Epoch: 0001 train_loss= 1.39081 train_acc= 0.26059 val_loss= 1.39169 val_acc= 0.19643 time= 0.12501
Epoch: 0002 train_loss= 1.38737 train_acc= 0.30945 val_loss= 1.39041 val_acc= 0.33929 time= 0.01563
Epoch: 0003 train_loss= 1.38474 train_acc= 0.36808 val_loss= 1.38990 val_acc= 0.33929 time= 0.01563
Epoch: 0004 train_loss= 1.38156 train_acc= 0.35179 val_loss= 1.38954 val_acc= 0.33929 time= 0.01562
Epoch: 0005 train_loss= 1.37872 train_acc= 0.35179 val_loss= 1.38927 val_acc= 0.33929 time= 0.01563
Epoch: 0006 train_loss= 1.37532 train_acc= 0.35505 val_loss= 1.38907 val_acc= 0.33929 time= 0.01562
Epoch: 0007 train_loss= 1.37370 train_acc= 0.35505 val_loss= 1.38904 val_acc= 0.33929 time= 0.02025
Epoch: 0008 train_loss= 1.36971 train_acc= 0.35505 val_loss= 1.38928 val_acc= 0.33929 time= 0.01150
Epoch: 0009 train_loss= 1.36944 train_acc= 0.35505 val_loss= 1.38980 val_acc= 0.33929 time= 0.01563
Epoch: 0010 train_loss= 1.36645 train_acc= 0.35505 val_loss= 1.39063 val_acc= 0.33929 time= 0.01563
Epoch: 0011 train_loss= 1.36639 train_acc= 0.35505 val_loss= 1.39174 val_acc= 0.33929 time= 0.01562
Epoch: 0012 train_loss= 1.36235 train_acc= 0.35505 val_loss= 1.39316 val_acc= 0.33929 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.37352 accuracy= 0.30973 time= 0.00000 
