Epoch: 0001 train_loss= 0.70106 train_acc= 0.50182 val_loss= 0.69843 val_acc= 0.50820 time= 0.44908
Epoch: 0002 train_loss= 0.69781 train_acc= 0.52909 val_loss= 0.69592 val_acc= 0.52459 time= 0.01000
Epoch: 0003 train_loss= 0.69461 train_acc= 0.53273 val_loss= 0.69401 val_acc= 0.52459 time= 0.01000
Epoch: 0004 train_loss= 0.69274 train_acc= 0.55091 val_loss= 0.69262 val_acc= 0.55738 time= 0.01100
Epoch: 0005 train_loss= 0.69113 train_acc= 0.55273 val_loss= 0.69175 val_acc= 0.55738 time= 0.01200
Epoch: 0006 train_loss= 0.68991 train_acc= 0.57091 val_loss= 0.69127 val_acc= 0.55738 time= 0.00900
Epoch: 0007 train_loss= 0.68821 train_acc= 0.57455 val_loss= 0.69094 val_acc= 0.55738 time= 0.01100
Epoch: 0008 train_loss= 0.68818 train_acc= 0.60909 val_loss= 0.69066 val_acc= 0.55738 time= 0.01000
Epoch: 0009 train_loss= 0.68711 train_acc= 0.59091 val_loss= 0.69042 val_acc= 0.55738 time= 0.01200
Epoch: 0010 train_loss= 0.68559 train_acc= 0.60545 val_loss= 0.69012 val_acc= 0.57377 time= 0.01100
Epoch: 0011 train_loss= 0.68565 train_acc= 0.59636 val_loss= 0.68975 val_acc= 0.55738 time= 0.01000
Epoch: 0012 train_loss= 0.68394 train_acc= 0.62727 val_loss= 0.68927 val_acc= 0.54098 time= 0.00900
Epoch: 0013 train_loss= 0.68549 train_acc= 0.63455 val_loss= 0.68880 val_acc= 0.55738 time= 0.01200
Epoch: 0014 train_loss= 0.68449 train_acc= 0.64364 val_loss= 0.68834 val_acc= 0.55738 time= 0.00900
Epoch: 0015 train_loss= 0.68214 train_acc= 0.64727 val_loss= 0.68782 val_acc= 0.55738 time= 0.01100
Epoch: 0016 train_loss= 0.67985 train_acc= 0.64182 val_loss= 0.68730 val_acc= 0.57377 time= 0.01200
Epoch: 0017 train_loss= 0.68087 train_acc= 0.65455 val_loss= 0.68678 val_acc= 0.57377 time= 0.01100
Epoch: 0018 train_loss= 0.67847 train_acc= 0.64727 val_loss= 0.68627 val_acc= 0.57377 time= 0.01000
Epoch: 0019 train_loss= 0.67863 train_acc= 0.63636 val_loss= 0.68580 val_acc= 0.59016 time= 0.01100
Epoch: 0020 train_loss= 0.67905 train_acc= 0.69273 val_loss= 0.68534 val_acc= 0.59016 time= 0.00800
Epoch: 0021 train_loss= 0.67488 train_acc= 0.67455 val_loss= 0.68489 val_acc= 0.60656 time= 0.01000
Epoch: 0022 train_loss= 0.67529 train_acc= 0.67636 val_loss= 0.68444 val_acc= 0.60656 time= 0.01000
Epoch: 0023 train_loss= 0.67512 train_acc= 0.65091 val_loss= 0.68383 val_acc= 0.60656 time= 0.01100
Epoch: 0024 train_loss= 0.67116 train_acc= 0.66909 val_loss= 0.68301 val_acc= 0.63934 time= 0.01200
Epoch: 0025 train_loss= 0.66868 train_acc= 0.70909 val_loss= 0.68226 val_acc= 0.63934 time= 0.01300
Epoch: 0026 train_loss= 0.67143 train_acc= 0.69091 val_loss= 0.68146 val_acc= 0.68852 time= 0.01300
Epoch: 0027 train_loss= 0.66811 train_acc= 0.72182 val_loss= 0.68074 val_acc= 0.67213 time= 0.01200
