Epoch: 0001 train_loss= 2.08356 train_acc= 0.05660 val_loss= 2.08169 val_acc= 0.20690 time= 0.26564
Epoch: 0002 train_loss= 2.07990 train_acc= 0.17610 val_loss= 2.08011 val_acc= 0.20690 time= 0.00000
Epoch: 0003 train_loss= 2.07976 train_acc= 0.16981 val_loss= 2.07864 val_acc= 0.24138 time= 0.01563
Epoch: 0004 train_loss= 2.07808 train_acc= 0.17610 val_loss= 2.07728 val_acc= 0.17241 time= 0.00000
Epoch: 0005 train_loss= 2.07655 train_acc= 0.18239 val_loss= 2.07586 val_acc= 0.20690 time= 0.00000
Epoch: 0006 train_loss= 2.07550 train_acc= 0.19497 val_loss= 2.07443 val_acc= 0.17241 time= 0.01563
Epoch: 0007 train_loss= 2.07654 train_acc= 0.18239 val_loss= 2.07291 val_acc= 0.17241 time= 0.00000
Epoch: 0008 train_loss= 2.07070 train_acc= 0.16352 val_loss= 2.07113 val_acc= 0.17241 time= 0.00000
Epoch: 0009 train_loss= 2.07070 train_acc= 0.18239 val_loss= 2.06918 val_acc= 0.17241 time= 0.01563
Epoch: 0010 train_loss= 2.06701 train_acc= 0.18239 val_loss= 2.06698 val_acc= 0.17241 time= 0.00000
Epoch: 0011 train_loss= 2.06589 train_acc= 0.16981 val_loss= 2.06466 val_acc= 0.17241 time= 0.00000
Epoch: 0012 train_loss= 2.06382 train_acc= 0.16352 val_loss= 2.06215 val_acc= 0.17241 time= 0.00000
Epoch: 0013 train_loss= 2.05859 train_acc= 0.17610 val_loss= 2.05948 val_acc= 0.17241 time= 0.01563
Epoch: 0014 train_loss= 2.05767 train_acc= 0.16352 val_loss= 2.05661 val_acc= 0.17241 time= 0.00000
Epoch: 0015 train_loss= 2.05426 train_acc= 0.18239 val_loss= 2.05361 val_acc= 0.17241 time= 0.00000
Epoch: 0016 train_loss= 2.05487 train_acc= 0.17610 val_loss= 2.05051 val_acc= 0.17241 time= 0.01563
Epoch: 0017 train_loss= 2.05059 train_acc= 0.18239 val_loss= 2.04733 val_acc= 0.17241 time= 0.00000
Epoch: 0018 train_loss= 2.04539 train_acc= 0.16981 val_loss= 2.04412 val_acc= 0.17241 time= 0.00000
Epoch: 0019 train_loss= 2.04182 train_acc= 0.16352 val_loss= 2.04088 val_acc= 0.17241 time= 0.01563
Epoch: 0020 train_loss= 2.03718 train_acc= 0.17610 val_loss= 2.03762 val_acc= 0.17241 time= 0.00000
Epoch: 0021 train_loss= 2.03822 train_acc= 0.16981 val_loss= 2.03442 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.02724 train_acc= 0.17610 val_loss= 2.03130 val_acc= 0.17241 time= 0.01563
Epoch: 0023 train_loss= 2.02915 train_acc= 0.16981 val_loss= 2.02825 val_acc= 0.17241 time= 0.00000
Epoch: 0024 train_loss= 2.02810 train_acc= 0.18239 val_loss= 2.02540 val_acc= 0.17241 time= 0.00000
Epoch: 0025 train_loss= 2.01920 train_acc= 0.16981 val_loss= 2.02269 val_acc= 0.17241 time= 0.00000
Epoch: 0026 train_loss= 2.02335 train_acc= 0.16981 val_loss= 2.02008 val_acc= 0.17241 time= 0.01563
Epoch: 0027 train_loss= 2.02012 train_acc= 0.16981 val_loss= 2.01763 val_acc= 0.17241 time= 0.00000
Epoch: 0028 train_loss= 2.01056 train_acc= 0.16352 val_loss= 2.01526 val_acc= 0.17241 time= 0.00000
Epoch: 0029 train_loss= 2.01086 train_acc= 0.17610 val_loss= 2.01307 val_acc= 0.17241 time= 0.01563
Epoch: 0030 train_loss= 2.00880 train_acc= 0.16981 val_loss= 2.01102 val_acc= 0.17241 time= 0.00000
Epoch: 0031 train_loss= 2.00943 train_acc= 0.16981 val_loss= 2.00927 val_acc= 0.17241 time= 0.00000
Epoch: 0032 train_loss= 2.01036 train_acc= 0.18239 val_loss= 2.00777 val_acc= 0.17241 time= 0.00000
Epoch: 0033 train_loss= 2.00616 train_acc= 0.16981 val_loss= 2.00650 val_acc= 0.17241 time= 0.01563
Epoch: 0034 train_loss= 2.00924 train_acc= 0.16981 val_loss= 2.00547 val_acc= 0.24138 time= 0.00000
Epoch: 0035 train_loss= 2.00115 train_acc= 0.20755 val_loss= 2.00468 val_acc= 0.20690 time= 0.00000
