def train(args):
train_iter, dev_iter = data_processor.load_data(args) # 将数据分为训练集和验证集
print('加载数据完成')
model = TextRNN(args)
Cuda = torch.cuda.is_available()
if Cuda and args.cuda:
model.cuda()
"""
Q5:
Please give optimizer here
Add lr_scheduler to adjust learning rate.
"""
optimizer = torch.optim.Adam(model.parameters(), lr = args.lr)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.8)
steps = 0
best_acc = 0
last_step = 0
model.train()
for epoch in range(1, args.epoch + 1):
for batch in train_iter:
feature, target = batch.text, batch.label
# t_()函数表示将(max_len, batch_size)转置为(batch_size, max_len)
with torch.no_grad():
feature.t_(), target.sub_(1) # target减去1
if args.cuda and Cuda:
feature, target = feature.cuda(), target.cuda()
optimizer.zero_grad()
logits = model(feature)
loss = F.cross_entropy(logits, target)
loss.backward()
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
# torch.max(logits, 1)函数：返回每一行中最大值的那个元素，且返回其索引（返回最大元素在这一行的列索引）
corrects = (torch.max(logits, 1)[1] == target).sum()
train_acc = 100.0 * corrects / batch.batch_size
sys.stdout.write(
'\rBatch[{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.format(steps,
loss.item(),
train_acc,
corrects,
batch.batch_size))
if steps % args.test_interval == 0:
dev_acc = eval(dev_iter, model, args)
if dev_acc > best_acc:
best_acc = dev_acc
last_step = steps
if args.save_best:
print('Saving best model, acc: {:.4f}%\n'.format(best_acc))
save(model, args.save_dir, 'best', steps)
else:
scheduler.step()
print('lr decayed to {}'.format(optimizer.state_dict()['param_groups'][0]['lr']))
if steps - last_step >= args.early_stopping:
print('\nearly stop by {} steps, acc: {:.4f}%'.format(args.early_stopping, best_acc))
raise KeyboardInterrupt
def train(args):
train_iter, dev_iter = data_processor.load_data(args) # 将数据分为训练集和验证集
print('加载数据完成')
model = TextRNN(args)
if args.cuda: model.cuda()
"""
Q5:
Please give optimizer here
"""
optimizer = torch.optim.Adam(model.parameters())
steps = 0
best_acc = 0
last_step = 0
model.train()
for epoch in range(1, args.epoch + 1):
for batch in train_iter:
feature, target = batch.text, batch.label
# t_()函数表示将(max_len, batch_size)转置为(batch_size, max_len)
with torch.no_grad():
#feature.t_()
target.sub_(1) # target减去1
#print(feature.shape)
if args.cuda:
feature, target = feature.cuda(), target.cuda()
optimizer.zero_grad()
logits = model(feature)
#print(logits.shape)
loss = F.cross_entropy(logits, target)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1)
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
# torch.max(logits, 1)函数：返回每一行中最大值的那个元素，且返回其索引（返回最大元素在这一行的列索引）
corrects = (torch.max(logits, 1)[1] == target).sum()
train_acc = 100.0 * corrects / batch.batch_size
sys.stdout.write(
'\rBatch[{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.format(
steps, loss.item(), train_acc, corrects,
batch.batch_size))
if steps % args.test_interval == 0:
dev_acc = eval(dev_iter, model, args)
if dev_acc > best_acc:
best_acc = dev_acc
last_step = steps
if args.save_best:
print('Saving best model, acc: {:.4f}%\n'.format(
best_acc))
save(model, args.save_dir, 'best', steps)
else:
if steps - last_step >= args.early_stopping:
print('\nearly stop by {} steps, acc: {:.4f}%'.format(
args.early_stopping, best_acc))
raise KeyboardInterrupt