def test_one_data(model, item, th, n_episode=DefaultConfig.n_episode):
''' 测试一条数据
Args:
model -- DMN模型
item -- [facts, question, answer]
th -- TextHelper
Returns:
None
'''
# batch_size = 1
model.eval()
item = [item]
facts, facts_mask, question, question_mask, answer = pad_batch_data(
item, th)
preds = model(facts, facts_mask, question, question_mask, answer.size(1),
n_episode)
item = item[0]
preds = preds.max(1)[1].data.tolist()
fact = item[0][0]
facts = [th.indices2sentence(fact) for fact in item[0]]
facts = [" ".join(fact) for fact in facts]
q = " ".join(th.indices2sentence(item[1]))
a = " ".join(th.indices2sentence(item[2]))
preds = " ".join(th.indices2sentence(preds))
print("Facts:")
print("\n".join(facts))
print("Question:", q)
print("Answer:", a)
print("Predict:", preds)
print()
def train(opt, th, train_data):
''' 训练
Args:
opt -- 配置信息
th -- TextHelper实例
train_data -- 训练数据，[[facts, question, answer]]
'''
# 加载原始数据
seqbegin_id = th.word2index(th.seq_begin)
model = DMN(th.vocab_size, opt.embed_size, opt.hidden_size, seqbegin_id,
th.word2index(th.pad))
if opt.use_cuda:
model = model.cuda(opt.device_id)
optimizer = optim.Adam(model.parameters(), lr=opt.learning_rate)
loss_func = nn.CrossEntropyLoss(ignore_index=th.word2index(th.pad))
for e in range(opt.max_epoch):
losses = []
for batch_data in get_data_loader(train_data, opt.batch_size,
opt.shuffle):
# batch内的数据进行pad，转成Variable
allfacts, allfacts_mask, questions, questions_mask, answers = \
pad_batch_data(batch_data, th)
# 前向
preds = model(allfacts, allfacts_mask, questions, questions_mask,
answers.size(1), opt.n_episode)
# loss
optimizer.zero_grad()
loss = loss_func(preds, answers.view(-1))
losses.append(loss.data.tolist()[0])
# 反向
loss.backward()
optimizer.step()
avg_loss = np.mean(losses)
if avg_loss <= opt.min_loss or e % opt.print_every_epoch == 0 or e == opt.max_epoch - 1:
info = "e={}, loss={}".format(e, avg_loss)
print(info)
if e == opt.max_epoch - 1 and avg_loss > opt.min_loss:
print("epoch finish, loss > min_loss")
torch.save(model, opt.model_path)
break
elif avg_loss <= opt.min_loss:
print("Early stop")
torch.save(model, opt.model_path)
break
def cal_test_accuracy(model, test_data, th, n_episode=DefaultConfig.n_episode):
'''测试，测试数据'''
batch_size = 1
model.eval()
correct = 0
for item in get_data_loader(test_data, batch_size, False):
facts, facts_mask, question, question_mask, answer = pad_batch_data(item, th)
preds = model(facts, facts_mask, question, question_mask, answer.size(1), n_episode)
#print (answer.data.shape, preds.data.shape)
preds = preds.max(1)[1].data.tolist()
answer = answer.view(-1).data.tolist()
if preds == answer:
correct += 1
print ("acccuracy = ", correct / len(test_data))