Epoch: 0001 train_loss= 2.08651 train_acc= 0.13208 val_loss= 2.08730 val_acc= 0.00000 time= 0.35939
Epoch: 0002 train_loss= 2.08466 train_acc= 0.11698 val_loss= 2.08686 val_acc= 0.17241 time= 0.01563
Epoch: 0003 train_loss= 2.08374 train_acc= 0.16604 val_loss= 2.08659 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.08312 train_acc= 0.17736 val_loss= 2.08635 val_acc= 0.17241 time= 0.01562
Epoch: 0005 train_loss= 2.08224 train_acc= 0.17736 val_loss= 2.08612 val_acc= 0.17241 time= 0.00000
Epoch: 0006 train_loss= 2.08140 train_acc= 0.19245 val_loss= 2.08583 val_acc= 0.17241 time= 0.01563
Epoch: 0007 train_loss= 2.08028 train_acc= 0.19245 val_loss= 2.08549 val_acc= 0.17241 time= 0.00000
Epoch: 0008 train_loss= 2.07950 train_acc= 0.19623 val_loss= 2.08506 val_acc= 0.17241 time= 0.02057
Epoch: 0009 train_loss= 2.07870 train_acc= 0.19245 val_loss= 2.08451 val_acc= 0.17241 time= 0.00800
Epoch: 0010 train_loss= 2.07812 train_acc= 0.18113 val_loss= 2.08376 val_acc= 0.17241 time= 0.00800
Epoch: 0011 train_loss= 2.07478 train_acc= 0.19245 val_loss= 2.08280 val_acc= 0.17241 time= 0.00700
Epoch: 0012 train_loss= 2.07374 train_acc= 0.19623 val_loss= 2.08164 val_acc= 0.17241 time= 0.01000
Epoch: 0013 train_loss= 2.07257 train_acc= 0.18868 val_loss= 2.08027 val_acc= 0.17241 time= 0.00800
Epoch: 0014 train_loss= 2.07033 train_acc= 0.20000 val_loss= 2.07864 val_acc= 0.17241 time= 0.00407
Epoch: 0015 train_loss= 2.06863 train_acc= 0.19245 val_loss= 2.07664 val_acc= 0.17241 time= 0.00000
Epoch: 0016 train_loss= 2.06678 train_acc= 0.20000 val_loss= 2.07435 val_acc= 0.17241 time= 0.01563
Epoch: 0017 train_loss= 2.06361 train_acc= 0.18113 val_loss= 2.07176 val_acc= 0.17241 time= 0.00000
Epoch: 0018 train_loss= 2.06109 train_acc= 0.18491 val_loss= 2.06900 val_acc= 0.17241 time= 0.01563
Epoch: 0019 train_loss= 2.05548 train_acc= 0.19245 val_loss= 2.06606 val_acc= 0.17241 time= 0.00000
Epoch: 0020 train_loss= 2.05633 train_acc= 0.17736 val_loss= 2.06291 val_acc= 0.17241 time= 0.01563
Epoch: 0021 train_loss= 2.05214 train_acc= 0.16604 val_loss= 2.05971 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.04877 train_acc= 0.20377 val_loss= 2.05646 val_acc= 0.17241 time= 0.01563
Epoch: 0023 train_loss= 2.04513 train_acc= 0.18491 val_loss= 2.05311 val_acc= 0.17241 time= 0.00000
Epoch: 0024 train_loss= 2.04268 train_acc= 0.20377 val_loss= 2.04978 val_acc= 0.17241 time= 0.01563
Epoch: 0025 train_loss= 2.04068 train_acc= 0.17358 val_loss= 2.04648 val_acc= 0.17241 time= 0.00000
Epoch: 0026 train_loss= 2.03988 train_acc= 0.16604 val_loss= 2.04330 val_acc= 0.17241 time= 0.01563
Epoch: 0027 train_loss= 2.03919 train_acc= 0.18113 val_loss= 2.04021 val_acc= 0.17241 time= 0.01563
