Epoch: 0001 train_loss= 2.08776 train_acc= 0.05660 val_loss= 2.08446 val_acc= 0.17241 time= 0.12501
Epoch: 0002 train_loss= 2.08463 train_acc= 0.15094 val_loss= 2.08197 val_acc= 0.17241 time= 0.01563
Epoch: 0003 train_loss= 2.08219 train_acc= 0.15723 val_loss= 2.07950 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.08015 train_acc= 0.15723 val_loss= 2.07721 val_acc= 0.17241 time= 0.01563
Epoch: 0005 train_loss= 2.07813 train_acc= 0.15094 val_loss= 2.07514 val_acc= 0.17241 time= 0.00000
Epoch: 0006 train_loss= 2.07647 train_acc= 0.15723 val_loss= 2.07323 val_acc= 0.17241 time= 0.01563
Epoch: 0007 train_loss= 2.07468 train_acc= 0.15723 val_loss= 2.07146 val_acc= 0.17241 time= 0.00000
Epoch: 0008 train_loss= 2.07230 train_acc= 0.15723 val_loss= 2.06977 val_acc= 0.17241 time= 0.01563
Epoch: 0009 train_loss= 2.07048 train_acc= 0.15723 val_loss= 2.06819 val_acc= 0.17241 time= 0.01563
Epoch: 0010 train_loss= 2.07031 train_acc= 0.15723 val_loss= 2.06674 val_acc= 0.17241 time= 0.00000
Epoch: 0011 train_loss= 2.06831 train_acc= 0.15723 val_loss= 2.06531 val_acc= 0.17241 time= 0.01563
Epoch: 0012 train_loss= 2.06723 train_acc= 0.15094 val_loss= 2.06396 val_acc= 0.17241 time= 0.00000
Epoch: 0013 train_loss= 2.06559 train_acc= 0.15723 val_loss= 2.06257 val_acc= 0.17241 time= 0.01563
Epoch: 0014 train_loss= 2.06252 train_acc= 0.15723 val_loss= 2.06123 val_acc= 0.17241 time= 0.00000
Epoch: 0015 train_loss= 2.06210 train_acc= 0.15723 val_loss= 2.06003 val_acc= 0.17241 time= 0.01563
Epoch: 0016 train_loss= 2.05969 train_acc= 0.16352 val_loss= 2.05881 val_acc= 0.17241 time= 0.01563
Epoch: 0017 train_loss= 2.05816 train_acc= 0.15723 val_loss= 2.05766 val_acc= 0.17241 time= 0.00000
Epoch: 0018 train_loss= 2.05902 train_acc= 0.15723 val_loss= 2.05654 val_acc= 0.17241 time= 0.01563
Epoch: 0019 train_loss= 2.05694 train_acc= 0.15094 val_loss= 2.05524 val_acc= 0.17241 time= 0.00000
Epoch: 0020 train_loss= 2.05626 train_acc= 0.15723 val_loss= 2.05364 val_acc= 0.17241 time= 0.01563
Epoch: 0021 train_loss= 2.05478 train_acc= 0.15723 val_loss= 2.05183 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.05408 train_acc= 0.16352 val_loss= 2.04979 val_acc= 0.17241 time= 0.01563
Epoch: 0023 train_loss= 2.05274 train_acc= 0.14465 val_loss= 2.04758 val_acc= 0.17241 time= 0.01563
Epoch: 0024 train_loss= 2.05173 train_acc= 0.16981 val_loss= 2.04516 val_acc= 0.17241 time= 0.00000
Epoch: 0025 train_loss= 2.05005 train_acc= 0.16352 val_loss= 2.04274 val_acc= 0.20690 time= 0.01563
Epoch: 0026 train_loss= 2.04946 train_acc= 0.18239 val_loss= 2.04044 val_acc= 0.20690 time= 0.00000
Epoch: 0027 train_loss= 2.04939 train_acc= 0.20126 val_loss= 2.03811 val_acc= 0.20690 time= 0.01562
Epoch: 0028 train_loss= 2.04929 train_acc= 0.20126 val_loss= 2.03601 val_acc= 0.20690 time= 0.00000
Epoch: 0029 train_loss= 2.04696 train_acc= 0.19497 val_loss= 2.03398 val_acc= 0.20690 time= 0.01563
Epoch: 0030 train_loss= 2.04644 train_acc= 0.20126 val_loss= 2.03217 val_acc= 0.20690 time= 0.00000
Epoch: 0031 train_loss= 2.04694 train_acc= 0.19497 val_loss= 2.03055 val_acc= 0.20690 time= 0.01563
Epoch: 0032 train_loss= 2.04499 train_acc= 0.19497 val_loss= 2.02919 val_acc= 0.20690 time= 0.01563
Epoch: 0033 train_loss= 2.04448 train_acc= 0.19497 val_loss= 2.02789 val_acc= 0.20690 time= 0.00000
Epoch: 0034 train_loss= 2.04432 train_acc= 0.19497 val_loss= 2.02674 val_acc= 0.20690 time= 0.01563
