Epoch: 0001 train_loss= 2.09502 train_acc= 0.07170 val_loss= 2.09534 val_acc= 0.06897 time= 0.57259
Epoch: 0002 train_loss= 2.09316 train_acc= 0.07170 val_loss= 2.09321 val_acc= 0.06897 time= 0.00500
Epoch: 0003 train_loss= 2.08985 train_acc= 0.07170 val_loss= 2.09132 val_acc= 0.06897 time= 0.00500
Epoch: 0004 train_loss= 2.08752 train_acc= 0.07547 val_loss= 2.08969 val_acc= 0.06897 time= 0.00600
Epoch: 0005 train_loss= 2.08826 train_acc= 0.07170 val_loss= 2.08823 val_acc= 0.06897 time= 0.00400
Epoch: 0006 train_loss= 2.08650 train_acc= 0.07170 val_loss= 2.08702 val_acc= 0.06897 time= 0.00500
Epoch: 0007 train_loss= 2.08519 train_acc= 0.07925 val_loss= 2.08587 val_acc= 0.06897 time= 0.00600
Epoch: 0008 train_loss= 2.08269 train_acc= 0.08302 val_loss= 2.08495 val_acc= 0.10345 time= 0.00400
Epoch: 0009 train_loss= 2.08318 train_acc= 0.10189 val_loss= 2.08416 val_acc= 0.10345 time= 0.00700
Epoch: 0010 train_loss= 2.08212 train_acc= 0.15094 val_loss= 2.08346 val_acc= 0.06897 time= 0.00500
Epoch: 0011 train_loss= 2.08137 train_acc= 0.16226 val_loss= 2.08287 val_acc= 0.06897 time= 0.00600
Epoch: 0012 train_loss= 2.07863 train_acc= 0.17358 val_loss= 2.08236 val_acc= 0.06897 time= 0.00500
Epoch: 0013 train_loss= 2.07967 train_acc= 0.17358 val_loss= 2.08195 val_acc= 0.06897 time= 0.00400
Epoch: 0014 train_loss= 2.07662 train_acc= 0.17358 val_loss= 2.08159 val_acc= 0.06897 time= 0.00500
Epoch: 0015 train_loss= 2.07512 train_acc= 0.17358 val_loss= 2.08125 val_acc= 0.06897 time= 0.00500
Epoch: 0016 train_loss= 2.07480 train_acc= 0.17358 val_loss= 2.08089 val_acc= 0.06897 time= 0.00500
Epoch: 0017 train_loss= 2.07399 train_acc= 0.17358 val_loss= 2.08056 val_acc= 0.06897 time= 0.00500
Epoch: 0018 train_loss= 2.07343 train_acc= 0.17358 val_loss= 2.08025 val_acc= 0.06897 time= 0.00400
Epoch: 0019 train_loss= 2.07297 train_acc= 0.17358 val_loss= 2.08007 val_acc= 0.06897 time= 0.00600
Epoch: 0020 train_loss= 2.07098 train_acc= 0.16981 val_loss= 2.08005 val_acc= 0.06897 time= 0.00500
Epoch: 0021 train_loss= 2.06646 train_acc= 0.17358 val_loss= 2.08004 val_acc= 0.06897 time= 0.00500
Epoch: 0022 train_loss= 2.06951 train_acc= 0.17358 val_loss= 2.08015 val_acc= 0.06897 time= 0.00500
Epoch: 0023 train_loss= 2.06777 train_acc= 0.17358 val_loss= 2.08030 val_acc= 0.06897 time= 0.00500
Epoch: 0024 train_loss= 2.06609 train_acc= 0.17358 val_loss= 2.08051 val_acc= 0.06897 time= 0.00500
Epoch: 0025 train_loss= 2.06514 train_acc= 0.17358 val_loss= 2.08083 val_acc= 0.06897 time= 0.00500
Early stopping...
Optimization Finished!
Test set results: cost= 2.04286 accuracy= 0.22034 time= 0.00300 
