loss = loss.cpu()
optimizer.step()
losses += loss
# logging
if (i + 1) % 500 == 0:
log.info('accuracy: %d%%' % (correct_cnt // 5))
correct_cnt = 0
log.info('Train time ' + \
time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)) + \
', ' + 'Mean loss: %0.4f' % (losses.data.numpy()[0] / i))
# save model
if args.gpu:
model = model.cpu()
state_to_save = model.state_dict()
if args.epoch % 10 == 0:
torch.save(
state_to_save,
os.path.join(args.model_dir, 'epoch%d.dat' % args.epoch))
accuracy = test(args, model, dataset_test, targets_test, log_test)
f_accuracy_train.write('%0.2f\n' %
(100 * correct_cnt_sum / targets.shape[0]))
f_accuracy_test.write('%0.2f\n' % (100 * accuracy))
f_loss.write('%0.4f\n' %
(losses.data.numpy()[0] / targets.shape[0]))
if accuracy > max_accuracy:
max_accuracy = accuracy
overfitting_cnt = 0
torch.save(state_to_save,
os.path.join(args.model_dir, 'best_model.dat'))
