizer.step()
epoch_loss += 1.0 / batch_num * float(loss)
epoch_elapsed_time = current_time() - epoch_start_time
# Evaluate the current model on dev and test sets
dev_preds = model.forward(dev_tokens, dev_lens)
