Epoch: 0001 train_loss= 0.70078 train_acc= 0.49455 val_loss= 0.69773 val_acc= 0.52459 time= 0.42190
Epoch: 0002 train_loss= 0.69718 train_acc= 0.53273 val_loss= 0.69551 val_acc= 0.52459 time= 0.01563
Epoch: 0003 train_loss= 0.69490 train_acc= 0.54727 val_loss= 0.69395 val_acc= 0.52459 time= 0.01562
Epoch: 0004 train_loss= 0.69316 train_acc= 0.53818 val_loss= 0.69284 val_acc= 0.52459 time= 0.00000
Epoch: 0005 train_loss= 0.69158 train_acc= 0.54000 val_loss= 0.69209 val_acc= 0.52459 time= 0.01563
Epoch: 0006 train_loss= 0.69007 train_acc= 0.55455 val_loss= 0.69158 val_acc= 0.52459 time= 0.00000
Epoch: 0007 train_loss= 0.68860 train_acc= 0.56000 val_loss= 0.69127 val_acc= 0.54098 time= 0.01562
Epoch: 0008 train_loss= 0.68848 train_acc= 0.56727 val_loss= 0.69108 val_acc= 0.54098 time= 0.00000
Epoch: 0009 train_loss= 0.68643 train_acc= 0.58545 val_loss= 0.69095 val_acc= 0.54098 time= 0.01563
Epoch: 0010 train_loss= 0.68628 train_acc= 0.57273 val_loss= 0.69081 val_acc= 0.54098 time= 0.00000
Epoch: 0011 train_loss= 0.68563 train_acc= 0.58727 val_loss= 0.69068 val_acc= 0.54098 time= 0.01563
Epoch: 0012 train_loss= 0.68612 train_acc= 0.58909 val_loss= 0.69058 val_acc= 0.54098 time= 0.00000
Epoch: 0013 train_loss= 0.68304 train_acc= 0.58545 val_loss= 0.69039 val_acc= 0.55738 time= 0.01563
Epoch: 0014 train_loss= 0.68117 train_acc= 0.60182 val_loss= 0.69018 val_acc= 0.54098 time= 0.00000
Epoch: 0015 train_loss= 0.68161 train_acc= 0.62182 val_loss= 0.68996 val_acc= 0.55738 time= 0.01563
Epoch: 0016 train_loss= 0.68086 train_acc= 0.62182 val_loss= 0.68968 val_acc= 0.57377 time= 0.01563
Epoch: 0017 train_loss= 0.68101 train_acc= 0.63818 val_loss= 0.68937 val_acc= 0.57377 time= 0.00000
Epoch: 0018 train_loss= 0.67942 train_acc= 0.66909 val_loss= 0.68913 val_acc= 0.57377 time= 0.01563
Epoch: 0019 train_loss= 0.67802 train_acc= 0.66545 val_loss= 0.68893 val_acc= 0.57377 time= 0.00000
Epoch: 0020 train_loss= 0.67775 train_acc= 0.61636 val_loss= 0.68870 val_acc= 0.55738 time= 0.01563
Epoch: 0021 train_loss= 0.67537 train_acc= 0.65818 val_loss= 0.68848 val_acc= 0.55738 time= 0.00000
Epoch: 0022 train_loss= 0.67668 train_acc= 0.68000 val_loss= 0.68833 val_acc= 0.55738 time= 0.01563
Epoch: 0023 train_loss= 0.67452 train_acc= 0.62000 val_loss= 0.68807 val_acc= 0.55738 time= 0.01563
Epoch: 0024 train_loss= 0.67366 train_acc= 0.68364 val_loss= 0.68777 val_acc= 0.57377 time= 0.00000
Epoch: 0025 train_loss= 0.67068 train_acc= 0.64182 val_loss= 0.68745 val_acc= 0.60656 time= 0.01563
Epoch: 0026 train_loss= 0.67122 train_acc= 0.68545 val_loss= 0.68714 val_acc= 0.60656 time= 0.00000
Epoch: 0027 train_loss= 0.66782 train_acc= 0.65636 val_loss= 0.68686 val_acc= 0.57377 time= 0.01563
Epoch: 0028 train_loss= 0.66874 train_acc= 0.69455 val_loss= 0.68658 val_acc= 0.60656 time= 0.00000
Epoch: 0029 train_loss= 0.66431 train_acc= 0.67455 val_loss= 0.68625 val_acc= 0.59016 time= 0.01563
Epoch: 0030 train_loss= 0.66958 train_acc= 0.68000 val_loss= 0.68598 val_acc= 0.60656 time= 0.01563
Epoch: 0031 train_loss= 0.66552 train_acc= 0.69636 val_loss= 0.68569 val_acc= 0.60656 time= 0.00000
Epoch: 0032 train_loss= 0.66229 train_acc= 0.69636 val_loss= 0.68557 val_acc= 0.62295 time= 0.01563
