Epoch: 0001 train_loss= 2.08687 train_acc= 0.13477 val_loss= 2.08490 val_acc= 0.20690 time= 0.73492
Epoch: 0002 train_loss= 2.08423 train_acc= 0.15903 val_loss= 2.08252 val_acc= 0.20690 time= 0.00000
Epoch: 0003 train_loss= 2.08192 train_acc= 0.14825 val_loss= 2.08006 val_acc= 0.20690 time= 0.01562
Epoch: 0004 train_loss= 2.07971 train_acc= 0.15364 val_loss= 2.07775 val_acc= 0.20690 time= 0.00000
Epoch: 0005 train_loss= 2.07798 train_acc= 0.14555 val_loss= 2.07558 val_acc= 0.20690 time= 0.01563
Epoch: 0006 train_loss= 2.07635 train_acc= 0.14825 val_loss= 2.07351 val_acc= 0.20690 time= 0.00000
Epoch: 0007 train_loss= 2.07527 train_acc= 0.14825 val_loss= 2.07157 val_acc= 0.20690 time= 0.01563
Epoch: 0008 train_loss= 2.07378 train_acc= 0.14555 val_loss= 2.06961 val_acc= 0.20690 time= 0.00000
Epoch: 0009 train_loss= 2.07271 train_acc= 0.14825 val_loss= 2.06773 val_acc= 0.20690 time= 0.01563
Epoch: 0010 train_loss= 2.07197 train_acc= 0.15094 val_loss= 2.06578 val_acc= 0.20690 time= 0.00000
Epoch: 0011 train_loss= 2.07227 train_acc= 0.15364 val_loss= 2.06391 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.07199 train_acc= 0.14825 val_loss= 2.06232 val_acc= 0.20690 time= 0.01563
Epoch: 0013 train_loss= 2.07131 train_acc= 0.14555 val_loss= 2.06092 val_acc= 0.20690 time= 0.00000
Epoch: 0014 train_loss= 2.06980 train_acc= 0.15094 val_loss= 2.05967 val_acc= 0.20690 time= 0.01563
Epoch: 0015 train_loss= 2.06988 train_acc= 0.15094 val_loss= 2.05838 val_acc= 0.20690 time= 0.00000
Epoch: 0016 train_loss= 2.06911 train_acc= 0.14555 val_loss= 2.05738 val_acc= 0.20690 time= 0.01563
Epoch: 0017 train_loss= 2.06844 train_acc= 0.15094 val_loss= 2.05652 val_acc= 0.20690 time= 0.00000
Epoch: 0018 train_loss= 2.06940 train_acc= 0.15903 val_loss= 2.05577 val_acc= 0.06897 time= 0.01563
Epoch: 0019 train_loss= 2.06796 train_acc= 0.18598 val_loss= 2.05499 val_acc= 0.06897 time= 0.00000
Epoch: 0020 train_loss= 2.06708 train_acc= 0.15903 val_loss= 2.05440 val_acc= 0.06897 time= 0.01563
Epoch: 0021 train_loss= 2.06764 train_acc= 0.17520 val_loss= 2.05398 val_acc= 0.06897 time= 0.02467
Epoch: 0022 train_loss= 2.06678 train_acc= 0.15903 val_loss= 2.05333 val_acc= 0.06897 time= 0.01725
Epoch: 0023 train_loss= 2.06746 train_acc= 0.15633 val_loss= 2.05269 val_acc= 0.06897 time= 0.00091
Epoch: 0024 train_loss= 2.06631 train_acc= 0.15633 val_loss= 2.05197 val_acc= 0.06897 time= 0.01563
Epoch: 0025 train_loss= 2.06689 train_acc= 0.16173 val_loss= 2.05096 val_acc= 0.06897 time= 0.00000
Epoch: 0026 train_loss= 2.06672 train_acc= 0.15903 val_loss= 2.04958 val_acc= 0.06897 time= 0.01563
Epoch: 0027 train_loss= 2.06671 train_acc= 0.16173 val_loss= 2.04832 val_acc= 0.06897 time= 0.00000
Epoch: 0028 train_loss= 2.06546 train_acc= 0.16173 val_loss= 2.04727 val_acc= 0.06897 time= 0.01562
Epoch: 0029 train_loss= 2.06417 train_acc= 0.16173 val_loss= 2.04656 val_acc= 0.06897 time= 0.01563
Epoch: 0030 train_loss= 2.06512 train_acc= 0.16173 val_loss= 2.04629 val_acc= 0.06897 time= 0.00000
