Epoch: 0001 train_loss= 1.61005 train_acc= 0.50130 val_loss= 1.24562 val_acc= 0.50820 time= 0.28127
Epoch: 0002 train_loss= 1.81825 train_acc= 0.50390 val_loss= 1.08879 val_acc= 0.50820 time= 0.01562
Epoch: 0003 train_loss= 1.30097 train_acc= 0.49481 val_loss= 0.95798 val_acc= 0.50820 time= 0.01563
Epoch: 0004 train_loss= 1.30053 train_acc= 0.51169 val_loss= 0.87816 val_acc= 0.49180 time= 0.01563
Epoch: 0005 train_loss= 0.85269 train_acc= 0.50130 val_loss= 0.83535 val_acc= 0.49180 time= 0.00000
Epoch: 0006 train_loss= 0.89086 train_acc= 0.50260 val_loss= 0.82966 val_acc= 0.40984 time= 0.01563
Epoch: 0007 train_loss= 0.90891 train_acc= 0.51169 val_loss= 0.84667 val_acc= 0.47541 time= 0.01563
Epoch: 0008 train_loss= 0.81601 train_acc= 0.50779 val_loss= 0.86391 val_acc= 0.49180 time= 0.01563
Epoch: 0009 train_loss= 1.06333 train_acc= 0.48701 val_loss= 0.87465 val_acc= 0.45902 time= 0.01562
Epoch: 0010 train_loss= 0.81418 train_acc= 0.48571 val_loss= 0.87419 val_acc= 0.45902 time= 0.01563
Epoch: 0011 train_loss= 0.91388 train_acc= 0.48701 val_loss= 0.87046 val_acc= 0.45902 time= 0.00000
Epoch: 0012 train_loss= 0.90445 train_acc= 0.51818 val_loss= 0.85903 val_acc= 0.45902 time= 0.01563
Epoch: 0013 train_loss= 0.83678 train_acc= 0.48442 val_loss= 0.85254 val_acc= 0.45902 time= 0.01563
Epoch: 0014 train_loss= 1.01059 train_acc= 0.50909 val_loss= 0.83917 val_acc= 0.44262 time= 0.01562
Epoch: 0015 train_loss= 0.89903 train_acc= 0.50130 val_loss= 0.82224 val_acc= 0.45902 time= 0.01563
Epoch: 0016 train_loss= 0.78949 train_acc= 0.48182 val_loss= 0.80702 val_acc= 0.49180 time= 0.01563
Epoch: 0017 train_loss= 0.72490 train_acc= 0.52857 val_loss= 0.79356 val_acc= 0.49180 time= 0.01563
Epoch: 0018 train_loss= 0.74680 train_acc= 0.49221 val_loss= 0.78512 val_acc= 0.50820 time= 0.01563
Epoch: 0019 train_loss= 0.76031 train_acc= 0.51299 val_loss= 0.77862 val_acc= 0.50820 time= 0.00000
Epoch: 0020 train_loss= 0.79430 train_acc= 0.48182 val_loss= 0.77308 val_acc= 0.52459 time= 0.01562
Epoch: 0021 train_loss= 0.81925 train_acc= 0.48701 val_loss= 0.76959 val_acc= 0.50820 time= 0.01563
Epoch: 0022 train_loss= 0.88328 train_acc= 0.46753 val_loss= 0.76490 val_acc= 0.49180 time= 0.01563
Epoch: 0023 train_loss= 0.77422 train_acc= 0.53247 val_loss= 0.76169 val_acc= 0.50820 time= 0.01563
Epoch: 0024 train_loss= 0.78392 train_acc= 0.50649 val_loss= 0.75946 val_acc= 0.49180 time= 0.00000
Epoch: 0025 train_loss= 0.79847 train_acc= 0.51429 val_loss= 0.75758 val_acc= 0.52459 time= 0.01563
Epoch: 0026 train_loss= 0.75635 train_acc= 0.52208 val_loss= 0.75628 val_acc= 0.50820 time= 0.01563