Epoch: 0033 train_loss= 0.66484 train_acc= 0.69818 val_loss= 0.68546 val_acc= 0.60656 time= 0.00000
Epoch: 0034 train_loss= 0.66173 train_acc= 0.68909 val_loss= 0.68522 val_acc= 0.59016 time= 0.01563
Epoch: 0035 train_loss= 0.65885 train_acc= 0.68182 val_loss= 0.68466 val_acc= 0.62295 time= 0.00000
Epoch: 0036 train_loss= 0.65911 train_acc= 0.69818 val_loss= 0.68410 val_acc= 0.60656 time= 0.01563
Epoch: 0037 train_loss= 0.65815 train_acc= 0.69818 val_loss= 0.68357 val_acc= 0.59016 time= 0.00000
Epoch: 0038 train_loss= 0.65904 train_acc= 0.69091 val_loss= 0.68312 val_acc= 0.59016 time= 0.01563
Epoch: 0039 train_loss= 0.65895 train_acc= 0.67636 val_loss= 0.68289 val_acc= 0.60656 time= 0.00000
Epoch: 0040 train_loss= 0.65747 train_acc= 0.70000 val_loss= 0.68314 val_acc= 0.60656 time= 0.01562
Epoch: 0041 train_loss= 0.65942 train_acc= 0.68000 val_loss= 0.68356 val_acc= 0.59016 time= 0.01563
Epoch: 0042 train_loss= 0.65573 train_acc= 0.70545 val_loss= 0.68380 val_acc= 0.57377 time= 0.00000
Epoch: 0043 train_loss= 0.65187 train_acc= 0.69636 val_loss= 0.68339 val_acc= 0.59016 time= 0.01563
Epoch: 0044 train_loss= 0.65732 train_acc= 0.68000 val_loss= 0.68199 val_acc= 0.60656 time= 0.00000
Epoch: 0045 train_loss= 0.65091 train_acc= 0.68545 val_loss= 0.68067 val_acc= 0.59016 time= 0.01563
Epoch: 0046 train_loss= 0.65245 train_acc= 0.70727 val_loss= 0.68004 val_acc= 0.63934 time= 0.00000
Epoch: 0047 train_loss= 0.65038 train_acc= 0.69273 val_loss= 0.67972 val_acc= 0.60656 time= 0.01563
Epoch: 0048 train_loss= 0.64710 train_acc= 0.67818 val_loss= 0.67950 val_acc= 0.62295 time= 0.00000
Epoch: 0049 train_loss= 0.64428 train_acc= 0.70364 val_loss= 0.67921 val_acc= 0.59016 time= 0.00000
Epoch: 0050 train_loss= 0.64223 train_acc= 0.69273 val_loss= 0.67872 val_acc= 0.60656 time= 0.01562
Epoch: 0051 train_loss= 0.64294 train_acc= 0.68364 val_loss= 0.67829 val_acc= 0.62295 time= 0.00000
Epoch: 0052 train_loss= 0.64600 train_acc= 0.67091 val_loss= 0.67796 val_acc= 0.63934 time= 0.01563
Epoch: 0053 train_loss= 0.64133 train_acc= 0.71091 val_loss= 0.67801 val_acc= 0.62295 time= 0.00000
Epoch: 0054 train_loss= 0.64553 train_acc= 0.71455 val_loss= 0.67880 val_acc= 0.60656 time= 0.01563
Epoch: 0055 train_loss= 0.64234 train_acc= 0.68364 val_loss= 0.67826 val_acc= 0.62295 time= 0.00000
Epoch: 0056 train_loss= 0.64027 train_acc= 0.73455 val_loss= 0.67813 val_acc= 0.62295 time= 0.01563
Epoch: 0057 train_loss= 0.64533 train_acc= 0.68545 val_loss= 0.67691 val_acc= 0.63934 time= 0.00000
Epoch: 0058 train_loss= 0.63900 train_acc= 0.70727 val_loss= 0.67610 val_acc= 0.57377 time= 0.01563
Epoch: 0059 train_loss= 0.64248 train_acc= 0.71818 val_loss= 0.67627 val_acc= 0.57377 time= 0.01563
Epoch: 0060 train_loss= 0.63230 train_acc= 0.72364 val_loss= 0.67617 val_acc= 0.59016 time= 0.00000
Epoch: 0061 train_loss= 0.63733 train_acc= 0.70545 val_loss= 0.67540 val_acc= 0.55738 time= 0.01563
Epoch: 0062 train_loss= 0.64610 train_acc= 0.68364 val_loss= 0.67645 val_acc= 0.67213 time= 0.00000
Epoch: 0063 train_loss= 0.63005 train_acc= 0.72364 val_loss= 0.67922 val_acc= 0.60656 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.66706 accuracy= 0.62295 time= 0.00000 
