Epoch: 0001 train_loss= 2.08769 train_acc= 0.10566 val_loss= 2.08522 val_acc= 0.06897 time= 0.23439
Epoch: 0002 train_loss= 2.08507 train_acc= 0.13585 val_loss= 2.08303 val_acc= 0.06897 time= 0.00000
Epoch: 0003 train_loss= 2.08307 train_acc= 0.12830 val_loss= 2.08092 val_acc= 0.06897 time= 0.01563
Epoch: 0004 train_loss= 2.08093 train_acc= 0.13585 val_loss= 2.07894 val_acc= 0.06897 time= 0.01563
Epoch: 0005 train_loss= 2.07921 train_acc= 0.13208 val_loss= 2.07698 val_acc= 0.06897 time= 0.00000
Epoch: 0006 train_loss= 2.07761 train_acc= 0.13208 val_loss= 2.07489 val_acc= 0.06897 time= 0.01563
Epoch: 0007 train_loss= 2.07599 train_acc= 0.13208 val_loss= 2.07264 val_acc= 0.06897 time= 0.00000
Epoch: 0008 train_loss= 2.07458 train_acc= 0.12453 val_loss= 2.07015 val_acc= 0.06897 time= 0.01563
Epoch: 0009 train_loss= 2.07354 train_acc= 0.12830 val_loss= 2.06747 val_acc= 0.06897 time= 0.00000
Epoch: 0010 train_loss= 2.07197 train_acc= 0.13208 val_loss= 2.06459 val_acc= 0.06897 time= 0.01563
Epoch: 0011 train_loss= 2.07131 train_acc= 0.13208 val_loss= 2.06139 val_acc= 0.06897 time= 0.01563
Epoch: 0012 train_loss= 2.06974 train_acc= 0.13208 val_loss= 2.05790 val_acc= 0.06897 time= 0.00000
Epoch: 0013 train_loss= 2.06804 train_acc= 0.10943 val_loss= 2.05414 val_acc= 0.10345 time= 0.01563
Epoch: 0014 train_loss= 2.06821 train_acc= 0.12830 val_loss= 2.05008 val_acc= 0.10345 time= 0.00000
Epoch: 0015 train_loss= 2.06550 train_acc= 0.15094 val_loss= 2.04575 val_acc= 0.10345 time= 0.01563
Epoch: 0016 train_loss= 2.06582 train_acc= 0.15472 val_loss= 2.04130 val_acc= 0.10345 time= 0.01563
Epoch: 0017 train_loss= 2.06534 train_acc= 0.15094 val_loss= 2.03671 val_acc= 0.10345 time= 0.00000
Epoch: 0018 train_loss= 2.06290 train_acc= 0.14717 val_loss= 2.03202 val_acc= 0.10345 time= 0.01562
Epoch: 0019 train_loss= 2.06318 train_acc= 0.14717 val_loss= 2.02732 val_acc= 0.10345 time= 0.00000
Epoch: 0020 train_loss= 2.06141 train_acc= 0.16604 val_loss= 2.02269 val_acc= 0.10345 time= 0.01563
Epoch: 0021 train_loss= 2.06171 train_acc= 0.13585 val_loss= 2.01820 val_acc= 0.10345 time= 0.00000
Epoch: 0022 train_loss= 2.06043 train_acc= 0.11698 val_loss= 2.01411 val_acc= 0.27586 time= 0.01563
Epoch: 0023 train_loss= 2.06107 train_acc= 0.14717 val_loss= 2.01031 val_acc= 0.27586 time= 0.01563
Epoch: 0024 train_loss= 2.06030 train_acc= 0.15094 val_loss= 2.00702 val_acc= 0.27586 time= 0.00000
Epoch: 0025 train_loss= 2.05894 train_acc= 0.15472 val_loss= 2.00422 val_acc= 0.27586 time= 0.01563
Epoch: 0026 train_loss= 2.05759 train_acc= 0.16226 val_loss= 2.00187 val_acc= 0.27586 time= 0.00000
Epoch: 0027 train_loss= 2.05577 train_acc= 0.16226 val_loss= 1.99982 val_acc= 0.27586 time= 0.01563
Epoch: 0028 train_loss= 2.05620 train_acc= 0.15849 val_loss= 1.99801 val_acc= 0.27586 time= 0.00000
Epoch: 0029 train_loss= 2.05641 train_acc= 0.16604 val_loss= 1.99643 val_acc= 0.27586 time= 0.01563
Epoch: 0030 train_loss= 2.05544 train_acc= 0.16226 val_loss= 1.99504 val_acc= 0.27586 time= 0.01563
Epoch: 0031 train_loss= 2.05635 train_acc= 0.17736 val_loss= 1.99390 val_acc= 0.27586 time= 0.00000
Epoch: 0032 train_loss= 2.05313 train_acc= 0.16604 val_loss= 1.99294 val_acc= 0.27586 time= 0.01563
Epoch: 0033 train_loss= 2.05341 train_acc= 0.15472 val_loss= 1.99224 val_acc= 0.27586 time= 0.00000
Epoch: 0034 train_loss= 2.05446 train_acc= 0.16981 val_loss= 1.99159 val_acc= 0.27586 time= 0.01563
Epoch: 0035 train_loss= 2.05202 train_acc= 0.16604 val_loss= 1.99101 val_acc= 0.27586 time= 0.01563
