Epoch: 0001 train_loss= 0.70104 train_acc= 0.50000 val_loss= 0.69723 val_acc= 0.56452 time= 0.20314
Epoch: 0002 train_loss= 0.69761 train_acc= 0.53939 val_loss= 0.69438 val_acc= 0.54839 time= 0.00000
Epoch: 0003 train_loss= 0.69506 train_acc= 0.54848 val_loss= 0.69216 val_acc= 0.54839 time= 0.01563
Epoch: 0004 train_loss= 0.69291 train_acc= 0.55455 val_loss= 0.69046 val_acc= 0.54839 time= 0.01563
Epoch: 0005 train_loss= 0.69111 train_acc= 0.54848 val_loss= 0.68918 val_acc= 0.54839 time= 0.00000
Epoch: 0006 train_loss= 0.68963 train_acc= 0.55152 val_loss= 0.68823 val_acc= 0.54839 time= 0.01563
Epoch: 0007 train_loss= 0.68928 train_acc= 0.54848 val_loss= 0.68755 val_acc= 0.54839 time= 0.00000
Epoch: 0008 train_loss= 0.68808 train_acc= 0.55758 val_loss= 0.68702 val_acc= 0.54839 time= 0.01563
Epoch: 0009 train_loss= 0.68798 train_acc= 0.54848 val_loss= 0.68654 val_acc= 0.54839 time= 0.01563
Epoch: 0010 train_loss= 0.68685 train_acc= 0.55152 val_loss= 0.68608 val_acc= 0.54839 time= 0.00000
Epoch: 0011 train_loss= 0.68540 train_acc= 0.56667 val_loss= 0.68564 val_acc= 0.54839 time= 0.01563
Epoch: 0012 train_loss= 0.68496 train_acc= 0.55758 val_loss= 0.68519 val_acc= 0.54839 time= 0.01563
Epoch: 0013 train_loss= 0.68512 train_acc= 0.56061 val_loss= 0.68479 val_acc= 0.54839 time= 0.00000
Epoch: 0014 train_loss= 0.68524 train_acc= 0.56364 val_loss= 0.68441 val_acc= 0.54839 time= 0.01563
Epoch: 0015 train_loss= 0.68320 train_acc= 0.57576 val_loss= 0.68403 val_acc= 0.54839 time= 0.01403
Epoch: 0016 train_loss= 0.68404 train_acc= 0.56364 val_loss= 0.68369 val_acc= 0.54839 time= 0.01100
Epoch: 0017 train_loss= 0.68079 train_acc= 0.57576 val_loss= 0.68338 val_acc= 0.54839 time= 0.01000
Epoch: 0018 train_loss= 0.68063 train_acc= 0.56970 val_loss= 0.68309 val_acc= 0.54839 time= 0.00900
Epoch: 0019 train_loss= 0.67918 train_acc= 0.57576 val_loss= 0.68284 val_acc= 0.54839 time= 0.01000
Epoch: 0020 train_loss= 0.67928 train_acc= 0.56970 val_loss= 0.68260 val_acc= 0.54839 time= 0.00900
Epoch: 0021 train_loss= 0.67790 train_acc= 0.56970 val_loss= 0.68239 val_acc= 0.54839 time= 0.01100
Epoch: 0022 train_loss= 0.67681 train_acc= 0.60000 val_loss= 0.68213 val_acc= 0.56452 time= 0.01100
Epoch: 0023 train_loss= 0.67897 train_acc= 0.57576 val_loss= 0.68187 val_acc= 0.56452 time= 0.00900
Epoch: 0024 train_loss= 0.67854 train_acc= 0.57576 val_loss= 0.68170 val_acc= 0.56452 time= 0.00800
Epoch: 0025 train_loss= 0.67734 train_acc= 0.58788 val_loss= 0.68156 val_acc= 0.58065 time= 0.01000
Epoch: 0026 train_loss= 0.67589 train_acc= 0.61212 val_loss= 0.68134 val_acc= 0.58065 time= 0.01000
Epoch: 0027 train_loss= 0.67312 train_acc= 0.59091 val_loss= 0.68106 val_acc= 0.58065 time= 0.00900
Epoch: 0028 train_loss= 0.67291 train_acc= 0.59697 val_loss= 0.68081 val_acc= 0.58065 time= 0.01000
Epoch: 0029 train_loss= 0.67198 train_acc= 0.63333 val_loss= 0.68047 val_acc= 0.58065 time= 0.01100
Epoch: 0030 train_loss= 0.66901 train_acc= 0.60000 val_loss= 0.68006 val_acc= 0.58065 time= 0.01000
