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train.py
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train.py
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import os
import sys
import torch
import torch.autograd as autograd
import torch.nn.functional as F
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def train(train_iter, dev_iter, model, args):
if args.cuda:
model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
steps = 0
best_acc = 0
last_step = 0
plotStep = []
trainLoss = []
valLoss = []
trainAcc = []
valAcc = []
model.train()
for epoch in range(1, args.epochs+1):
for batch in train_iter:
feature, target = batch.text, batch.label
#print(feature, feature.shape)
#os._exit(1)
with torch.no_grad():
feature = feature.data.t() # 转置，将[W, batch] 转化为[batch, W], W为词的个数
target = target.data.sub(1) # 因为label是1,2，所以要减一
#feature.data.t_(), target.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
optimizer.zero_grad()
logit = model(feature)
#print(feature.shape) # [64, 43] [batch, dim]
#print('logit vector', logit.size())
#print('target vector', target.size())
loss = F.cross_entropy(logit, target)
loss.backward()
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
corrects = (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
accuracy = 100.0 * corrects/batch.batch_size
sys.stdout.write(
'\rEpoch[{}] Batch[{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.format(epoch,
steps,
loss.item(),
accuracy,
corrects,
batch.batch_size))
if steps % args.test_interval == 0:
dev_loss, dev_acc = eval(dev_iter, model, args)
if dev_acc > best_acc:
best_acc = dev_acc
last_step = steps
if args.save_best:
save(model, args.save_dir, 'best', steps)
else:
if steps - last_step >= args.early_stop:
print('early stop by {} steps.'.format(args.early_stop))
plotStep.append(steps)
trainLoss.append(loss.item())
valLoss.append(dev_loss)
trainAcc.append(accuracy)
valAcc.append(dev_acc)
elif steps % args.save_interval == 0:
save(model, args.save_dir, 'snapshot', steps)
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(plotStep, trainLoss, label='Training Loss')
plt.plot(plotStep, valLoss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.subplot(1, 2, 2)
plt.plot(plotStep, trainAcc, label='Training Acc')
plt.plot(plotStep, valAcc, label='Validation Acc')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
jpg_file = os.path.join(args.save_dir, 'train_val.jpg')
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
os.mknod(jpg_file)
plt.savefig(jpg_file)
log_file = os.path.join(args.save_dir, 'log.txt')
with open(log_file, 'w+') as f:
f.write('Iter : ' + str(plotStep))
f.write('Train Acc: ' + str(trainAcc))
f.write('Val Acc: ' + str(valAcc))
f.write('Train Loss:' + str(trainLoss))
f.write('Val Acc: ' + str(valLoss))
f.write(str(args))
def eval(data_iter, model, args):
model.eval()
corrects, avg_loss = 0, 0
for batch in data_iter:
feature, target = batch.text, batch.label
#feature.data.t_(), target.data.sub_(1) # batch first, index align
with torch.no_grad():
feature = feature.data.t() # 转置，将[W, batch] 转化为[batch, W]
target = target.data.sub(1) # 因为label是1,2，所以要减一
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit = model(feature)
loss = F.cross_entropy(logit, target, size_average=False)
avg_loss += loss.item()
corrects += (torch.max(logit, 1)
[1].view(target.size()).data == target.data).sum()
size = len(data_iter.dataset)
avg_loss /= size
accuracy = 100.0 * corrects/size
print('\nEvaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \n'.format(avg_loss,
accuracy,
corrects,
size))
return avg_loss, accuracy
def predict(text, model, text_field, label_feild, cuda_flag):
assert isinstance(text, str)
model.eval()
#print(text)
# text = text_field.tokenize(text)
text = text_field.preprocess(text)
#print(text)
text = [[text_field.vocab.stoi[x] for x in text]]
#print(text)
#os._exit(1)
x = torch.tensor(text)
x = autograd.Variable(x)
if cuda_flag:
x = x.cuda()
#print(x)
output = model(x)
_, predicted = torch.max(output, 1)
#return label_feild.vocab.itos[predicted.data[0][0]+1]
return label_feild.vocab.itos[predicted.data[0]+1]
def save(model, save_dir, save_prefix, steps):
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
save_prefix = os.path.join(save_dir, save_prefix)
save_path = '{}_steps_{}.pt'.format(save_prefix, steps)
torch.save(model.state_dict(), save_path)