Epoch: 0001 train_loss= 2.08210 train_acc= 0.16981 val_loss= 2.10685 val_acc= 0.10345 time= 0.56302
Epoch: 0002 train_loss= 2.08023 train_acc= 0.16604 val_loss= 2.10650 val_acc= 0.10345 time= 0.00000
Epoch: 0003 train_loss= 2.07785 train_acc= 0.16604 val_loss= 2.10624 val_acc= 0.10345 time= 0.01562
Epoch: 0004 train_loss= 2.07586 train_acc= 0.14717 val_loss= 2.10624 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.07257 train_acc= 0.15094 val_loss= 2.10658 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.06809 train_acc= 0.15472 val_loss= 2.10721 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.06872 train_acc= 0.14340 val_loss= 2.10814 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.06511 train_acc= 0.15094 val_loss= 2.10946 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.06348 train_acc= 0.15849 val_loss= 2.11118 val_acc= 0.06897 time= 0.01563
Epoch: 0010 train_loss= 2.06154 train_acc= 0.16604 val_loss= 2.11342 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.05875 train_acc= 0.16604 val_loss= 2.11607 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.05975 train_acc= 0.13208 val_loss= 2.11924 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07065 accuracy= 0.20339 time= 0.00000 
