def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
total_n = 0
lr = args.lr * (0.01**(epoch // 50))
t_sets.set_lr(optimizer, lr)
# pbar = progressbar.ProgressBar(max_value=n_traindata//train_batch_size)
for batch_idx, (inputs, targets) in enumerate(trainloader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
optimizer.zero_grad()
inputs, targets = Variable(inputs), Variable(targets)
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.data[0]
print("Trainloss: {:.02f}".format(loss.data[0]))
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
# pbar.update(batch_idx)
total_n = batch_idx + 1
# Save checkpoint.
t_acc = 100. * correct / total
t_loss = train_loss / (total_n)
w_line = '\nTrain:\t{:d}\t{:.04f}\t{:.04f}\tLR {:0.6f}\n'.format(
epoch, t_loss, t_acc, lr)
print(w_line)
def train(epoch):
print('\nEpoch: %d' % epoch)
model.train()
train_loss = 0
correct = 0
total = 0
total_n = 0
# before it was 150, 225 and 300
lr = args.lr * (0.1 ** (epoch // 150)) * (0.1 ** (epoch // 225))
t_sets.set_lr(optimizer, lr)
if args.verbose:
pbar = progressbar.ProgressBar(max_value=n_traindata//train_batch_size)
# acc_batch_loss = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
# if batch_idx % update_rate ==0:
optimizer.zero_grad()
# acc_batch_loss = 0
inputs, targets = Variable(inputs), Variable(targets)
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
# acc_batch_loss += loss.cpu().numpy()[0]
# if batch_idx % update_rate ==0:
optimizer.step()
train_loss += loss.data[0]
# print("Trainloss: {:.02f}".format(loss.data[0]))
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
if args.verbose:
pbar.update(batch_idx)
total_n = batch_idx + 1
if p_constraint and positive_clipper.frequency % total_n == 0:
model.apply(positive_clipper)
# Save checkpoint.
t_acc = 100. * correct / total
t_loss = train_loss / (total_n)
w_line = '\nTrain:\t {:d}\tLoss: \tAcc: {:.04f}\t{:.04f}\tLR {:0.6f}'.format(epoch, t_loss, t_acc, lr)
print (w_line)
result_file = os.path.join(save_dir, 'ckpt_result.txt')
if not os.path.isfile(result_file):
with open(result_file, 'w') as f:
f.write(w_line)
else:
with open(result_file, 'a') as f:
f.write(w_line)
sys.stdout.flush()