Epoch: 0001 train_loss= 2.08711 train_acc= 0.11321 val_loss= 2.08459 val_acc= 0.17241 time= 0.75024
Epoch: 0002 train_loss= 2.08470 train_acc= 0.11590 val_loss= 2.08233 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08239 train_acc= 0.13208 val_loss= 2.08035 val_acc= 0.10345 time= 0.01563
Epoch: 0004 train_loss= 2.08044 train_acc= 0.15364 val_loss= 2.07853 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.07859 train_acc= 0.15364 val_loss= 2.07687 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07720 train_acc= 0.15364 val_loss= 2.07536 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.07573 train_acc= 0.15633 val_loss= 2.07398 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.07424 train_acc= 0.15364 val_loss= 2.07275 val_acc= 0.10345 time= 0.01563
Epoch: 0009 train_loss= 2.07334 train_acc= 0.15364 val_loss= 2.07151 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.07238 train_acc= 0.15364 val_loss= 2.07014 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.07078 train_acc= 0.15364 val_loss= 2.06887 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.06977 train_acc= 0.15364 val_loss= 2.06768 val_acc= 0.10345 time= 0.01563
Epoch: 0013 train_loss= 2.06998 train_acc= 0.15364 val_loss= 2.06653 val_acc= 0.10345 time= 0.01563
Epoch: 0014 train_loss= 2.06879 train_acc= 0.15364 val_loss= 2.06534 val_acc= 0.10345 time= 0.00000
Epoch: 0015 train_loss= 2.06699 train_acc= 0.15364 val_loss= 2.06421 val_acc= 0.10345 time= 0.01563
Epoch: 0016 train_loss= 2.06680 train_acc= 0.15364 val_loss= 2.06312 val_acc= 0.10345 time= 0.01563
Epoch: 0017 train_loss= 2.06678 train_acc= 0.15364 val_loss= 2.06207 val_acc= 0.10345 time= 0.00000
Epoch: 0018 train_loss= 2.06534 train_acc= 0.15364 val_loss= 2.06111 val_acc= 0.10345 time= 0.01563
Epoch: 0019 train_loss= 2.06513 train_acc= 0.15364 val_loss= 2.06018 val_acc= 0.10345 time= 0.01563
Epoch: 0020 train_loss= 2.06448 train_acc= 0.15364 val_loss= 2.05933 val_acc= 0.10345 time= 0.01563
Epoch: 0021 train_loss= 2.06451 train_acc= 0.15364 val_loss= 2.05852 val_acc= 0.10345 time= 0.00000
Epoch: 0022 train_loss= 2.06328 train_acc= 0.15364 val_loss= 2.05775 val_acc= 0.10345 time= 0.01563
Epoch: 0023 train_loss= 2.06233 train_acc= 0.15364 val_loss= 2.05707 val_acc= 0.10345 time= 0.01563
Epoch: 0024 train_loss= 2.06243 train_acc= 0.15903 val_loss= 2.05651 val_acc= 0.10345 time= 0.00000
Epoch: 0025 train_loss= 2.06168 train_acc= 0.15903 val_loss= 2.05604 val_acc= 0.10345 time= 0.01563
Epoch: 0026 train_loss= 2.06068 train_acc= 0.15633 val_loss= 2.05562 val_acc= 0.10345 time= 0.01563
Epoch: 0027 train_loss= 2.06136 train_acc= 0.15364 val_loss= 2.05526 val_acc= 0.10345 time= 0.00000
Epoch: 0028 train_loss= 2.06135 train_acc= 0.15633 val_loss= 2.05495 val_acc= 0.10345 time= 0.01563