Epoch: 0036 train_loss= 2.00793 train_acc= 0.17610 val_loss= 2.00420 val_acc= 0.20690 time= 0.01563
Epoch: 0037 train_loss= 1.99607 train_acc= 0.17610 val_loss= 2.00379 val_acc= 0.20690 time= 0.00000
Epoch: 0038 train_loss= 1.99813 train_acc= 0.18239 val_loss= 2.00362 val_acc= 0.20690 time= 0.00000
Epoch: 0039 train_loss= 2.00217 train_acc= 0.18239 val_loss= 2.00330 val_acc= 0.20690 time= 0.01563
Epoch: 0040 train_loss= 1.99851 train_acc= 0.17610 val_loss= 2.00310 val_acc= 0.20690 time= 0.00000
Epoch: 0041 train_loss= 1.99718 train_acc= 0.16981 val_loss= 2.00279 val_acc= 0.20690 time= 0.00000
Epoch: 0042 train_loss= 1.99502 train_acc= 0.18868 val_loss= 2.00249 val_acc= 0.20690 time= 0.00000
Epoch: 0043 train_loss= 1.99659 train_acc= 0.16981 val_loss= 2.00209 val_acc= 0.20690 time= 0.01563
Epoch: 0044 train_loss= 1.99894 train_acc= 0.18868 val_loss= 2.00178 val_acc= 0.20690 time= 0.00000
Epoch: 0045 train_loss= 2.00232 train_acc= 0.18239 val_loss= 2.00130 val_acc= 0.20690 time= 0.00000
Epoch: 0046 train_loss= 1.99783 train_acc= 0.18868 val_loss= 2.00072 val_acc= 0.20690 time= 0.01563
Epoch: 0047 train_loss= 1.99993 train_acc= 0.18239 val_loss= 1.99998 val_acc= 0.20690 time= 0.00000
Epoch: 0048 train_loss= 1.98949 train_acc= 0.19497 val_loss= 1.99930 val_acc= 0.20690 time= 0.00951
Epoch: 0049 train_loss= 1.99525 train_acc= 0.18868 val_loss= 1.99860 val_acc= 0.20690 time= 0.00500
Epoch: 0050 train_loss= 1.99861 train_acc= 0.16981 val_loss= 1.99787 val_acc= 0.20690 time= 0.00500
Epoch: 0051 train_loss= 1.99374 train_acc= 0.18868 val_loss= 1.99751 val_acc= 0.24138 time= 0.00500
Epoch: 0052 train_loss= 1.99827 train_acc= 0.16352 val_loss= 1.99719 val_acc= 0.24138 time= 0.00500
Epoch: 0053 train_loss= 1.99251 train_acc= 0.15094 val_loss= 1.99676 val_acc= 0.20690 time= 0.00400
Epoch: 0054 train_loss= 1.99966 train_acc= 0.15723 val_loss= 1.99648 val_acc= 0.13793 time= 0.00500
Epoch: 0055 train_loss= 1.99512 train_acc= 0.15723 val_loss= 1.99614 val_acc= 0.20690 time= 0.00500
Epoch: 0056 train_loss= 1.99617 train_acc= 0.19497 val_loss= 1.99584 val_acc= 0.24138 time= 0.00400
Epoch: 0057 train_loss= 1.99169 train_acc= 0.18868 val_loss= 1.99541 val_acc= 0.20690 time= 0.00206
Epoch: 0058 train_loss= 1.99258 train_acc= 0.15094 val_loss= 1.99484 val_acc= 0.17241 time= 0.00000
Epoch: 0059 train_loss= 1.99669 train_acc= 0.18868 val_loss= 1.99430 val_acc= 0.17241 time= 0.00000
Epoch: 0060 train_loss= 1.99075 train_acc= 0.20755 val_loss= 1.99381 val_acc= 0.24138 time= 0.01562
Epoch: 0061 train_loss= 1.99810 train_acc= 0.21384 val_loss= 1.99339 val_acc= 0.24138 time= 0.00000
Epoch: 0062 train_loss= 1.99826 train_acc= 0.23270 val_loss= 1.99309 val_acc= 0.24138 time= 0.00000
Epoch: 0063 train_loss= 1.99489 train_acc= 0.18239 val_loss= 1.99308 val_acc= 0.24138 time= 0.01563
Epoch: 0064 train_loss= 1.99464 train_acc= 0.19497 val_loss= 1.99298 val_acc= 0.24138 time= 0.00000
Epoch: 0065 train_loss= 1.99559 train_acc= 0.16352 val_loss= 1.99292 val_acc= 0.20690 time= 0.00000
Epoch: 0066 train_loss= 1.98918 train_acc= 0.16981 val_loss= 1.99281 val_acc= 0.20690 time= 0.01563
Epoch: 0067 train_loss= 1.99224 train_acc= 0.18239 val_loss= 1.99283 val_acc= 0.20690 time= 0.00000
Epoch: 0068 train_loss= 1.98983 train_acc= 0.20126 val_loss= 1.99289 val_acc= 0.20690 time= 0.00000
Epoch: 0069 train_loss= 1.99309 train_acc= 0.21384 val_loss= 1.99315 val_acc= 0.20690 time= 0.01563
Epoch: 0070 train_loss= 1.98980 train_acc= 0.18868 val_loss= 1.99358 val_acc= 0.24138 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.16961 accuracy= 0.16949 time= 0.00000 