Epoch: 0036 train_loss= 2.05264 train_acc= 0.15849 val_loss= 1.99034 val_acc= 0.27586 time= 0.00000
Epoch: 0037 train_loss= 2.05100 train_acc= 0.16226 val_loss= 1.98971 val_acc= 0.27586 time= 0.01563
Epoch: 0038 train_loss= 2.05184 train_acc= 0.15849 val_loss= 1.98903 val_acc= 0.27586 time= 0.00000
Epoch: 0039 train_loss= 2.05108 train_acc= 0.16226 val_loss= 1.98822 val_acc= 0.27586 time= 0.01563
Epoch: 0040 train_loss= 2.05140 train_acc= 0.15849 val_loss= 1.98745 val_acc= 0.27586 time= 0.00000
Epoch: 0041 train_loss= 2.05042 train_acc= 0.17358 val_loss= 1.98681 val_acc= 0.27586 time= 0.01563
Epoch: 0042 train_loss= 2.05022 train_acc= 0.16226 val_loss= 1.98594 val_acc= 0.27586 time= 0.01563
Epoch: 0043 train_loss= 2.05044 train_acc= 0.16226 val_loss= 1.98490 val_acc= 0.27586 time= 0.00000
Epoch: 0044 train_loss= 2.05105 train_acc= 0.16226 val_loss= 1.98361 val_acc= 0.27586 time= 0.01563
Epoch: 0045 train_loss= 2.04944 train_acc= 0.15849 val_loss= 1.98248 val_acc= 0.27586 time= 0.00000
Epoch: 0046 train_loss= 2.05046 train_acc= 0.16226 val_loss= 1.98178 val_acc= 0.27586 time= 0.01563
Epoch: 0047 train_loss= 2.04955 train_acc= 0.15849 val_loss= 1.98151 val_acc= 0.27586 time= 0.01563
Epoch: 0048 train_loss= 2.04969 train_acc= 0.16226 val_loss= 1.98146 val_acc= 0.27586 time= 0.00000
Epoch: 0049 train_loss= 2.04946 train_acc= 0.15849 val_loss= 1.98140 val_acc= 0.27586 time= 0.01563
Epoch: 0050 train_loss= 2.04770 train_acc= 0.16604 val_loss= 1.98113 val_acc= 0.27586 time= 0.00000
Epoch: 0051 train_loss= 2.04862 train_acc= 0.16604 val_loss= 1.98084 val_acc= 0.27586 time= 0.01563
Epoch: 0052 train_loss= 2.04673 train_acc= 0.16226 val_loss= 1.98082 val_acc= 0.27586 time= 0.01563
Epoch: 0053 train_loss= 2.04816 train_acc= 0.16981 val_loss= 1.98087 val_acc= 0.27586 time= 0.00000
Epoch: 0054 train_loss= 2.04857 train_acc= 0.15849 val_loss= 1.98089 val_acc= 0.27586 time= 0.01563
Epoch: 0055 train_loss= 2.04853 train_acc= 0.15849 val_loss= 1.98096 val_acc= 0.27586 time= 0.00000
Epoch: 0056 train_loss= 2.04838 train_acc= 0.16226 val_loss= 1.98075 val_acc= 0.27586 time= 0.01563
Epoch: 0057 train_loss= 2.04757 train_acc= 0.16226 val_loss= 1.98037 val_acc= 0.27586 time= 0.00000
Epoch: 0058 train_loss= 2.04687 train_acc= 0.16981 val_loss= 1.98004 val_acc= 0.27586 time= 0.01563
Epoch: 0059 train_loss= 2.04715 train_acc= 0.16226 val_loss= 1.97971 val_acc= 0.27586 time= 0.01563
Epoch: 0060 train_loss= 2.04590 train_acc= 0.15849 val_loss= 1.97877 val_acc= 0.27586 time= 0.00000
Epoch: 0061 train_loss= 2.04803 train_acc= 0.15849 val_loss= 1.97797 val_acc= 0.27586 time= 0.01563
Epoch: 0062 train_loss= 2.04740 train_acc= 0.16226 val_loss= 1.97679 val_acc= 0.27586 time= 0.00000
Epoch: 0063 train_loss= 2.04586 train_acc= 0.16604 val_loss= 1.97575 val_acc= 0.27586 time= 0.01563
Epoch: 0064 train_loss= 2.04799 train_acc= 0.16226 val_loss= 1.97493 val_acc= 0.27586 time= 0.01563
Epoch: 0065 train_loss= 2.04758 train_acc= 0.16226 val_loss= 1.97480 val_acc= 0.27586 time= 0.00000
Epoch: 0066 train_loss= 2.04569 train_acc= 0.16226 val_loss= 1.97506 val_acc= 0.27586 time= 0.01563
Epoch: 0067 train_loss= 2.04648 train_acc= 0.16604 val_loss= 1.97552 val_acc= 0.27586 time= 0.00000
Epoch: 0068 train_loss= 2.04638 train_acc= 0.15849 val_loss= 1.97589 val_acc= 0.27586 time= 0.01563
Epoch: 0069 train_loss= 2.04560 train_acc= 0.15849 val_loss= 1.97649 val_acc= 0.27586 time= 0.01563
Epoch: 0070 train_loss= 2.04767 train_acc= 0.16226 val_loss= 1.97681 val_acc= 0.27586 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.10863 accuracy= 0.15254 time= 0.01563 
