Epoch: 0001 train_loss= 2.08735 train_acc= 0.11950 val_loss= 2.08507 val_acc= 0.10345 time= 0.15626
Epoch: 0002 train_loss= 2.08480 train_acc= 0.14465 val_loss= 2.08332 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08270 train_acc= 0.13208 val_loss= 2.08191 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.08035 train_acc= 0.13836 val_loss= 2.08082 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.07889 train_acc= 0.13836 val_loss= 2.07988 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.07661 train_acc= 0.14465 val_loss= 2.07914 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.07490 train_acc= 0.14465 val_loss= 2.07856 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.07324 train_acc= 0.13836 val_loss= 2.07821 val_acc= 0.10345 time= 0.01563
Epoch: 0009 train_loss= 2.07093 train_acc= 0.15094 val_loss= 2.07808 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.06787 train_acc= 0.14465 val_loss= 2.07826 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.06810 train_acc= 0.15094 val_loss= 2.07861 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.06456 train_acc= 0.14465 val_loss= 2.07921 val_acc= 0.10345 time= 0.01563
Epoch: 0013 train_loss= 2.06490 train_acc= 0.16352 val_loss= 2.07990 val_acc= 0.10345 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.07926 accuracy= 0.08475 time= 0.00000 
