Epoch: 0001 train_loss= 0.70095 train_acc= 0.51818 val_loss= 0.69790 val_acc= 0.52459 time= 0.48981
Epoch: 0002 train_loss= 0.69764 train_acc= 0.53636 val_loss= 0.69536 val_acc= 0.52459 time= 0.01563
Epoch: 0003 train_loss= 0.69447 train_acc= 0.53818 val_loss= 0.69354 val_acc= 0.52459 time= 0.01563
Epoch: 0004 train_loss= 0.69208 train_acc= 0.53818 val_loss= 0.69229 val_acc= 0.52459 time= 0.00000
Epoch: 0005 train_loss= 0.69006 train_acc= 0.53818 val_loss= 0.69145 val_acc= 0.54098 time= 0.01563
Epoch: 0006 train_loss= 0.68920 train_acc= 0.54182 val_loss= 0.69089 val_acc= 0.54098 time= 0.00000
Epoch: 0007 train_loss= 0.68817 train_acc= 0.54364 val_loss= 0.69052 val_acc= 0.54098 time= 0.01563
Epoch: 0008 train_loss= 0.68783 train_acc= 0.54727 val_loss= 0.69020 val_acc= 0.54098 time= 0.00000
Epoch: 0009 train_loss= 0.68710 train_acc= 0.54364 val_loss= 0.68993 val_acc= 0.54098 time= 0.02052
Epoch: 0010 train_loss= 0.68663 train_acc= 0.55091 val_loss= 0.68968 val_acc= 0.55738 time= 0.00000
Epoch: 0011 train_loss= 0.68493 train_acc= 0.55455 val_loss= 0.68937 val_acc= 0.55738 time= 0.01100
Epoch: 0012 train_loss= 0.68405 train_acc= 0.56000 val_loss= 0.68901 val_acc= 0.55738 time= 0.01563
Epoch: 0013 train_loss= 0.68492 train_acc= 0.55818 val_loss= 0.68860 val_acc= 0.55738 time= 0.00000
Epoch: 0014 train_loss= 0.68421 train_acc= 0.56000 val_loss= 0.68813 val_acc= 0.57377 time= 0.01563
Epoch: 0015 train_loss= 0.68300 train_acc= 0.56545 val_loss= 0.68769 val_acc= 0.57377 time= 0.00000
Epoch: 0016 train_loss= 0.68364 train_acc= 0.56727 val_loss= 0.68725 val_acc= 0.57377 time= 0.01562
Epoch: 0017 train_loss= 0.68157 train_acc= 0.57636 val_loss= 0.68685 val_acc= 0.59016 time= 0.00000
Epoch: 0018 train_loss= 0.68143 train_acc= 0.59091 val_loss= 0.68652 val_acc= 0.59016 time= 0.01563
Epoch: 0019 train_loss= 0.68061 train_acc= 0.58545 val_loss= 0.68620 val_acc= 0.59016 time= 0.00000
Epoch: 0020 train_loss= 0.67917 train_acc= 0.58364 val_loss= 0.68589 val_acc= 0.59016 time= 0.01563
Epoch: 0021 train_loss= 0.67758 train_acc= 0.58364 val_loss= 0.68556 val_acc= 0.60656 time= 0.01563
Epoch: 0022 train_loss= 0.67995 train_acc= 0.59455 val_loss= 0.68523 val_acc= 0.60656 time= 0.00000
Epoch: 0023 train_loss= 0.67844 train_acc= 0.59455 val_loss= 0.68489 val_acc= 0.59016 time= 0.01562
Epoch: 0024 train_loss= 0.67729 train_acc= 0.60727 val_loss= 0.68462 val_acc= 0.59016 time= 0.00000
Epoch: 0025 train_loss= 0.67659 train_acc= 0.58545 val_loss= 0.68432 val_acc= 0.59016 time= 0.01563
Epoch: 0026 train_loss= 0.67563 train_acc= 0.59273 val_loss= 0.68397 val_acc= 0.59016 time= 0.00000
Epoch: 0027 train_loss= 0.67642 train_acc= 0.63636 val_loss= 0.68377 val_acc= 0.59016 time= 0.01562
Epoch: 0028 train_loss= 0.67468 train_acc= 0.58909 val_loss= 0.68337 val_acc= 0.59016 time= 0.00000
Epoch: 0029 train_loss= 0.67189 train_acc= 0.59636 val_loss= 0.68284 val_acc= 0.59016 time= 0.01563
Epoch: 0030 train_loss= 0.67202 train_acc= 0.61455 val_loss= 0.68236 val_acc= 0.59016 time= 0.00000
Epoch: 0031 train_loss= 0.67095 train_acc= 0.62545 val_loss= 0.68189 val_acc= 0.59016 time= 0.01563
Epoch: 0032 train_loss= 0.67045 train_acc= 0.63636 val_loss= 0.68145 val_acc= 0.59016 time= 0.01563
Epoch: 0033 train_loss= 0.67002 train_acc= 0.61091 val_loss= 0.68095 val_acc= 0.59016 time= 0.00000
Epoch: 0034 train_loss= 0.66991 train_acc= 0.64182 val_loss= 0.68051 val_acc= 0.60656 time= 0.01563
Epoch: 0035 train_loss= 0.67081 train_acc= 0.62909 val_loss= 0.68008 val_acc= 0.62295 time= 0.00000
Epoch: 0036 train_loss= 0.66878 train_acc= 0.65818 val_loss= 0.67966 val_acc= 0.62295 time= 0.01562
Epoch: 0037 train_loss= 0.66729 train_acc= 0.64000 val_loss= 0.67922 val_acc= 0.62295 time= 0.00000
Epoch: 0038 train_loss= 0.66582 train_acc= 0.61818 val_loss= 0.67885 val_acc= 0.63934 time= 0.01563
