iterations = 0
start = time.time()
best_dev_acc = -1
train_iter.repeat = False
header = ' Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss Accuracy Dev/Accuracy'
dev_log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'.split(','))
log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'.split(','))
os.makedirs(args.save_path, exist_ok=True)
print(header)
for epoch in range(args.epochs):
train_iter.init_epoch()
n_correct, n_total = 0, 0
for batch_idx, batch in enumerate(train_iter):
model.train(); opt.zero_grad()
iterations += 1
answer = model(batch)
n_correct += (torch.max(answer, 1)[1].view(batch.label.size()).data == batch.label.data).sum()
n_total += batch.batch_size
train_acc = 100. * n_correct/n_total
loss = criterion(answer, batch.label)
loss.backward(); opt.step()
if iterations % args.save_every == 0:
snapshot_prefix = os.path.join(args.save_path, 'snapshot')
snapshot_path = snapshot_prefix + '_acc_{:.4f}_loss_{:.6f}_iter_{}_model.pt'.format(train_acc, loss.data[0], iterations)
torch.save(model, snapshot_path)
for f in glob.glob(snapshot_prefix + '*'):
if f != snapshot_path:
os.remove(f)
if iterations % args.dev_every == 0:
dev_log_template = ' '.join(
'{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'
.split(','))
log_template = ' '.join(
'{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'
.split(','))
makedirs(args.save_path)
print(header)
for epoch in range(args.epochs):
train_iter.init_epoch()
n_correct, n_total = 0, 0
for batch_idx, batch in enumerate(train_iter):
# switch model to training mode, clear gradient accumulators
model.train()
opt.zero_grad()
iterations += 1
# forward pass
answer = model(batch)
# calculate accuracy of predictions in the current batch
n_correct += (torch.max(answer, 1)[1].view(
batch.label.size()) == batch.label).sum().item()
n_total += batch.batch_size
train_acc = 100. * n_correct / n_total
# calculate loss of the network output with respect to training labels
loss = criterion(answer, batch.label)
iterations = 0
start = time.time()
best_dev_acc = -1
header = ' Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss Accuracy Dev/Accuracy'
dev_log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'.split(','))
log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'.split(','))
makedirs(args.save_path)
print(header)
for epoch in range(args.epochs):
train_iter.init_epoch()
n_correct, n_total = 0, 0
for batch_idx, batch in enumerate(train_iter):
# switch model to training mode, clear gradient accumulators
model.train(); opt.zero_grad()
iterations += 1
# forward pass
answer = model(batch)
# calculate accuracy of predictions in the current batch
n_correct += (torch.max(answer, 1)[1].view(batch.label.size()) == batch.label).sum().item()
n_total += batch.batch_size
train_acc = 100. * n_correct/n_total
# calculate loss of the network output with respect to training labels
loss = criterion(answer, batch.label)
# backpropagate and update optimizer learning rate