Epoch: 0001 train_loss= 2.10882 train_acc= 0.11321 val_loss= 2.13201 val_acc= 0.06897 time= 0.54771
Epoch: 0002 train_loss= 2.10177 train_acc= 0.11321 val_loss= 2.12446 val_acc= 0.06897 time= 0.00000
Epoch: 0003 train_loss= 2.09742 train_acc= 0.13585 val_loss= 2.11811 val_acc= 0.06897 time= 0.00000
Epoch: 0004 train_loss= 2.09293 train_acc= 0.13208 val_loss= 2.11290 val_acc= 0.06897 time= 0.01563
Epoch: 0005 train_loss= 2.09182 train_acc= 0.13585 val_loss= 2.10849 val_acc= 0.03448 time= 0.00000
Epoch: 0006 train_loss= 2.08919 train_acc= 0.14340 val_loss= 2.10479 val_acc= 0.06897 time= 0.01562
Epoch: 0007 train_loss= 2.08354 train_acc= 0.15849 val_loss= 2.10173 val_acc= 0.06897 time= 0.00000
Epoch: 0008 train_loss= 2.08202 train_acc= 0.15472 val_loss= 2.09923 val_acc= 0.06897 time= 0.00000
Epoch: 0009 train_loss= 2.07987 train_acc= 0.15849 val_loss= 2.09718 val_acc= 0.06897 time= 0.01563
Epoch: 0010 train_loss= 2.07797 train_acc= 0.15849 val_loss= 2.09549 val_acc= 0.06897 time= 0.00000
Epoch: 0011 train_loss= 2.07662 train_acc= 0.15849 val_loss= 2.09411 val_acc= 0.06897 time= 0.00000
Epoch: 0012 train_loss= 2.07502 train_acc= 0.15849 val_loss= 2.09292 val_acc= 0.06897 time= 0.01562
Epoch: 0013 train_loss= 2.07293 train_acc= 0.15849 val_loss= 2.09177 val_acc= 0.06897 time= 0.00000
Epoch: 0014 train_loss= 2.07202 train_acc= 0.15849 val_loss= 2.09082 val_acc= 0.06897 time= 0.00000
Epoch: 0015 train_loss= 2.07140 train_acc= 0.15849 val_loss= 2.09003 val_acc= 0.06897 time= 0.01563
Epoch: 0016 train_loss= 2.06909 train_acc= 0.15849 val_loss= 2.08940 val_acc= 0.06897 time= 0.00000
Epoch: 0017 train_loss= 2.06824 train_acc= 0.15849 val_loss= 2.08893 val_acc= 0.06897 time= 0.00000
Epoch: 0018 train_loss= 2.06925 train_acc= 0.15849 val_loss= 2.08841 val_acc= 0.06897 time= 0.01563
Epoch: 0019 train_loss= 2.06757 train_acc= 0.15849 val_loss= 2.08768 val_acc= 0.06897 time= 0.00000
Epoch: 0020 train_loss= 2.06489 train_acc= 0.15849 val_loss= 2.08672 val_acc= 0.06897 time= 0.00000
Epoch: 0021 train_loss= 2.06516 train_acc= 0.15849 val_loss= 2.08543 val_acc= 0.06897 time= 0.02039
Epoch: 0022 train_loss= 2.06483 train_acc= 0.15849 val_loss= 2.08388 val_acc= 0.06897 time= 0.00000
Epoch: 0023 train_loss= 2.06423 train_acc= 0.15849 val_loss= 2.08210 val_acc= 0.06897 time= 0.01100
Epoch: 0024 train_loss= 2.06127 train_acc= 0.15849 val_loss= 2.07997 val_acc= 0.06897 time= 0.00000
Epoch: 0025 train_loss= 2.05934 train_acc= 0.15849 val_loss= 2.07756 val_acc= 0.06897 time= 0.00000
Epoch: 0026 train_loss= 2.05778 train_acc= 0.15849 val_loss= 2.07495 val_acc= 0.06897 time= 0.00000
Epoch: 0027 train_loss= 2.05748 train_acc= 0.15849 val_loss= 2.07213 val_acc= 0.06897 time= 0.00000
Epoch: 0028 train_loss= 2.05531 train_acc= 0.15849 val_loss= 2.06908 val_acc= 0.06897 time= 0.00000
Epoch: 0029 train_loss= 2.05570 train_acc= 0.16604 val_loss= 2.06578 val_acc= 0.20690 time= 0.00000
Epoch: 0030 train_loss= 2.05406 train_acc= 0.15472 val_loss= 2.06249 val_acc= 0.24138 time= 0.01563
Epoch: 0031 train_loss= 2.05446 train_acc= 0.15849 val_loss= 2.05943 val_acc= 0.24138 time= 0.00000
Epoch: 0032 train_loss= 2.04851 train_acc= 0.15472 val_loss= 2.05656 val_acc= 0.24138 time= 0.00000
Epoch: 0033 train_loss= 2.04995 train_acc= 0.15849 val_loss= 2.05394 val_acc= 0.24138 time= 0.01563
Epoch: 0034 train_loss= 2.04913 train_acc= 0.16226 val_loss= 2.05164 val_acc= 0.24138 time= 0.00000
Epoch: 0035 train_loss= 2.05029 train_acc= 0.15849 val_loss= 2.04995 val_acc= 0.24138 time= 0.00000
Epoch: 0036 train_loss= 2.04781 train_acc= 0.15849 val_loss= 2.04874 val_acc= 0.24138 time= 0.01563
Epoch: 0037 train_loss= 2.04611 train_acc= 0.15849 val_loss= 2.04813 val_acc= 0.24138 time= 0.00000
Epoch: 0038 train_loss= 2.04691 train_acc= 0.15849 val_loss= 2.04821 val_acc= 0.24138 time= 0.00000
Epoch: 0039 train_loss= 2.04844 train_acc= 0.15849 val_loss= 2.04897 val_acc= 0.24138 time= 0.01563
Epoch: 0040 train_loss= 2.04241 train_acc= 0.16226 val_loss= 2.05049 val_acc= 0.24138 time= 0.00000
Epoch: 0041 train_loss= 2.04816 train_acc= 0.16226 val_loss= 2.05261 val_acc= 0.24138 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.10655 accuracy= 0.13559 time= 0.01563 
