Epoch: 0001 train_loss= 1.40137 train_acc= 0.23828 val_loss= 1.35163 val_acc= 0.30357 time= 0.45879
Epoch: 0002 train_loss= 1.39967 train_acc= 0.24805 val_loss= 1.36983 val_acc= 0.26786 time= 0.01800
Epoch: 0003 train_loss= 1.38142 train_acc= 0.29688 val_loss= 1.38593 val_acc= 0.26786 time= 0.01384
Epoch: 0004 train_loss= 1.37751 train_acc= 0.32227 val_loss= 1.39946 val_acc= 0.25000 time= 0.01562
Epoch: 0005 train_loss= 1.38704 train_acc= 0.30664 val_loss= 1.41261 val_acc= 0.26786 time= 0.00000
Epoch: 0006 train_loss= 1.38166 train_acc= 0.30664 val_loss= 1.42181 val_acc= 0.26786 time= 0.00000
Epoch: 0007 train_loss= 1.36814 train_acc= 0.32422 val_loss= 1.42905 val_acc= 0.26786 time= 0.01563
Epoch: 0008 train_loss= 1.37212 train_acc= 0.30664 val_loss= 1.43270 val_acc= 0.26786 time= 0.02547
Epoch: 0009 train_loss= 1.37835 train_acc= 0.30078 val_loss= 1.43551 val_acc= 0.26786 time= 0.01500
Epoch: 0010 train_loss= 1.37949 train_acc= 0.29297 val_loss= 1.43538 val_acc= 0.26786 time= 0.00364
Epoch: 0011 train_loss= 1.37266 train_acc= 0.30078 val_loss= 1.43402 val_acc= 0.26786 time= 0.02062
Epoch: 0012 train_loss= 1.37069 train_acc= 0.31445 val_loss= 1.43161 val_acc= 0.28571 time= 0.01100
Early stopping...
Optimization Finished!
Test set results: cost= 1.38859 accuracy= 0.28319 time= 0.01563 