Epoch: 0031 train_loss= 2.06523 train_acc= 0.15633 val_loss= 2.04636 val_acc= 0.06897 time= 0.01563
Epoch: 0032 train_loss= 2.06500 train_acc= 0.16173 val_loss= 2.04653 val_acc= 0.06897 time= 0.00000
Epoch: 0033 train_loss= 2.06523 train_acc= 0.15094 val_loss= 2.04663 val_acc= 0.06897 time= 0.01563
Epoch: 0034 train_loss= 2.06558 train_acc= 0.15903 val_loss= 2.04676 val_acc= 0.06897 time= 0.00000
Epoch: 0035 train_loss= 2.06554 train_acc= 0.15903 val_loss= 2.04683 val_acc= 0.06897 time= 0.01563
Epoch: 0036 train_loss= 2.06499 train_acc= 0.16173 val_loss= 2.04684 val_acc= 0.06897 time= 0.01563
Epoch: 0037 train_loss= 2.06324 train_acc= 0.16712 val_loss= 2.04676 val_acc= 0.06897 time= 0.00000
Epoch: 0038 train_loss= 2.06250 train_acc= 0.16173 val_loss= 2.04636 val_acc= 0.06897 time= 0.01563
Epoch: 0039 train_loss= 2.06454 train_acc= 0.16173 val_loss= 2.04614 val_acc= 0.06897 time= 0.00000
Epoch: 0040 train_loss= 2.06427 train_acc= 0.16442 val_loss= 2.04621 val_acc= 0.06897 time= 0.01563
Epoch: 0041 train_loss= 2.06408 train_acc= 0.15633 val_loss= 2.04633 val_acc= 0.06897 time= 0.00000
Epoch: 0042 train_loss= 2.06403 train_acc= 0.15903 val_loss= 2.04634 val_acc= 0.06897 time= 0.01563
Epoch: 0043 train_loss= 2.06390 train_acc= 0.16712 val_loss= 2.04641 val_acc= 0.06897 time= 0.00000
Epoch: 0044 train_loss= 2.06274 train_acc= 0.16173 val_loss= 2.04632 val_acc= 0.06897 time= 0.01563
Epoch: 0045 train_loss= 2.06265 train_acc= 0.15903 val_loss= 2.04627 val_acc= 0.06897 time= 0.00000
Epoch: 0046 train_loss= 2.06267 train_acc= 0.16442 val_loss= 2.04619 val_acc= 0.06897 time= 0.01563
Epoch: 0047 train_loss= 2.06226 train_acc= 0.16712 val_loss= 2.04600 val_acc= 0.06897 time= 0.01563
Epoch: 0048 train_loss= 2.06204 train_acc= 0.16712 val_loss= 2.04567 val_acc= 0.06897 time= 0.00000
Epoch: 0049 train_loss= 2.06190 train_acc= 0.16712 val_loss= 2.04525 val_acc= 0.06897 time= 0.01562
Epoch: 0050 train_loss= 2.06266 train_acc= 0.16442 val_loss= 2.04510 val_acc= 0.06897 time= 0.00000
Epoch: 0051 train_loss= 2.06243 train_acc= 0.16712 val_loss= 2.04525 val_acc= 0.06897 time= 0.01931
Epoch: 0052 train_loss= 2.06199 train_acc= 0.16442 val_loss= 2.04579 val_acc= 0.06897 time= 0.00900
Epoch: 0053 train_loss= 2.06272 train_acc= 0.16173 val_loss= 2.04578 val_acc= 0.06897 time= 0.00400
Epoch: 0054 train_loss= 2.06302 train_acc= 0.16173 val_loss= 2.04539 val_acc= 0.06897 time= 0.01566
Epoch: 0055 train_loss= 2.06296 train_acc= 0.16442 val_loss= 2.04476 val_acc= 0.06897 time= 0.01064
Epoch: 0056 train_loss= 2.06214 train_acc= 0.16173 val_loss= 2.04455 val_acc= 0.06897 time= 0.01012
Epoch: 0057 train_loss= 2.06149 train_acc= 0.16981 val_loss= 2.04430 val_acc= 0.06897 time= 0.00808
Epoch: 0058 train_loss= 2.06228 train_acc= 0.16981 val_loss= 2.04444 val_acc= 0.06897 time= 0.00906
Epoch: 0059 train_loss= 2.06123 train_acc= 0.18598 val_loss= 2.04540 val_acc= 0.06897 time= 0.01205
Early stopping...
Optimization Finished!
Test set results: cost= 2.07682 accuracy= 0.20339 time= 0.00500 
