Epoch: 0001 train_loss= 0.70118 train_acc= 0.47273 val_loss= 0.69867 val_acc= 0.49180 time= 0.80844
Epoch: 0002 train_loss= 0.69801 train_acc= 0.56104 val_loss= 0.69679 val_acc= 0.49180 time= 0.01300
Epoch: 0003 train_loss= 0.69559 train_acc= 0.56883 val_loss= 0.69551 val_acc= 0.50820 time= 0.01300
Epoch: 0004 train_loss= 0.69386 train_acc= 0.56883 val_loss= 0.69466 val_acc= 0.50820 time= 0.01100
Epoch: 0005 train_loss= 0.69295 train_acc= 0.57922 val_loss= 0.69417 val_acc= 0.50820 time= 0.01200
Epoch: 0006 train_loss= 0.69176 train_acc= 0.58182 val_loss= 0.69388 val_acc= 0.50820 time= 0.01200
Epoch: 0007 train_loss= 0.69103 train_acc= 0.59351 val_loss= 0.69371 val_acc= 0.54098 time= 0.01308
Epoch: 0008 train_loss= 0.69050 train_acc= 0.60779 val_loss= 0.69365 val_acc= 0.55738 time= 0.01003
Epoch: 0009 train_loss= 0.69000 train_acc= 0.58571 val_loss= 0.69353 val_acc= 0.55738 time= 0.01147
Epoch: 0010 train_loss= 0.68943 train_acc= 0.61169 val_loss= 0.69348 val_acc= 0.55738 time= 0.01100
Epoch: 0011 train_loss= 0.69000 train_acc= 0.60130 val_loss= 0.69330 val_acc= 0.55738 time= 0.01200
Epoch: 0012 train_loss= 0.68785 train_acc= 0.61169 val_loss= 0.69314 val_acc= 0.55738 time= 0.01100
Epoch: 0013 train_loss= 0.68842 train_acc= 0.60519 val_loss= 0.69309 val_acc= 0.55738 time= 0.01100
Epoch: 0014 train_loss= 0.68737 train_acc= 0.61818 val_loss= 0.69299 val_acc= 0.55738 time= 0.01000
Epoch: 0015 train_loss= 0.68632 train_acc= 0.61558 val_loss= 0.69276 val_acc= 0.55738 time= 0.01100
Epoch: 0016 train_loss= 0.68595 train_acc= 0.61169 val_loss= 0.69230 val_acc= 0.55738 time= 0.01100
Epoch: 0017 train_loss= 0.68593 train_acc= 0.62857 val_loss= 0.69182 val_acc= 0.55738 time= 0.01200
Epoch: 0018 train_loss= 0.68432 train_acc= 0.63766 val_loss= 0.69138 val_acc= 0.55738 time= 0.01000
Epoch: 0019 train_loss= 0.68394 train_acc= 0.62468 val_loss= 0.69097 val_acc= 0.55738 time= 0.01200
Epoch: 0020 train_loss= 0.68351 train_acc= 0.63117 val_loss= 0.69046 val_acc= 0.55738 time= 0.01000
Epoch: 0021 train_loss= 0.68288 train_acc= 0.65065 val_loss= 0.69019 val_acc= 0.55738 time= 0.01000
Epoch: 0022 train_loss= 0.68184 train_acc= 0.61688 val_loss= 0.68954 val_acc= 0.59016 time= 0.00917
Epoch: 0023 train_loss= 0.68084 train_acc= 0.65325 val_loss= 0.68897 val_acc= 0.59016 time= 0.00513
Epoch: 0024 train_loss= 0.67922 train_acc= 0.65325 val_loss= 0.68861 val_acc= 0.59016 time= 0.00000
Epoch: 0025 train_loss= 0.68129 train_acc= 0.66364 val_loss= 0.68839 val_acc= 0.59016 time= 0.01562
Epoch: 0026 train_loss= 0.67919 train_acc= 0.64675 val_loss= 0.68790 val_acc= 0.59016 time= 0.01563
Epoch: 0027 train_loss= 0.67770 train_acc= 0.62468 val_loss= 0.68690 val_acc= 0.62295 time= 0.00000
Epoch: 0028 train_loss= 0.67742 train_acc= 0.65455 val_loss= 0.68632 val_acc= 0.62295 time= 0.02019
Epoch: 0029 train_loss= 0.67685 train_acc= 0.68182 val_loss= 0.68627 val_acc= 0.62295 time= 0.00900
Epoch: 0030 train_loss= 0.67641 train_acc= 0.65195 val_loss= 0.68688 val_acc= 0.57377 time= 0.00900
Epoch: 0031 train_loss= 0.67604 train_acc= 0.65065 val_loss= 0.68701 val_acc= 0.55738 time= 0.00000
