Epoch: 0001 train_loss= 2.15950 train_acc= 0.13208 val_loss= 2.10066 val_acc= 0.10345 time= 0.34392
Epoch: 0002 train_loss= 2.11136 train_acc= 0.11950 val_loss= 2.10603 val_acc= 0.10345 time= 0.01562
Epoch: 0003 train_loss= 2.05968 train_acc= 0.16352 val_loss= 2.11538 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.06173 train_acc= 0.15094 val_loss= 2.12318 val_acc= 0.03448 time= 0.01563
Epoch: 0005 train_loss= 2.06429 train_acc= 0.15723 val_loss= 2.13528 val_acc= 0.03448 time= 0.01563
Epoch: 0006 train_loss= 2.07339 train_acc= 0.13836 val_loss= 2.15268 val_acc= 0.03448 time= 0.01563
Epoch: 0007 train_loss= 2.04261 train_acc= 0.15094 val_loss= 2.16840 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.04222 train_acc= 0.14465 val_loss= 2.17202 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.03993 train_acc= 0.17610 val_loss= 2.16901 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.03581 train_acc= 0.18239 val_loss= 2.16106 val_acc= 0.17241 time= 0.01563
Epoch: 0011 train_loss= 2.02769 train_acc= 0.19497 val_loss= 2.15705 val_acc= 0.17241 time= 0.00000
Epoch: 0012 train_loss= 2.02590 train_acc= 0.18868 val_loss= 2.15238 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.01536 accuracy= 0.25424 time= 0.01562 