Epoch: 0027 train_loss= 0.78532 train_acc= 0.50260 val_loss= 0.75480 val_acc= 0.50820 time= 0.01563
Epoch: 0028 train_loss= 0.74361 train_acc= 0.48701 val_loss= 0.75320 val_acc= 0.50820 time= 0.01563
Epoch: 0029 train_loss= 0.72331 train_acc= 0.50909 val_loss= 0.75148 val_acc= 0.50820 time= 0.01563
Epoch: 0030 train_loss= 0.75250 train_acc= 0.50649 val_loss= 0.75008 val_acc= 0.49180 time= 0.01563
Epoch: 0031 train_loss= 0.75399 train_acc= 0.51688 val_loss= 0.74870 val_acc= 0.49180 time= 0.00000
Epoch: 0032 train_loss= 0.92552 train_acc= 0.50000 val_loss= 0.74624 val_acc= 0.49180 time= 0.01563
Epoch: 0033 train_loss= 0.74054 train_acc= 0.49481 val_loss= 0.74394 val_acc= 0.50820 time= 0.01563
Epoch: 0034 train_loss= 0.72064 train_acc= 0.51429 val_loss= 0.74181 val_acc= 0.50820 time= 0.01563
Epoch: 0035 train_loss= 0.79067 train_acc= 0.50390 val_loss= 0.73994 val_acc= 0.50820 time= 0.01562
Epoch: 0036 train_loss= 0.71791 train_acc= 0.49221 val_loss= 0.73818 val_acc= 0.50820 time= 0.01563
Epoch: 0037 train_loss= 0.74893 train_acc= 0.53117 val_loss= 0.73686 val_acc= 0.49180 time= 0.00000
Epoch: 0038 train_loss= 0.72032 train_acc= 0.50779 val_loss= 0.73591 val_acc= 0.47541 time= 0.01562
Epoch: 0039 train_loss= 0.77497 train_acc= 0.49091 val_loss= 0.73400 val_acc= 0.49180 time= 0.01563
Epoch: 0040 train_loss= 0.74448 train_acc= 0.51169 val_loss= 0.73273 val_acc= 0.49180 time= 0.01563
Epoch: 0041 train_loss= 0.70932 train_acc= 0.53896 val_loss= 0.73157 val_acc= 0.49180 time= 0.01563
Epoch: 0042 train_loss= 0.76655 train_acc= 0.49221 val_loss= 0.72970 val_acc= 0.47541 time= 0.01563
Epoch: 0043 train_loss= 0.73022 train_acc= 0.51039 val_loss= 0.72815 val_acc= 0.47541 time= 0.01563
Epoch: 0044 train_loss= 0.75328 train_acc= 0.51558 val_loss= 0.72687 val_acc= 0.47541 time= 0.01563
Epoch: 0045 train_loss= 0.72756 train_acc= 0.52208 val_loss= 0.72590 val_acc= 0.47541 time= 0.00000
Epoch: 0046 train_loss= 0.71255 train_acc= 0.51818 val_loss= 0.72505 val_acc= 0.47541 time= 0.01563
Epoch: 0047 train_loss= 0.70525 train_acc= 0.50260 val_loss= 0.72421 val_acc= 0.45902 time= 0.01563
Epoch: 0048 train_loss= 0.74695 train_acc= 0.47273 val_loss= 0.72334 val_acc= 0.44262 time= 0.01563
Epoch: 0049 train_loss= 0.71084 train_acc= 0.52727 val_loss= 0.72256 val_acc= 0.47541 time= 0.01563
Epoch: 0050 train_loss= 0.70771 train_acc= 0.49481 val_loss= 0.72185 val_acc= 0.47541 time= 0.01563
Epoch: 0051 train_loss= 0.70208 train_acc= 0.52468 val_loss= 0.72104 val_acc= 0.47541 time= 0.00000
Epoch: 0052 train_loss= 0.69884 train_acc= 0.53117 val_loss= 0.72037 val_acc= 0.47541 time= 0.01563