Epoch: 0032 train_loss= 0.67481 train_acc= 0.62208 val_loss= 0.68602 val_acc= 0.59016 time= 0.01915
Epoch: 0033 train_loss= 0.67263 train_acc= 0.62078 val_loss= 0.68423 val_acc= 0.60656 time= 0.00900
Epoch: 0034 train_loss= 0.67199 train_acc= 0.69610 val_loss= 0.68308 val_acc= 0.60656 time= 0.00900
Epoch: 0035 train_loss= 0.67564 train_acc= 0.65455 val_loss= 0.68157 val_acc= 0.67213 time= 0.00200
Epoch: 0036 train_loss= 0.67165 train_acc= 0.68052 val_loss= 0.68056 val_acc= 0.75410 time= 0.01567
Epoch: 0037 train_loss= 0.67147 train_acc= 0.69610 val_loss= 0.68051 val_acc= 0.67213 time= 0.01041
Epoch: 0038 train_loss= 0.66761 train_acc= 0.68052 val_loss= 0.68083 val_acc= 0.62295 time= 0.00900
Epoch: 0039 train_loss= 0.67128 train_acc= 0.65584 val_loss= 0.68068 val_acc= 0.60656 time= 0.00500
Epoch: 0040 train_loss= 0.67033 train_acc= 0.63636 val_loss= 0.67937 val_acc= 0.67213 time= 0.00000
Epoch: 0041 train_loss= 0.67325 train_acc= 0.65455 val_loss= 0.67834 val_acc= 0.70492 time= 0.02206
Epoch: 0042 train_loss= 0.66596 train_acc= 0.68961 val_loss= 0.67739 val_acc= 0.73770 time= 0.00800
Epoch: 0043 train_loss= 0.66802 train_acc= 0.70130 val_loss= 0.67719 val_acc= 0.70492 time= 0.01000
Epoch: 0044 train_loss= 0.66654 train_acc= 0.66883 val_loss= 0.67673 val_acc= 0.70492 time= 0.00000
Epoch: 0045 train_loss= 0.66517 train_acc= 0.65065 val_loss= 0.67639 val_acc= 0.68852 time= 0.01916
Epoch: 0046 train_loss= 0.66432 train_acc= 0.66234 val_loss= 0.67519 val_acc= 0.73770 time= 0.00900
Epoch: 0047 train_loss= 0.66519 train_acc= 0.67013 val_loss= 0.67467 val_acc= 0.73770 time= 0.01000
Epoch: 0048 train_loss= 0.66463 train_acc= 0.65974 val_loss= 0.67333 val_acc= 0.68852 time= 0.00200
Epoch: 0049 train_loss= 0.66201 train_acc= 0.67532 val_loss= 0.67238 val_acc= 0.68852 time= 0.01706
Epoch: 0050 train_loss= 0.65891 train_acc= 0.68571 val_loss= 0.67306 val_acc= 0.73770 time= 0.01000
Epoch: 0051 train_loss= 0.65427 train_acc= 0.70260 val_loss= 0.67471 val_acc= 0.60656 time= 0.01000
Epoch: 0052 train_loss= 0.66158 train_acc= 0.64545 val_loss= 0.67400 val_acc= 0.60656 time= 0.01200
Epoch: 0053 train_loss= 0.66258 train_acc= 0.64675 val_loss= 0.67252 val_acc= 0.67213 time= 0.01000
Epoch: 0054 train_loss= 0.66149 train_acc= 0.65714 val_loss= 0.67093 val_acc= 0.70492 time= 0.01000
Epoch: 0055 train_loss= 0.65803 train_acc= 0.66883 val_loss= 0.66914 val_acc= 0.70492 time= 0.01000
Epoch: 0056 train_loss= 0.65701 train_acc= 0.70779 val_loss= 0.66779 val_acc= 0.67213 time= 0.01111
Epoch: 0057 train_loss= 0.65592 train_acc= 0.67922 val_loss= 0.66623 val_acc= 0.70492 time= 0.01210
Epoch: 0058 train_loss= 0.65497 train_acc= 0.68442 val_loss= 0.66683 val_acc= 0.67213 time= 0.01125
Epoch: 0059 train_loss= 0.65544 train_acc= 0.67013 val_loss= 0.67025 val_acc= 0.62295 time= 0.01000
Epoch: 0060 train_loss= 0.65704 train_acc= 0.65325 val_loss= 0.67100 val_acc= 0.60656 time= 0.01100
Early stopping...
Optimization Finished!
Test set results: cost= 0.66108 accuracy= 0.68033 time= 0.00400 