Epoch: 0029 train_loss= 2.06073 train_acc= 0.15364 val_loss= 2.05472 val_acc= 0.10345 time= 0.00000
Epoch: 0030 train_loss= 2.06099 train_acc= 0.15364 val_loss= 2.05450 val_acc= 0.10345 time= 0.01563
Epoch: 0031 train_loss= 2.06000 train_acc= 0.15903 val_loss= 2.05436 val_acc= 0.10345 time= 0.01563
Epoch: 0032 train_loss= 2.06010 train_acc= 0.15633 val_loss= 2.05425 val_acc= 0.10345 time= 0.00000
Epoch: 0033 train_loss= 2.05951 train_acc= 0.15094 val_loss= 2.05413 val_acc= 0.10345 time= 0.01562
Epoch: 0034 train_loss= 2.05914 train_acc= 0.15364 val_loss= 2.05409 val_acc= 0.10345 time= 0.01563
Epoch: 0035 train_loss= 2.05926 train_acc= 0.15903 val_loss= 2.05407 val_acc= 0.10345 time= 0.00000
Epoch: 0036 train_loss= 2.05978 train_acc= 0.15364 val_loss= 2.05408 val_acc= 0.10345 time= 0.01563
Epoch: 0037 train_loss= 2.05950 train_acc= 0.15364 val_loss= 2.05407 val_acc= 0.10345 time= 0.00000
Epoch: 0038 train_loss= 2.05974 train_acc= 0.15364 val_loss= 2.05402 val_acc= 0.10345 time= 0.01563
Epoch: 0039 train_loss= 2.05844 train_acc= 0.15094 val_loss= 2.05388 val_acc= 0.10345 time= 0.01563
Epoch: 0040 train_loss= 2.05797 train_acc= 0.15364 val_loss= 2.05367 val_acc= 0.10345 time= 0.00000
Epoch: 0041 train_loss= 2.05892 train_acc= 0.15633 val_loss= 2.05347 val_acc= 0.10345 time= 0.01563
Epoch: 0042 train_loss= 2.05848 train_acc= 0.15364 val_loss= 2.05327 val_acc= 0.10345 time= 0.00000
Epoch: 0043 train_loss= 2.05886 train_acc= 0.16442 val_loss= 2.05309 val_acc= 0.10345 time= 0.01563
Epoch: 0044 train_loss= 2.05884 train_acc= 0.15364 val_loss= 2.05287 val_acc= 0.10345 time= 0.01563
Epoch: 0045 train_loss= 2.05886 train_acc= 0.14555 val_loss= 2.05271 val_acc= 0.10345 time= 0.00000
Epoch: 0046 train_loss= 2.05761 train_acc= 0.19137 val_loss= 2.05259 val_acc= 0.10345 time= 0.01563
Epoch: 0047 train_loss= 2.05730 train_acc= 0.17251 val_loss= 2.05252 val_acc= 0.10345 time= 0.00000
Epoch: 0048 train_loss= 2.05848 train_acc= 0.16981 val_loss= 2.05250 val_acc= 0.10345 time= 0.01563
Epoch: 0049 train_loss= 2.05745 train_acc= 0.15903 val_loss= 2.05251 val_acc= 0.10345 time= 0.00000
Epoch: 0050 train_loss= 2.05758 train_acc= 0.15903 val_loss= 2.05256 val_acc= 0.10345 time= 0.01563
Epoch: 0051 train_loss= 2.05690 train_acc= 0.16173 val_loss= 2.05252 val_acc= 0.10345 time= 0.00000
Epoch: 0052 train_loss= 2.05779 train_acc= 0.16981 val_loss= 2.05250 val_acc= 0.10345 time= 0.01563
Epoch: 0053 train_loss= 2.05670 train_acc= 0.17790 val_loss= 2.05252 val_acc= 0.10345 time= 0.00000
Epoch: 0054 train_loss= 2.05617 train_acc= 0.16173 val_loss= 2.05253 val_acc= 0.10345 time= 0.01563
Epoch: 0055 train_loss= 2.05686 train_acc= 0.16442 val_loss= 2.05261 val_acc= 0.10345 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.06598 accuracy= 0.11864 time= 0.01562 