Epoch: 0035 train_loss= 2.04305 train_acc= 0.19497 val_loss= 2.02566 val_acc= 0.20690 time= 0.00000
Epoch: 0036 train_loss= 2.04297 train_acc= 0.19497 val_loss= 2.02466 val_acc= 0.20690 time= 0.01563
Epoch: 0037 train_loss= 2.04276 train_acc= 0.19497 val_loss= 2.02379 val_acc= 0.20690 time= 0.00000
Epoch: 0038 train_loss= 2.04342 train_acc= 0.19497 val_loss= 2.02308 val_acc= 0.20690 time= 0.01563
Epoch: 0039 train_loss= 2.04343 train_acc= 0.19497 val_loss= 2.02240 val_acc= 0.20690 time= 0.01562
Epoch: 0040 train_loss= 2.04058 train_acc= 0.19497 val_loss= 2.02164 val_acc= 0.20690 time= 0.00000
Epoch: 0041 train_loss= 2.03924 train_acc= 0.19497 val_loss= 2.02093 val_acc= 0.20690 time= 0.01563
Epoch: 0042 train_loss= 2.04146 train_acc= 0.19497 val_loss= 2.02034 val_acc= 0.20690 time= 0.00000
Epoch: 0043 train_loss= 2.03968 train_acc= 0.19497 val_loss= 2.01975 val_acc= 0.20690 time= 0.01562
Epoch: 0044 train_loss= 2.03853 train_acc= 0.19497 val_loss= 2.01917 val_acc= 0.20690 time= 0.00000
Epoch: 0045 train_loss= 2.03918 train_acc= 0.19497 val_loss= 2.01871 val_acc= 0.20690 time= 0.01563
Epoch: 0046 train_loss= 2.04042 train_acc= 0.19497 val_loss= 2.01816 val_acc= 0.20690 time= 0.00000
Epoch: 0047 train_loss= 2.03882 train_acc= 0.19497 val_loss= 2.01753 val_acc= 0.20690 time= 0.01563
Epoch: 0048 train_loss= 2.03938 train_acc= 0.19497 val_loss= 2.01689 val_acc= 0.20690 time= 0.01563
Epoch: 0049 train_loss= 2.03848 train_acc= 0.19497 val_loss= 2.01631 val_acc= 0.20690 time= 0.00000
Epoch: 0050 train_loss= 2.04087 train_acc= 0.19497 val_loss= 2.01587 val_acc= 0.20690 time= 0.01563
Epoch: 0051 train_loss= 2.04034 train_acc= 0.19497 val_loss= 2.01563 val_acc= 0.20690 time= 0.00000
Epoch: 0052 train_loss= 2.03777 train_acc= 0.19497 val_loss= 2.01533 val_acc= 0.20690 time= 0.01562
Epoch: 0053 train_loss= 2.03895 train_acc= 0.19497 val_loss= 2.01486 val_acc= 0.20690 time= 0.00000
Epoch: 0054 train_loss= 2.03868 train_acc= 0.19497 val_loss= 2.01445 val_acc= 0.20690 time= 0.01563
Epoch: 0055 train_loss= 2.03744 train_acc= 0.19497 val_loss= 2.01399 val_acc= 0.20690 time= 0.00000
Epoch: 0056 train_loss= 2.03821 train_acc= 0.19497 val_loss= 2.01352 val_acc= 0.20690 time= 0.01563
Epoch: 0057 train_loss= 2.03777 train_acc= 0.19497 val_loss= 2.01311 val_acc= 0.20690 time= 0.00000
Epoch: 0058 train_loss= 2.03683 train_acc= 0.19497 val_loss= 2.01255 val_acc= 0.20690 time= 0.01563
Epoch: 0059 train_loss= 2.03838 train_acc= 0.19497 val_loss= 2.01209 val_acc= 0.20690 time= 0.00000
Epoch: 0060 train_loss= 2.03677 train_acc= 0.19497 val_loss= 2.01172 val_acc= 0.20690 time= 0.01563
Epoch: 0061 train_loss= 2.03621 train_acc= 0.19497 val_loss= 2.01161 val_acc= 0.20690 time= 0.01563
Epoch: 0062 train_loss= 2.03689 train_acc= 0.19497 val_loss= 2.01160 val_acc= 0.20690 time= 0.00000
Epoch: 0063 train_loss= 2.03739 train_acc= 0.19497 val_loss= 2.01173 val_acc= 0.20690 time= 0.01562
Epoch: 0064 train_loss= 2.03658 train_acc= 0.19497 val_loss= 2.01189 val_acc= 0.20690 time= 0.00000
Epoch: 0065 train_loss= 2.03483 train_acc= 0.19497 val_loss= 2.01211 val_acc= 0.20690 time= 0.01563
Epoch: 0066 train_loss= 2.03792 train_acc= 0.19497 val_loss= 2.01215 val_acc= 0.20690 time= 0.00000
Epoch: 0067 train_loss= 2.03495 train_acc= 0.19497 val_loss= 2.01236 val_acc= 0.20690 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.03548 accuracy= 0.11864 time= 0.00000 
