Epoch: 0001 train_loss= 2.08693 train_acc= 0.07925 val_loss= 2.08890 val_acc= 0.17241 time= 0.20313
Epoch: 0002 train_loss= 2.08476 train_acc= 0.12075 val_loss= 2.08944 val_acc= 0.17241 time= 0.01562
Epoch: 0003 train_loss= 2.08316 train_acc= 0.14340 val_loss= 2.09013 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.08151 train_acc= 0.20755 val_loss= 2.09126 val_acc= 0.17241 time= 0.01563
Epoch: 0005 train_loss= 2.08015 train_acc= 0.14340 val_loss= 2.09260 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07880 train_acc= 0.19623 val_loss= 2.09418 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.07699 train_acc= 0.16604 val_loss= 2.09596 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.07487 train_acc= 0.16981 val_loss= 2.09790 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.07352 train_acc= 0.17358 val_loss= 2.09999 val_acc= 0.10345 time= 0.01563
Epoch: 0010 train_loss= 2.07127 train_acc= 0.16226 val_loss= 2.10221 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.06996 train_acc= 0.17736 val_loss= 2.10452 val_acc= 0.10345 time= 0.01563
Epoch: 0012 train_loss= 2.06892 train_acc= 0.16226 val_loss= 2.10681 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.09459 accuracy= 0.08475 time= 0.00000 
