Epoch: 0001 train_loss= 0.70104 train_acc= 0.53273 val_loss= 0.69708 val_acc= 0.57377 time= 0.44484
Epoch: 0002 train_loss= 0.69762 train_acc= 0.56182 val_loss= 0.69409 val_acc= 0.57377 time= 0.00000
Epoch: 0003 train_loss= 0.69514 train_acc= 0.55818 val_loss= 0.69168 val_acc= 0.57377 time= 0.01563
Epoch: 0004 train_loss= 0.69301 train_acc= 0.55636 val_loss= 0.68978 val_acc= 0.55738 time= 0.01563
Epoch: 0005 train_loss= 0.69151 train_acc= 0.55636 val_loss= 0.68833 val_acc= 0.55738 time= 0.00000
Epoch: 0006 train_loss= 0.69077 train_acc= 0.55636 val_loss= 0.68726 val_acc= 0.57377 time= 0.01563
Epoch: 0007 train_loss= 0.68999 train_acc= 0.55455 val_loss= 0.68643 val_acc= 0.57377 time= 0.00000
Epoch: 0008 train_loss= 0.68933 train_acc= 0.56000 val_loss= 0.68575 val_acc= 0.57377 time= 0.01563
Epoch: 0009 train_loss= 0.68771 train_acc= 0.55636 val_loss= 0.68512 val_acc= 0.57377 time= 0.01563
Epoch: 0010 train_loss= 0.68831 train_acc= 0.56727 val_loss= 0.68440 val_acc= 0.57377 time= 0.00000
Epoch: 0011 train_loss= 0.68718 train_acc= 0.56909 val_loss= 0.68365 val_acc= 0.57377 time= 0.01563
Epoch: 0012 train_loss= 0.68727 train_acc= 0.56545 val_loss= 0.68297 val_acc= 0.57377 time= 0.00000
Epoch: 0013 train_loss= 0.68640 train_acc= 0.57273 val_loss= 0.68237 val_acc= 0.57377 time= 0.02440
Epoch: 0014 train_loss= 0.68680 train_acc= 0.57273 val_loss= 0.68182 val_acc= 0.59016 time= 0.01100
Epoch: 0015 train_loss= 0.68472 train_acc= 0.57455 val_loss= 0.68133 val_acc= 0.60656 time= 0.01100
Epoch: 0016 train_loss= 0.68453 train_acc= 0.59455 val_loss= 0.68081 val_acc= 0.60656 time= 0.00312
Epoch: 0017 train_loss= 0.68323 train_acc= 0.58727 val_loss= 0.68028 val_acc= 0.60656 time= 0.00000
Epoch: 0018 train_loss= 0.68198 train_acc= 0.59636 val_loss= 0.67975 val_acc= 0.60656 time= 0.01563
Epoch: 0019 train_loss= 0.68078 train_acc= 0.60909 val_loss= 0.67915 val_acc= 0.60656 time= 0.01563
Epoch: 0020 train_loss= 0.68059 train_acc= 0.58545 val_loss= 0.67848 val_acc= 0.62295 time= 0.01563
Epoch: 0021 train_loss= 0.67981 train_acc= 0.58364 val_loss= 0.67789 val_acc= 0.62295 time= 0.00000
Epoch: 0022 train_loss= 0.67925 train_acc= 0.59636 val_loss= 0.67738 val_acc= 0.62295 time= 0.01563
Epoch: 0023 train_loss= 0.67785 train_acc= 0.62364 val_loss= 0.67671 val_acc= 0.62295 time= 0.01562
Epoch: 0024 train_loss= 0.67750 train_acc= 0.62000 val_loss= 0.67604 val_acc= 0.62295 time= 0.01363
Epoch: 0025 train_loss= 0.67911 train_acc= 0.60000 val_loss= 0.67548 val_acc= 0.63934 time= 0.01200
Epoch: 0026 train_loss= 0.67650 train_acc= 0.60364 val_loss= 0.67522 val_acc= 0.63934 time= 0.01200
Epoch: 0027 train_loss= 0.67764 train_acc= 0.63636 val_loss= 0.67475 val_acc= 0.63934 time= 0.01100
Epoch: 0028 train_loss= 0.67539 train_acc= 0.63091 val_loss= 0.67435 val_acc= 0.65574 time= 0.00207
Epoch: 0029 train_loss= 0.67344 train_acc= 0.63636 val_loss= 0.67371 val_acc= 0.65574 time= 0.01562
Epoch: 0030 train_loss= 0.67454 train_acc= 0.64000 val_loss= 0.67296 val_acc= 0.65574 time= 0.00000
Epoch: 0031 train_loss= 0.67141 train_acc= 0.63818 val_loss= 0.67243 val_acc= 0.65574 time= 0.02017
Epoch: 0032 train_loss= 0.67279 train_acc= 0.62727 val_loss= 0.67195 val_acc= 0.65574 time= 0.00101
Epoch: 0033 train_loss= 0.66960 train_acc= 0.65455 val_loss= 0.67120 val_acc= 0.65574 time= 0.01050
Epoch: 0034 train_loss= 0.67332 train_acc= 0.62545 val_loss= 0.67053 val_acc= 0.65574 time= 0.01563
Epoch: 0035 train_loss= 0.66911 train_acc= 0.61636 val_loss= 0.67035 val_acc= 0.63934 time= 0.00000
Epoch: 0036 train_loss= 0.66979 train_acc= 0.62000 val_loss= 0.67047 val_acc= 0.63934 time= 0.01563
Epoch: 0037 train_loss= 0.66860 train_acc= 0.63636 val_loss= 0.67081 val_acc= 0.62295 time= 0.00000
Epoch: 0038 train_loss= 0.66557 train_acc= 0.65091 val_loss= 0.67063 val_acc= 0.62295 time= 0.01563
Epoch: 0039 train_loss= 0.66422 train_acc= 0.64545 val_loss= 0.67071 val_acc= 0.63934 time= 0.01563
Epoch: 0040 train_loss= 0.66645 train_acc= 0.67455 val_loss= 0.67045 val_acc= 0.63934 time= 0.00000
Epoch: 0041 train_loss= 0.66576 train_acc= 0.67091 val_loss= 0.66954 val_acc= 0.63934 time= 0.01563
Epoch: 0042 train_loss= 0.66229 train_acc= 0.65818 val_loss= 0.66846 val_acc= 0.62295 time= 0.00000
Epoch: 0043 train_loss= 0.66529 train_acc= 0.65636 val_loss= 0.66822 val_acc= 0.60656 time= 0.01563
Epoch: 0044 train_loss= 0.66215 train_acc= 0.66909 val_loss= 0.66864 val_acc= 0.63934 time= 0.01562
Epoch: 0045 train_loss= 0.65825 train_acc= 0.68545 val_loss= 0.66896 val_acc= 0.65574 time= 0.00000
Epoch: 0046 train_loss= 0.65738 train_acc= 0.68909 val_loss= 0.66848 val_acc= 0.65574 time= 0.01563
Epoch: 0047 train_loss= 0.66184 train_acc= 0.63636 val_loss= 0.66916 val_acc= 0.63934 time= 0.00000
Epoch: 0048 train_loss= 0.65593 train_acc= 0.68545 val_loss= 0.66951 val_acc= 0.65574 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.66228 accuracy= 0.75410 time= 0.00000 
