def train():
# 配置文件
cf = Config('./config.yaml')
# 有GPU用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 训练数据
train_data = NewsDataset("./data/cnews_final_train.txt", cf.max_seq_len)
train_dataloader = DataLoader(train_data,
batch_size=cf.batch_size,
shuffle=True)
# 测试数据
test_data = NewsDataset("./data/cnews_final_test.txt", cf.max_seq_len)
test_dataloader = DataLoader(test_data,
batch_size=cf.batch_size,
shuffle=True)
# 预训练词向量矩阵
embedding_matrix = get_pre_embedding_matrix("./data/final_vectors")
# 模型
model = TextRNN(cf, torch.tensor(embedding_matrix))
# 优化器用adam
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()))
# 把模型放到指定设备
model.to(device)
# 让模型并行化运算
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# 训练
start_time = time.time()
total_batch = 0 # 总批次
best_acc_val = 0.0 # 最佳验证集准确率
last_improved = 0 # 记录上一次提升批次
require_improvement = 1000 # 如果超过1000轮未提升，提前结束训练
flag = False
model.train()
for epoch_id in trange(cf.epoch, desc="Epoch"):
# for step,batch in enumerate(tqdm(train_dataloader,"batch",total=len(train_dataloader))):
for step, batch in enumerate(train_dataloader):
label_id = batch['label_id'].squeeze(1).to(device)
seq_len = batch["seq_len"].to(device)
segment_ids = batch['segment_ids'].to(device)
# 将序列按长度降序排列
seq_len, perm_idx = seq_len.sort(0, descending=True)
label_id = label_id[perm_idx]
segment_ids = segment_ids[perm_idx].transpose(0, 1)
loss = model(segment_ids, seq_len, label_id)
loss.backward()
optimizer.step()
optimizer.zero_grad()
total_batch += 1
if total_batch % cf.print_per_batch == 0:
model.eval()
with torch.no_grad():
loss_train, acc_train = model.get_loss_acc(
segment_ids, seq_len, label_id)
loss_val, acc_val = evaluate(model, test_dataloader, device)
if acc_val > best_acc_val:
# 保存最好结果
best_acc_val = acc_val
last_improved = total_batch
torch.save(model.state_dict(), "./output/model.bin")
improved_str = "*"
else:
improved_str = ""
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},' \
+ ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, Time: {5} {6}'
print(
msg.format(total_batch, loss_train, acc_train, loss_val,
acc_val, time_dif, improved_str))
model.train()
if total_batch - last_improved > require_improvement:
print("长时间未优化")
flag = True
break
if flag:
break
def test():
# 配置文件
cf = Config('./config.yaml')
# 有GPU用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 测试数据
test_data = NewsDataset("./data/cnews_final_test.txt", cf.max_seq_len)
test_dataloader = DataLoader(test_data,
batch_size=cf.batch_size,
shuffle=True)
# 预训练词向量矩阵
embedding_matrix = get_pre_embedding_matrix("./data/final_vectors")
# 模型
model = TextRNN(cf, torch.tensor(embedding_matrix))
# model.load_state_dict(torch.load("./output/model.bin",map_location='cpu'))
model.load_state_dict(torch.load("./output/model.bin"))
# 把模型放到指定设备
model.to(device)
# 让模型并行化运算
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# 训练
start_time = time.time()
data_len = len(test_dataloader)
model.eval()
y_pred = np.array([])
y_test = np.array([])
# for step,batch in enumerate(tqdm(test_dataloader,"batch",total=len(test_dataloader))):
for step, batch in enumerate(test_dataloader):
label_id = batch['label_id'].squeeze(1).to(device)
seq_len = batch["seq_len"].to(device)
segment_ids = batch['segment_ids'].to(device)
# 将序列按长度降序排列
seq_len, perm_idx = seq_len.sort(0, descending=True)
label_id = label_id[perm_idx]
segment_ids = segment_ids[perm_idx].transpose(0, 1)
with torch.no_grad():
pred = model.get_labels(segment_ids, seq_len)
y_pred = np.hstack((y_pred, pred))
y_test = np.hstack((y_test, label_id.to("cpu").numpy()))
# 评估
print("Precision, Recall and F1-Score...")
print(
metrics.classification_report(y_test,
y_pred,
target_names=get_labels('./data/label')))
# 混淆矩阵
print("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test, y_pred)
print(cm)