Epoch: 0001 train_loss= 2.08882 train_acc= 0.11321 val_loss= 2.09527 val_acc= 0.06897 time= 0.79168
Epoch: 0002 train_loss= 2.08601 train_acc= 0.11590 val_loss= 2.09237 val_acc= 0.06897 time= 0.00000
Epoch: 0003 train_loss= 2.08340 train_acc= 0.11590 val_loss= 2.08979 val_acc= 0.06897 time= 0.00000
Epoch: 0004 train_loss= 2.08373 train_acc= 0.11860 val_loss= 2.08713 val_acc= 0.13793 time= 0.01563
Epoch: 0005 train_loss= 2.08293 train_acc= 0.12938 val_loss= 2.08429 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.08175 train_acc= 0.15094 val_loss= 2.08137 val_acc= 0.06897 time= 0.00000
Epoch: 0007 train_loss= 2.08016 train_acc= 0.14286 val_loss= 2.07879 val_acc= 0.06897 time= 0.01563
Epoch: 0008 train_loss= 2.07921 train_acc= 0.14286 val_loss= 2.07619 val_acc= 0.06897 time= 0.00000
Epoch: 0009 train_loss= 2.07713 train_acc= 0.12938 val_loss= 2.07369 val_acc= 0.06897 time= 0.00000
Epoch: 0010 train_loss= 2.07606 train_acc= 0.14286 val_loss= 2.07124 val_acc= 0.06897 time= 0.01563
Epoch: 0011 train_loss= 2.07524 train_acc= 0.13747 val_loss= 2.06879 val_acc= 0.06897 time= 0.00000
Epoch: 0012 train_loss= 2.07411 train_acc= 0.13208 val_loss= 2.06641 val_acc= 0.06897 time= 0.00000
Epoch: 0013 train_loss= 2.07339 train_acc= 0.15364 val_loss= 2.06416 val_acc= 0.10345 time= 0.01563
Epoch: 0014 train_loss= 2.07223 train_acc= 0.14825 val_loss= 2.06204 val_acc= 0.17241 time= 0.00000
Epoch: 0015 train_loss= 2.07207 train_acc= 0.14555 val_loss= 2.06016 val_acc= 0.20690 time= 0.00000
Epoch: 0016 train_loss= 2.07015 train_acc= 0.18329 val_loss= 2.05836 val_acc= 0.17241 time= 0.01563
Epoch: 0017 train_loss= 2.07050 train_acc= 0.15364 val_loss= 2.05698 val_acc= 0.17241 time= 0.00000
Epoch: 0018 train_loss= 2.06904 train_acc= 0.16712 val_loss= 2.05586 val_acc= 0.17241 time= 0.00000
Epoch: 0019 train_loss= 2.06982 train_acc= 0.17251 val_loss= 2.05504 val_acc= 0.17241 time= 0.01563
Epoch: 0020 train_loss= 2.06770 train_acc= 0.14825 val_loss= 2.05460 val_acc= 0.17241 time= 0.00000
Epoch: 0021 train_loss= 2.06760 train_acc= 0.16442 val_loss= 2.05465 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.06732 train_acc= 0.16712 val_loss= 2.05481 val_acc= 0.17241 time= 0.00000
Epoch: 0023 train_loss= 2.06532 train_acc= 0.16981 val_loss= 2.05533 val_acc= 0.17241 time= 0.01993
Epoch: 0024 train_loss= 2.06610 train_acc= 0.16981 val_loss= 2.05591 val_acc= 0.17241 time= 0.00101
Epoch: 0025 train_loss= 2.06432 train_acc= 0.16981 val_loss= 2.05662 val_acc= 0.17241 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.04176 accuracy= 0.11864 time= 0.01050 