Epoch: 0053 train_loss= 0.70877 train_acc= 0.50000 val_loss= 0.71965 val_acc= 0.49180 time= 0.01563
Epoch: 0054 train_loss= 0.70660 train_acc= 0.50260 val_loss= 0.71902 val_acc= 0.49180 time= 0.01563
Epoch: 0055 train_loss= 0.70964 train_acc= 0.51039 val_loss= 0.71862 val_acc= 0.49180 time= 0.01563
Epoch: 0056 train_loss= 0.74313 train_acc= 0.50260 val_loss= 0.71815 val_acc= 0.49180 time= 0.01563
Epoch: 0057 train_loss= 0.71362 train_acc= 0.51299 val_loss= 0.71771 val_acc= 0.49180 time= 0.00000
Epoch: 0058 train_loss= 0.72592 train_acc= 0.52468 val_loss= 0.71727 val_acc= 0.49180 time= 0.01563
Epoch: 0059 train_loss= 0.71693 train_acc= 0.50390 val_loss= 0.71692 val_acc= 0.50820 time= 0.01562
Epoch: 0060 train_loss= 0.70402 train_acc= 0.51429 val_loss= 0.71683 val_acc= 0.50820 time= 0.01563
Epoch: 0061 train_loss= 0.70317 train_acc= 0.49740 val_loss= 0.71667 val_acc= 0.47541 time= 0.01563
Epoch: 0062 train_loss= 0.71133 train_acc= 0.50779 val_loss= 0.71644 val_acc= 0.47541 time= 0.01563
Epoch: 0063 train_loss= 0.70692 train_acc= 0.50519 val_loss= 0.71613 val_acc= 0.47541 time= 0.00000
Epoch: 0064 train_loss= 0.75933 train_acc= 0.48312 val_loss= 0.71597 val_acc= 0.47541 time= 0.01562
Epoch: 0065 train_loss= 0.72106 train_acc= 0.51169 val_loss= 0.71589 val_acc= 0.47541 time= 0.01563
Epoch: 0066 train_loss= 0.70839 train_acc= 0.49740 val_loss= 0.71576 val_acc= 0.49180 time= 0.01563
Epoch: 0067 train_loss= 0.69750 train_acc= 0.54026 val_loss= 0.71552 val_acc= 0.49180 time= 0.01562
Epoch: 0068 train_loss= 0.70837 train_acc= 0.50909 val_loss= 0.71535 val_acc= 0.49180 time= 0.01563
Epoch: 0069 train_loss= 0.70058 train_acc= 0.49091 val_loss= 0.71519 val_acc= 0.49180 time= 0.00000
Epoch: 0070 train_loss= 0.70753 train_acc= 0.51039 val_loss= 0.71500 val_acc= 0.49180 time= 0.01563
Epoch: 0071 train_loss= 0.70907 train_acc= 0.47403 val_loss= 0.71479 val_acc= 0.49180 time= 0.01562
Epoch: 0072 train_loss= 0.72166 train_acc= 0.47013 val_loss= 0.71446 val_acc= 0.49180 time= 0.01563
Epoch: 0073 train_loss= 0.70840 train_acc= 0.51948 val_loss= 0.71416 val_acc= 0.50820 time= 0.01563
Epoch: 0074 train_loss= 0.70007 train_acc= 0.52078 val_loss= 0.71384 val_acc= 0.50820 time= 0.00000
Epoch: 0075 train_loss= 0.72057 train_acc= 0.50779 val_loss= 0.71366 val_acc= 0.52459 time= 0.01563
Epoch: 0076 train_loss= 0.70026 train_acc= 0.53117 val_loss= 0.71349 val_acc= 0.50820 time= 0.01562
Epoch: 0077 train_loss= 0.70480 train_acc= 0.52468 val_loss= 0.71325 val_acc= 0.50820 time= 0.01563
Epoch: 0078 train_loss= 0.70734 train_acc= 0.50260 val_loss= 0.71296 val_acc= 0.52459 time= 0.01563