Epoch: 0031 train_loss= 0.67041 train_acc= 0.60909 val_loss= 0.67970 val_acc= 0.58065 time= 0.01100
Epoch: 0032 train_loss= 0.66619 train_acc= 0.63333 val_loss= 0.67932 val_acc= 0.58065 time= 0.01100
Epoch: 0033 train_loss= 0.66798 train_acc= 0.63333 val_loss= 0.67889 val_acc= 0.58065 time= 0.01100
Epoch: 0034 train_loss= 0.66524 train_acc= 0.64848 val_loss= 0.67846 val_acc= 0.58065 time= 0.01100
Epoch: 0035 train_loss= 0.66336 train_acc= 0.66970 val_loss= 0.67802 val_acc= 0.58065 time= 0.01000
Epoch: 0036 train_loss= 0.66883 train_acc= 0.63030 val_loss= 0.67767 val_acc= 0.59677 time= 0.01200
Epoch: 0037 train_loss= 0.66601 train_acc= 0.62121 val_loss= 0.67743 val_acc= 0.59677 time= 0.01000
Epoch: 0038 train_loss= 0.66249 train_acc= 0.64545 val_loss= 0.67734 val_acc= 0.59677 time= 0.00900
Epoch: 0039 train_loss= 0.66159 train_acc= 0.61818 val_loss= 0.67755 val_acc= 0.61290 time= 0.01000
Epoch: 0040 train_loss= 0.65714 train_acc= 0.66061 val_loss= 0.67791 val_acc= 0.62903 time= 0.01000
Epoch: 0041 train_loss= 0.66466 train_acc= 0.63636 val_loss= 0.67801 val_acc= 0.64516 time= 0.00900
Epoch: 0042 train_loss= 0.65939 train_acc= 0.72424 val_loss= 0.67718 val_acc= 0.64516 time= 0.00900
Epoch: 0043 train_loss= 0.65483 train_acc= 0.63939 val_loss= 0.67660 val_acc= 0.62903 time= 0.01100
Epoch: 0044 train_loss= 0.65997 train_acc= 0.65152 val_loss= 0.67610 val_acc= 0.61290 time= 0.00900
Epoch: 0045 train_loss= 0.65161 train_acc= 0.68182 val_loss= 0.67560 val_acc= 0.61290 time= 0.00900
Epoch: 0046 train_loss= 0.65267 train_acc= 0.66061 val_loss= 0.67539 val_acc= 0.61290 time= 0.01000
Epoch: 0047 train_loss= 0.65549 train_acc= 0.66364 val_loss= 0.67521 val_acc= 0.61290 time= 0.01000
Epoch: 0048 train_loss= 0.65275 train_acc= 0.71212 val_loss= 0.67495 val_acc= 0.62903 time= 0.00900
Epoch: 0049 train_loss= 0.65147 train_acc= 0.63333 val_loss= 0.67495 val_acc= 0.64516 time= 0.01100
Epoch: 0050 train_loss= 0.65067 train_acc= 0.73939 val_loss= 0.67471 val_acc= 0.64516 time= 0.00800
Epoch: 0051 train_loss= 0.65062 train_acc= 0.69394 val_loss= 0.67428 val_acc= 0.64516 time= 0.01000
Epoch: 0052 train_loss= 0.64610 train_acc= 0.70000 val_loss= 0.67372 val_acc= 0.62903 time= 0.00900
Epoch: 0053 train_loss= 0.65087 train_acc= 0.66970 val_loss= 0.67332 val_acc= 0.62903 time= 0.01000
Epoch: 0054 train_loss= 0.64761 train_acc= 0.70909 val_loss= 0.67336 val_acc= 0.61290 time= 0.01000
Epoch: 0055 train_loss= 0.65124 train_acc= 0.63636 val_loss= 0.67286 val_acc= 0.61290 time= 0.00900
Epoch: 0056 train_loss= 0.65562 train_acc= 0.63939 val_loss= 0.67223 val_acc= 0.62903 time= 0.00900
Epoch: 0057 train_loss= 0.63784 train_acc= 0.67576 val_loss= 0.67230 val_acc= 0.66129 time= 0.01000
Epoch: 0058 train_loss= 0.64929 train_acc= 0.67879 val_loss= 0.67363 val_acc= 0.66129 time= 0.00900
Epoch: 0059 train_loss= 0.64333 train_acc= 0.68485 val_loss= 0.67526 val_acc= 0.67742 time= 0.00900
Early stopping...
Optimization Finished!
Test set results: cost= 0.65160 accuracy= 0.69355 time= 0.00300 
