Epoch: 0001 train_loss= 2.07351 train_acc= 0.16352 val_loss= 2.12799 val_acc= 0.03448 time= 0.31273
Epoch: 0002 train_loss= 2.04170 train_acc= 0.16981 val_loss= 2.11869 val_acc= 0.06897 time= 0.01563
Epoch: 0003 train_loss= 2.05791 train_acc= 0.17610 val_loss= 2.11445 val_acc= 0.03448 time= 0.01563
Epoch: 0004 train_loss= 2.03048 train_acc= 0.23270 val_loss= 2.11849 val_acc= 0.03448 time= 0.01563
Epoch: 0005 train_loss= 2.03278 train_acc= 0.18868 val_loss= 2.12859 val_acc= 0.03448 time= 0.00000
Epoch: 0006 train_loss= 2.03331 train_acc= 0.18868 val_loss= 2.13924 val_acc= 0.03448 time= 0.00000
Epoch: 0007 train_loss= 2.05265 train_acc= 0.19497 val_loss= 2.14797 val_acc= 0.03448 time= 0.01563
Epoch: 0008 train_loss= 2.03440 train_acc= 0.17610 val_loss= 2.15819 val_acc= 0.03448 time= 0.01563
Epoch: 0009 train_loss= 2.04255 train_acc= 0.18239 val_loss= 2.16402 val_acc= 0.03448 time= 0.01563
Epoch: 0010 train_loss= 2.02468 train_acc= 0.20126 val_loss= 2.16537 val_acc= 0.03448 time= 0.01562
Epoch: 0011 train_loss= 2.02428 train_acc= 0.20126 val_loss= 2.16466 val_acc= 0.03448 time= 0.00000
Epoch: 0012 train_loss= 2.03021 train_acc= 0.20755 val_loss= 2.16161 val_acc= 0.03448 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.11476 accuracy= 0.18644 time= 0.01563 