Epoch: 0079 train_loss= 0.71128 train_acc= 0.50909 val_loss= 0.71271 val_acc= 0.52459 time= 0.01563
Epoch: 0080 train_loss= 0.70032 train_acc= 0.52468 val_loss= 0.71267 val_acc= 0.52459 time= 0.00000
Epoch: 0081 train_loss= 0.69600 train_acc= 0.51429 val_loss= 0.71258 val_acc= 0.52459 time= 0.01563
Epoch: 0082 train_loss= 0.70287 train_acc= 0.51688 val_loss= 0.71236 val_acc= 0.52459 time= 0.01562
Epoch: 0083 train_loss= 0.69273 train_acc= 0.54026 val_loss= 0.71216 val_acc= 0.52459 time= 0.01563
Epoch: 0084 train_loss= 0.70071 train_acc= 0.52078 val_loss= 0.71188 val_acc= 0.52459 time= 0.01563
Epoch: 0085 train_loss= 0.72214 train_acc= 0.50519 val_loss= 0.71172 val_acc= 0.52459 time= 0.01563
Epoch: 0086 train_loss= 0.69743 train_acc= 0.50519 val_loss= 0.71159 val_acc= 0.54098 time= 0.00000
Epoch: 0087 train_loss= 0.70419 train_acc= 0.49221 val_loss= 0.71135 val_acc= 0.54098 time= 0.01563
Epoch: 0088 train_loss= 0.69979 train_acc= 0.50909 val_loss= 0.71116 val_acc= 0.50820 time= 0.01563
Epoch: 0089 train_loss= 0.69950 train_acc= 0.52468 val_loss= 0.71093 val_acc= 0.50820 time= 0.01563
Epoch: 0090 train_loss= 0.70310 train_acc= 0.51299 val_loss= 0.71069 val_acc= 0.50820 time= 0.01563
Epoch: 0091 train_loss= 0.69713 train_acc= 0.51429 val_loss= 0.71051 val_acc= 0.50820 time= 0.01563
Epoch: 0092 train_loss= 0.69575 train_acc= 0.49870 val_loss= 0.71030 val_acc= 0.50820 time= 0.00000
Epoch: 0093 train_loss= 0.70131 train_acc= 0.50779 val_loss= 0.71006 val_acc= 0.50820 time= 0.01563
Epoch: 0094 train_loss= 0.70975 train_acc= 0.48831 val_loss= 0.71003 val_acc= 0.50820 time= 0.01563
Epoch: 0095 train_loss= 0.69999 train_acc= 0.50260 val_loss= 0.70992 val_acc= 0.50820 time= 0.01563
Epoch: 0096 train_loss= 0.69482 train_acc= 0.50649 val_loss= 0.70982 val_acc= 0.50820 time= 0.01563
Epoch: 0097 train_loss= 0.69757 train_acc= 0.53117 val_loss= 0.70978 val_acc= 0.50820 time= 0.00000
Epoch: 0098 train_loss= 0.69545 train_acc= 0.51688 val_loss= 0.70977 val_acc= 0.50820 time= 0.01562
Epoch: 0099 train_loss= 0.71600 train_acc= 0.49091 val_loss= 0.70976 val_acc= 0.50820 time= 0.01563
Epoch: 0100 train_loss= 0.69375 train_acc= 0.50649 val_loss= 0.70972 val_acc= 0.50820 time= 0.01563
Epoch: 0101 train_loss= 0.69570 train_acc= 0.52338 val_loss= 0.70972 val_acc= 0.50820 time= 0.01563
Epoch: 0102 train_loss= 0.70309 train_acc= 0.50779 val_loss= 0.70987 val_acc= 0.50820 time= 0.00000
Epoch: 0103 train_loss= 0.70790 train_acc= 0.49221 val_loss= 0.70996 val_acc= 0.50820 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69454 accuracy= 0.54098 time= 0.01563 