Epoch: 0028 train_loss= 0.67088 train_acc= 0.66364 val_loss= 0.68004 val_acc= 0.68852 time= 0.01300
Epoch: 0029 train_loss= 0.66770 train_acc= 0.70545 val_loss= 0.67946 val_acc= 0.68852 time= 0.01300
Epoch: 0030 train_loss= 0.66565 train_acc= 0.71273 val_loss= 0.67890 val_acc= 0.67213 time= 0.01300
Epoch: 0031 train_loss= 0.66618 train_acc= 0.69273 val_loss= 0.67842 val_acc= 0.67213 time= 0.01100
Epoch: 0032 train_loss= 0.66785 train_acc= 0.69636 val_loss= 0.67811 val_acc= 0.67213 time= 0.00900
Epoch: 0033 train_loss= 0.66559 train_acc= 0.66182 val_loss= 0.67767 val_acc= 0.65574 time= 0.01100
Epoch: 0034 train_loss= 0.66316 train_acc= 0.68545 val_loss= 0.67726 val_acc= 0.65574 time= 0.00900
Epoch: 0035 train_loss= 0.66486 train_acc= 0.69636 val_loss= 0.67705 val_acc= 0.68852 time= 0.01000
Epoch: 0036 train_loss= 0.66040 train_acc= 0.73091 val_loss= 0.67727 val_acc= 0.60656 time= 0.01000
Epoch: 0037 train_loss= 0.66002 train_acc= 0.67455 val_loss= 0.67683 val_acc= 0.60656 time= 0.01000
Epoch: 0038 train_loss= 0.66169 train_acc= 0.69273 val_loss= 0.67606 val_acc= 0.63934 time= 0.01000
Epoch: 0039 train_loss= 0.66029 train_acc= 0.68364 val_loss= 0.67496 val_acc= 0.67213 time= 0.01000
Epoch: 0040 train_loss= 0.65544 train_acc= 0.68545 val_loss= 0.67412 val_acc= 0.67213 time= 0.01000
Epoch: 0041 train_loss= 0.66035 train_acc= 0.70909 val_loss= 0.67333 val_acc= 0.67213 time= 0.01100
Epoch: 0042 train_loss= 0.65500 train_acc= 0.69455 val_loss= 0.67240 val_acc= 0.65574 time= 0.01000
Epoch: 0043 train_loss= 0.65657 train_acc= 0.69091 val_loss= 0.67179 val_acc= 0.62295 time= 0.00900
Epoch: 0044 train_loss= 0.65493 train_acc= 0.70364 val_loss= 0.67128 val_acc= 0.59016 time= 0.00900
Epoch: 0045 train_loss= 0.64988 train_acc= 0.71818 val_loss= 0.67088 val_acc= 0.63934 time= 0.01100
Epoch: 0046 train_loss= 0.65311 train_acc= 0.69455 val_loss= 0.67064 val_acc= 0.63934 time= 0.00900
Epoch: 0047 train_loss= 0.65290 train_acc= 0.68000 val_loss= 0.67051 val_acc= 0.60656 time= 0.01100
Epoch: 0048 train_loss= 0.64193 train_acc= 0.73636 val_loss= 0.67032 val_acc= 0.63934 time= 0.01171
Epoch: 0049 train_loss= 0.64562 train_acc= 0.70727 val_loss= 0.67028 val_acc= 0.65574 time= 0.00929
Epoch: 0050 train_loss= 0.64223 train_acc= 0.70364 val_loss= 0.67027 val_acc= 0.63934 time= 0.01000
Epoch: 0051 train_loss= 0.63865 train_acc= 0.70545 val_loss= 0.67058 val_acc= 0.70492 time= 0.01000
Epoch: 0052 train_loss= 0.63978 train_acc= 0.70182 val_loss= 0.67187 val_acc= 0.65574 time= 0.00900
Early stopping...
Optimization Finished!
Test set results: cost= 0.64441 accuracy= 0.79508 time= 0.00500 
