def main():
# 参数设置
batch_size = 4
device = 'cuda' if torch.cuda.is_available() else 'cpu'
epochs = 10
learning_rate = 5e-6 #Learning Rate不宜太大
# 获取到dataset
train_dataset = CNewsDataset('data/cnews/cnews.train.txt')
valid_dataset = CNewsDataset('data/cnews/cnews.val.txt')
#test_data = load_data('cnews/cnews.test.txt')
# 生成Batch
train_dataloader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
valid_dataloader = DataLoader(valid_dataset,
batch_size=batch_size,
shuffle=False)
#test_dataloader = DataLoader(valid_data, batch_size=batch_size, shuffle=False)
# 读取BERT的配置文件
bert_config = BertConfig.from_pretrained('bert-base-chinese')
num_labels = len(train_dataset.labels)
# 初始化模型
model = BertClassifier(bert_config, num_labels).to(device)
optimizer = AdamW(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()
best_acc = 0
for epoch in range(1, epochs + 1):
losses = 0 # 损失
accuracy = 0 # 准确率
model.train()
train_bar = tqdm(train_dataloader)
for input_ids, token_type_ids, attention_mask, label_id in train_bar:
model.zero_grad()
train_bar.set_description('Epoch %i train' % epoch)
output = model(
input_ids=input_ids.to(device),
attention_mask=attention_mask.to(device),
token_type_ids=token_type_ids.to(device),
)
loss = criterion(output, label_id.to(device))
losses += loss.item()
pred_labels = torch.argmax(output, dim=1) # 预测出的label
acc = torch.sum(pred_labels == label_id.to(device)).item() / len(
pred_labels) #acc
accuracy += acc
loss.backward()
optimizer.step()
train_bar.set_postfix(loss=loss.item(), acc=acc)
average_loss = losses / len(train_dataloader)
average_acc = accuracy / len(train_dataloader)
print('\tTrain ACC:', average_acc, '\tLoss:', average_loss)
# 验证
model.eval()
losses = 0 # 损失
accuracy = 0 # 准确率
valid_bar = tqdm(valid_dataloader)
for input_ids, token_type_ids, attention_mask, label_id in valid_bar:
valid_bar.set_description('Epoch %i valid' % epoch)
output = model(
input_ids=input_ids.to(device),
attention_mask=attention_mask.to(device),
token_type_ids=token_type_ids.to(device),
)
loss = criterion(output, label_id.to(device))
losses += loss.item()
pred_labels = torch.argmax(output, dim=1) # 预测出的label
acc = torch.sum(pred_labels == label_id.to(device)).item() / len(
pred_labels) #acc
accuracy += acc
valid_bar.set_postfix(loss=loss.item(), acc=acc)
average_loss = losses / len(valid_dataloader)
average_acc = accuracy / len(valid_dataloader)
print('\tValid ACC:', average_acc, '\tLoss:', average_loss)
if average_acc > best_acc:
best_acc = average_acc
torch.save(model.state_dict(), 'models/best_model.pkl')
def main():
device = torch.device('cuda:3')
# 获取到dataset
print('加载训练数据')
train_data = load_data('dataset/train.csv')
print('加载验证数据')
valid_data = load_data('dataset/test.csv')
# test_data = load_data('cnews/cnews.test.txt')
batch_size = 16
# 生成Batch
print('生成batch')
train_dataloader = DataLoader(train_data,
batch_size=batch_size,
shuffle=True,
num_workers=3)
valid_dataloader = DataLoader(valid_data,
batch_size=batch_size,
shuffle=False,
num_workers=3)
# test_dataloader = DataLoader(valid_data, batch_size=batch_size, shuffle=False)
# 读取BERT的配置文件
bert_config = BertConfig.from_pretrained('./chinese_wwm_pytorch')
bert_config.num_labels = num_labels
print(bert_config)
# 初始化模型
model = BertClassifier(bert_config)
# model.to(device)
# 参数设置
EPOCHS = 20
learning_rate = 5e-6 # Learning Rate不宜太大
optimizer = AdamW(model.parameters(), lr=learning_rate)
# 损失函数采用交叉熵
criterion = nn.CrossEntropyLoss()
with open('output.txt', 'w') as wf:
wf.write('Batch Size: ' + str(batch_size) + '\tLearning Rate: ' +
str(learning_rate) + '\n')
best_acc = 0
# 设置并行训练,模型默认是把参数放在device[0]对应的gpu编号的gpu上，所以这里应该和上面设置的cuda:2对应
net = torch.nn.DataParallel(model, device_ids=[3, 4])
net.to(device)
# model.module.avgpool = nn.AdaptiveAvgPool2d(7)
# 开始训练
for Epoch in range(1, EPOCHS + 1):
losses = 0 # 损失
accuracy = 0 # 准确率
print('Epoch:', Epoch)
model.train()
for batch_index, batch in enumerate(train_dataloader):
# print(batch_index)
# print(batch)
input_ids = batch[0].to(device)
attention_mask = batch[1].to(device)
token_type_ids = batch[2].to(device)
label_ids = batch[3].to(device)
# 将三个输入喂到模型中
output = net( # forward
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
)
loss = criterion(output, label_ids)
losses += loss.item()
pred_labels = torch.argmax(output, dim=1) # 预测出的label
acc = torch.sum(pred_labels == label_ids.to(device)).item() / len(
pred_labels) # acc
accuracy += acc
# 打印训练过程中的准确率以及loss
# print('Epoch: %d ｜ Train: | Batch: %d / %d | Acc: %f | Loss: %f' % (Epoch, batch_index + 1, len(train_dataloader), acc, loss.item()))
# 模型梯度置零，损失函数反向传播，优化更新
model.zero_grad()
loss.backward()
optimizer.step()
# torch.cuda.empty_cache()
average_loss = losses / len(train_dataloader)
average_acc = accuracy / len(train_dataloader)
# 打印该epoch训练结果的
print('\tTrain ACC:', average_acc, '\tLoss:', average_loss)
# with open('output.txt', 'a') as rf:
# output_to_file = '\nEpoch: ' + str(Epoch) + '\tTrain ACC:' + str(average_acc) + '\tLoss: ' + str(
# average_loss)
# rf.write(output_to_file)
# 验证
model.eval()
losses = 0 # 损失
accuracy = 0 # 准确率
# 在验证集上进行验证
for batch_index, batch in enumerate(valid_dataloader):
input_ids = batch[0].to(device)
attention_mask = batch[1].to(device)
token_type_ids = batch[2].to(device)
label_ids = batch[3].to(device)
with torch.no_grad():
output = model( # forward
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
)
loss = criterion(output, label_ids)
losses += loss.item()
# 这里的两部操作都是直接对生成的结果张量进行操作
pred_labels = torch.argmax(output, dim=1) # 预测出的label
acc = torch.sum(pred_labels == label_ids.to(device)).item() / len(
pred_labels) # acc
accuracy += acc
average_loss = losses / len(valid_dataloader)
average_acc = accuracy / len(valid_dataloader)
print('\tValid ACC:', average_acc, '\tLoss:', average_loss)
# with open('output.txt', 'a') as rf:
# output_to_file = '\nEpoch: ' + str(Epoch) + '\tValid ACC:' + str(average_acc) + '\tLoss: ' + str(
# average_loss) + '\n'
# rf.write(output_to_file)
if average_acc > best_acc:
best_acc = average_acc
torch.save(model.state_dict(), 'best_model_on_trainset.pkl')
