Epoch: 0001 train_loss= 2.09246 train_acc= 0.08805 val_loss= 2.08233 val_acc= 0.10345 time= 0.26564
Epoch: 0002 train_loss= 2.09056 train_acc= 0.08805 val_loss= 2.08076 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08470 train_acc= 0.10063 val_loss= 2.07941 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.08610 train_acc= 0.09434 val_loss= 2.07826 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.08118 train_acc= 0.16981 val_loss= 2.07725 val_acc= 0.17241 time= 0.01563
Epoch: 0006 train_loss= 2.07942 train_acc= 0.12579 val_loss= 2.07635 val_acc= 0.17241 time= 0.00000
Epoch: 0007 train_loss= 2.07690 train_acc= 0.17610 val_loss= 2.07552 val_acc= 0.17241 time= 0.00000
Epoch: 0008 train_loss= 2.07961 train_acc= 0.16981 val_loss= 2.07471 val_acc= 0.17241 time= 0.01562
Epoch: 0009 train_loss= 2.07504 train_acc= 0.16981 val_loss= 2.07392 val_acc= 0.17241 time= 0.00000
Epoch: 0010 train_loss= 2.07416 train_acc= 0.16352 val_loss= 2.07309 val_acc= 0.17241 time= 0.00000
Epoch: 0011 train_loss= 2.07323 train_acc= 0.16981 val_loss= 2.07221 val_acc= 0.17241 time= 0.01563
Epoch: 0012 train_loss= 2.07190 train_acc= 0.16981 val_loss= 2.07128 val_acc= 0.17241 time= 0.00000
Epoch: 0013 train_loss= 2.07012 train_acc= 0.16981 val_loss= 2.07030 val_acc= 0.17241 time= 0.00000
Epoch: 0014 train_loss= 2.06693 train_acc= 0.16981 val_loss= 2.06927 val_acc= 0.17241 time= 0.01563
Epoch: 0015 train_loss= 2.06613 train_acc= 0.17610 val_loss= 2.06812 val_acc= 0.17241 time= 0.00000
Epoch: 0016 train_loss= 2.06608 train_acc= 0.16981 val_loss= 2.06684 val_acc= 0.17241 time= 0.00000
Epoch: 0017 train_loss= 2.06302 train_acc= 0.16981 val_loss= 2.06541 val_acc= 0.17241 time= 0.00000
Epoch: 0018 train_loss= 2.06325 train_acc= 0.16981 val_loss= 2.06387 val_acc= 0.17241 time= 0.01563
Epoch: 0019 train_loss= 2.05679 train_acc= 0.16981 val_loss= 2.06224 val_acc= 0.17241 time= 0.00000
Epoch: 0020 train_loss= 2.05654 train_acc= 0.16981 val_loss= 2.06052 val_acc= 0.17241 time= 0.00000
Epoch: 0021 train_loss= 2.05641 train_acc= 0.16981 val_loss= 2.05872 val_acc= 0.17241 time= 0.01563
Epoch: 0022 train_loss= 2.04949 train_acc= 0.17610 val_loss= 2.05680 val_acc= 0.17241 time= 0.00000
Epoch: 0023 train_loss= 2.04705 train_acc= 0.16981 val_loss= 2.05479 val_acc= 0.17241 time= 0.00000
Epoch: 0024 train_loss= 2.05209 train_acc= 0.16981 val_loss= 2.05273 val_acc= 0.17241 time= 0.00000
Epoch: 0025 train_loss= 2.04773 train_acc= 0.16981 val_loss= 2.05064 val_acc= 0.17241 time= 0.01563
Epoch: 0026 train_loss= 2.04539 train_acc= 0.16981 val_loss= 2.04849 val_acc= 0.17241 time= 0.00000
Epoch: 0027 train_loss= 2.04313 train_acc= 0.16981 val_loss= 2.04626 val_acc= 0.17241 time= 0.00000
Epoch: 0028 train_loss= 2.03762 train_acc= 0.16981 val_loss= 2.04397 val_acc= 0.17241 time= 0.01563
Epoch: 0029 train_loss= 2.03625 train_acc= 0.16981 val_loss= 2.04162 val_acc= 0.17241 time= 0.00000
Epoch: 0030 train_loss= 2.02920 train_acc= 0.16981 val_loss= 2.03925 val_acc= 0.17241 time= 0.00000
Epoch: 0031 train_loss= 2.03361 train_acc= 0.16981 val_loss= 2.03690 val_acc= 0.17241 time= 0.01563
Epoch: 0032 train_loss= 2.03390 train_acc= 0.16981 val_loss= 2.03458 val_acc= 0.17241 time= 0.00000
Epoch: 0033 train_loss= 2.03056 train_acc= 0.16981 val_loss= 2.03238 val_acc= 0.17241 time= 0.00000
Epoch: 0034 train_loss= 2.02640 train_acc= 0.16981 val_loss= 2.03043 val_acc= 0.17241 time= 0.00000
Epoch: 0035 train_loss= 2.02833 train_acc= 0.16981 val_loss= 2.02862 val_acc= 0.17241 time= 0.01563
Epoch: 0036 train_loss= 2.02433 train_acc= 0.16981 val_loss= 2.02694 val_acc= 0.17241 time= 0.00000
Epoch: 0037 train_loss= 2.02553 train_acc= 0.16981 val_loss= 2.02544 val_acc= 0.17241 time= 0.00000
Epoch: 0038 train_loss= 2.01726 train_acc= 0.16981 val_loss= 2.02417 val_acc= 0.17241 time= 0.01563
Epoch: 0039 train_loss= 2.02223 train_acc= 0.17610 val_loss= 2.02308 val_acc= 0.17241 time= 0.00000
Epoch: 0040 train_loss= 2.02245 train_acc= 0.17610 val_loss= 2.02214 val_acc= 0.17241 time= 0.00000
Epoch: 0041 train_loss= 2.02085 train_acc= 0.17610 val_loss= 2.02131 val_acc= 0.17241 time= 0.01563
Epoch: 0042 train_loss= 2.01967 train_acc= 0.25157 val_loss= 2.02063 val_acc= 0.13793 time= 0.00000
Epoch: 0043 train_loss= 2.02319 train_acc= 0.23270 val_loss= 2.02016 val_acc= 0.13793 time= 0.00000
Epoch: 0044 train_loss= 2.01977 train_acc= 0.21384 val_loss= 2.01988 val_acc= 0.13793 time= 0.00000
Epoch: 0045 train_loss= 2.01560 train_acc= 0.22013 val_loss= 2.01974 val_acc= 0.13793 time= 0.01563
Epoch: 0046 train_loss= 2.01699 train_acc= 0.22013 val_loss= 2.01974 val_acc= 0.13793 time= 0.00000
Epoch: 0047 train_loss= 2.01740 train_acc= 0.22013 val_loss= 2.01977 val_acc= 0.13793 time= 0.00000
Epoch: 0048 train_loss= 2.02033 train_acc= 0.21384 val_loss= 2.01994 val_acc= 0.13793 time= 0.01563
Epoch: 0049 train_loss= 2.02232 train_acc= 0.22013 val_loss= 2.02027 val_acc= 0.13793 time= 0.00000
Epoch: 0050 train_loss= 2.01741 train_acc= 0.22013 val_loss= 2.02057 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.11613 accuracy= 0.06780 time= 0.00000 
