Epoch: 0001 train_loss= 2.09083 train_acc= 0.11950 val_loss= 2.09424 val_acc= 0.17241 time= 0.21877
Epoch: 0002 train_loss= 2.08674 train_acc= 0.11321 val_loss= 2.09232 val_acc= 0.17241 time= 0.01562
Epoch: 0003 train_loss= 2.08444 train_acc= 0.10692 val_loss= 2.09061 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.08259 train_acc= 0.12579 val_loss= 2.08934 val_acc= 0.17241 time= 0.00000
Epoch: 0005 train_loss= 2.07937 train_acc= 0.12579 val_loss= 2.08852 val_acc= 0.17241 time= 0.01563
Epoch: 0006 train_loss= 2.07813 train_acc= 0.13836 val_loss= 2.08802 val_acc= 0.20690 time= 0.00000
Epoch: 0007 train_loss= 2.07610 train_acc= 0.15723 val_loss= 2.08802 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.07255 train_acc= 0.15094 val_loss= 2.08835 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.07100 train_acc= 0.16981 val_loss= 2.08911 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.06927 train_acc= 0.16352 val_loss= 2.09026 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.07017 train_acc= 0.14465 val_loss= 2.09156 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.06522 train_acc= 0.15723 val_loss= 2.09302 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08193 accuracy= 0.16949 time= 0.00000 