Epoch: 0028 train_loss= 2.03278 train_acc= 0.17358 val_loss= 2.03724 val_acc= 0.17241 time= 0.00000
Epoch: 0029 train_loss= 2.03532 train_acc= 0.16604 val_loss= 2.03448 val_acc= 0.17241 time= 0.01563
Epoch: 0030 train_loss= 2.03384 train_acc= 0.16981 val_loss= 2.03198 val_acc= 0.17241 time= 0.00000
Epoch: 0031 train_loss= 2.03348 train_acc= 0.16226 val_loss= 2.02953 val_acc= 0.17241 time= 0.01563
Epoch: 0032 train_loss= 2.03063 train_acc= 0.18491 val_loss= 2.02726 val_acc= 0.17241 time= 0.00000
Epoch: 0033 train_loss= 2.03600 train_acc= 0.15849 val_loss= 2.02497 val_acc= 0.17241 time= 0.01563
Epoch: 0034 train_loss= 2.03436 train_acc= 0.17736 val_loss= 2.02280 val_acc= 0.17241 time= 0.00000
Epoch: 0035 train_loss= 2.03416 train_acc= 0.16226 val_loss= 2.02076 val_acc= 0.17241 time= 0.01563
Epoch: 0036 train_loss= 2.03221 train_acc= 0.17358 val_loss= 2.01887 val_acc= 0.17241 time= 0.00000
Epoch: 0037 train_loss= 2.03303 train_acc= 0.19245 val_loss= 2.01694 val_acc= 0.17241 time= 0.01563
Epoch: 0038 train_loss= 2.03162 train_acc= 0.19623 val_loss= 2.01523 val_acc= 0.17241 time= 0.01563
Epoch: 0039 train_loss= 2.03209 train_acc= 0.20755 val_loss= 2.01373 val_acc= 0.17241 time= 0.00000
Epoch: 0040 train_loss= 2.02922 train_acc= 0.19623 val_loss= 2.01255 val_acc= 0.17241 time= 0.01562
Epoch: 0041 train_loss= 2.02810 train_acc= 0.18868 val_loss= 2.01128 val_acc= 0.17241 time= 0.01563
Epoch: 0042 train_loss= 2.02995 train_acc= 0.19245 val_loss= 2.01008 val_acc= 0.17241 time= 0.00000
Epoch: 0043 train_loss= 2.03033 train_acc= 0.19623 val_loss= 2.00901 val_acc= 0.17241 time= 0.01563
Epoch: 0044 train_loss= 2.03228 train_acc= 0.19623 val_loss= 2.00817 val_acc= 0.17241 time= 0.00000
Epoch: 0045 train_loss= 2.03170 train_acc= 0.19245 val_loss= 2.00781 val_acc= 0.17241 time= 0.01563
Epoch: 0046 train_loss= 2.03110 train_acc= 0.19245 val_loss= 2.00768 val_acc= 0.17241 time= 0.01563
Epoch: 0047 train_loss= 2.03034 train_acc= 0.18868 val_loss= 2.00776 val_acc= 0.17241 time= 0.00000
Epoch: 0048 train_loss= 2.03036 train_acc= 0.19245 val_loss= 2.00784 val_acc= 0.17241 time= 0.00000
Epoch: 0049 train_loss= 2.02875 train_acc= 0.19245 val_loss= 2.00788 val_acc= 0.17241 time= 0.01563
Epoch: 0050 train_loss= 2.02822 train_acc= 0.19623 val_loss= 2.00782 val_acc= 0.17241 time= 0.00000
Epoch: 0051 train_loss= 2.03105 train_acc= 0.19245 val_loss= 2.00790 val_acc= 0.17241 time= 0.01563
Epoch: 0052 train_loss= 2.02848 train_acc= 0.19245 val_loss= 2.00817 val_acc= 0.17241 time= 0.00000
Epoch: 0053 train_loss= 2.02805 train_acc= 0.19623 val_loss= 2.00837 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07409 accuracy= 0.16949 time= 0.00000 