Epoch: 0039 train_loss= 0.66212 train_acc= 0.68727 val_loss= 0.67841 val_acc= 0.67213 time= 0.00000
Epoch: 0040 train_loss= 0.66655 train_acc= 0.69091 val_loss= 0.67789 val_acc= 0.63934 time= 0.01563
Epoch: 0041 train_loss= 0.66174 train_acc= 0.64000 val_loss= 0.67742 val_acc= 0.63934 time= 0.01563
Epoch: 0042 train_loss= 0.66372 train_acc= 0.64364 val_loss= 0.67693 val_acc= 0.63934 time= 0.00000
Epoch: 0043 train_loss= 0.66386 train_acc= 0.69091 val_loss= 0.67649 val_acc= 0.62295 time= 0.01563
Epoch: 0044 train_loss= 0.66045 train_acc= 0.66545 val_loss= 0.67613 val_acc= 0.62295 time= 0.00000
Epoch: 0045 train_loss= 0.66130 train_acc= 0.70545 val_loss= 0.67605 val_acc= 0.60656 time= 0.01563
Epoch: 0046 train_loss= 0.66055 train_acc= 0.65273 val_loss= 0.67549 val_acc= 0.62295 time= 0.00000
Epoch: 0047 train_loss= 0.65920 train_acc= 0.68000 val_loss= 0.67502 val_acc= 0.62295 time= 0.01562
Epoch: 0048 train_loss= 0.65702 train_acc= 0.66364 val_loss= 0.67456 val_acc= 0.62295 time= 0.00000
Epoch: 0049 train_loss= 0.65686 train_acc= 0.67091 val_loss= 0.67402 val_acc= 0.63934 time= 0.01563
Epoch: 0050 train_loss= 0.65882 train_acc= 0.61273 val_loss= 0.67354 val_acc= 0.65574 time= 0.00000
Epoch: 0051 train_loss= 0.65528 train_acc= 0.72364 val_loss= 0.67335 val_acc= 0.70492 time= 0.01563
Epoch: 0052 train_loss= 0.65463 train_acc= 0.67455 val_loss= 0.67351 val_acc= 0.67213 time= 0.01563
Epoch: 0053 train_loss= 0.65573 train_acc= 0.71818 val_loss= 0.67241 val_acc= 0.68852 time= 0.00000
Epoch: 0054 train_loss= 0.65539 train_acc= 0.68909 val_loss= 0.67171 val_acc= 0.63934 time= 0.01563
Epoch: 0055 train_loss= 0.65302 train_acc= 0.69091 val_loss= 0.67170 val_acc= 0.63934 time= 0.00000
Epoch: 0056 train_loss= 0.65909 train_acc= 0.69273 val_loss= 0.67221 val_acc= 0.60656 time= 0.01563
Epoch: 0057 train_loss= 0.65401 train_acc= 0.63636 val_loss= 0.67161 val_acc= 0.62295 time= 0.01138
Epoch: 0058 train_loss= 0.65463 train_acc= 0.66000 val_loss= 0.67060 val_acc= 0.63934 time= 0.01000
Epoch: 0059 train_loss= 0.64955 train_acc= 0.70182 val_loss= 0.66988 val_acc= 0.67213 time= 0.00900
Epoch: 0060 train_loss= 0.64463 train_acc= 0.72727 val_loss= 0.66942 val_acc= 0.63934 time= 0.00900
Epoch: 0061 train_loss= 0.64814 train_acc= 0.71455 val_loss= 0.66922 val_acc= 0.63934 time= 0.00800
Epoch: 0062 train_loss= 0.65360 train_acc= 0.71091 val_loss= 0.66907 val_acc= 0.63934 time= 0.00800
Epoch: 0063 train_loss= 0.64387 train_acc= 0.70909 val_loss= 0.66938 val_acc= 0.67213 time= 0.00900
Epoch: 0064 train_loss= 0.64605 train_acc= 0.70727 val_loss= 0.66978 val_acc= 0.63934 time= 0.00800
Epoch: 0065 train_loss= 0.64972 train_acc= 0.66000 val_loss= 0.66873 val_acc= 0.63934 time= 0.00900
Epoch: 0066 train_loss= 0.64644 train_acc= 0.65636 val_loss= 0.66859 val_acc= 0.70492 time= 0.01000
Epoch: 0067 train_loss= 0.64871 train_acc= 0.69455 val_loss= 0.66916 val_acc= 0.67213 time= 0.01000
Epoch: 0068 train_loss= 0.64691 train_acc= 0.68909 val_loss= 0.66875 val_acc= 0.67213 time= 0.00800
Epoch: 0069 train_loss= 0.64121 train_acc= 0.69091 val_loss= 0.66894 val_acc= 0.67213 time= 0.00900
Epoch: 0070 train_loss= 0.64391 train_acc= 0.69818 val_loss= 0.66861 val_acc= 0.67213 time= 0.00800
Epoch: 0071 train_loss= 0.64730 train_acc= 0.68000 val_loss= 0.66884 val_acc= 0.67213 time= 0.00900
Epoch: 0072 train_loss= 0.64031 train_acc= 0.69818 val_loss= 0.66799 val_acc= 0.70492 time= 0.00800
Epoch: 0073 train_loss= 0.63922 train_acc= 0.71273 val_loss= 0.66820 val_acc= 0.63934 time= 0.00900
Epoch: 0074 train_loss= 0.64064 train_acc= 0.69091 val_loss= 0.66909 val_acc= 0.68852 time= 0.00800
Early stopping...
Optimization Finished!
Test set results: cost= 0.65928 accuracy= 0.65574 time= 0.00400 
