Epoch: 0001 train_loss= 2.08172 train_acc= 0.11698 val_loss= 2.08238 val_acc= 0.13793 time= 0.50674
Epoch: 0002 train_loss= 2.07812 train_acc= 0.18113 val_loss= 2.08060 val_acc= 0.06897 time= 0.00000
Epoch: 0003 train_loss= 2.07792 train_acc= 0.18868 val_loss= 2.07882 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.07583 train_acc= 0.17358 val_loss= 2.07701 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.07370 train_acc= 0.18113 val_loss= 2.07515 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.07255 train_acc= 0.17358 val_loss= 2.07327 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.06881 train_acc= 0.19623 val_loss= 2.07136 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.06964 train_acc= 0.19623 val_loss= 2.06940 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.06623 train_acc= 0.19623 val_loss= 2.06744 val_acc= 0.10345 time= 0.01563
Epoch: 0010 train_loss= 2.06490 train_acc= 0.18868 val_loss= 2.06547 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.06298 train_acc= 0.20000 val_loss= 2.06350 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.05901 train_acc= 0.20000 val_loss= 2.06161 val_acc= 0.10345 time= 0.01563
Epoch: 0013 train_loss= 2.05788 train_acc= 0.19245 val_loss= 2.05982 val_acc= 0.10345 time= 0.00000
Epoch: 0014 train_loss= 2.05935 train_acc= 0.19245 val_loss= 2.05813 val_acc= 0.10345 time= 0.01563
Epoch: 0015 train_loss= 2.05486 train_acc= 0.18868 val_loss= 2.05657 val_acc= 0.10345 time= 0.00000
Epoch: 0016 train_loss= 2.05026 train_acc= 0.18868 val_loss= 2.05524 val_acc= 0.10345 time= 0.00000
Epoch: 0017 train_loss= 2.05290 train_acc= 0.20000 val_loss= 2.05410 val_acc= 0.10345 time= 0.01563
Epoch: 0018 train_loss= 2.05254 train_acc= 0.20000 val_loss= 2.05319 val_acc= 0.10345 time= 0.00000
Epoch: 0019 train_loss= 2.05134 train_acc= 0.18868 val_loss= 2.05248 val_acc= 0.10345 time= 0.00000
Epoch: 0020 train_loss= 2.05085 train_acc= 0.19245 val_loss= 2.05214 val_acc= 0.10345 time= 0.01563
Epoch: 0021 train_loss= 2.04838 train_acc= 0.18868 val_loss= 2.05208 val_acc= 0.10345 time= 0.00000
Epoch: 0022 train_loss= 2.04892 train_acc= 0.18868 val_loss= 2.05236 val_acc= 0.10345 time= 0.00000
Epoch: 0023 train_loss= 2.04784 train_acc= 0.19245 val_loss= 2.05277 val_acc= 0.10345 time= 0.01563
Epoch: 0024 train_loss= 2.04609 train_acc= 0.19245 val_loss= 2.05332 val_acc= 0.10345 time= 0.00000
Epoch: 0025 train_loss= 2.04633 train_acc= 0.19245 val_loss= 2.05396 val_acc= 0.10345 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.12921 accuracy= 0.08475 time= 0.00000 
