Epoch: 0001 train_loss= 0.70117 train_acc= 0.46182 val_loss= 0.69764 val_acc= 0.62295 time= 0.42191
Epoch: 0002 train_loss= 0.69783 train_acc= 0.55091 val_loss= 0.69477 val_acc= 0.59016 time= 0.01562
Epoch: 0003 train_loss= 0.69555 train_acc= 0.54727 val_loss= 0.69232 val_acc= 0.57377 time= 0.01562
Epoch: 0004 train_loss= 0.69357 train_acc= 0.54545 val_loss= 0.69038 val_acc= 0.57377 time= 0.01563
Epoch: 0005 train_loss= 0.69203 train_acc= 0.54364 val_loss= 0.68887 val_acc= 0.57377 time= 0.00000
Epoch: 0006 train_loss= 0.69102 train_acc= 0.54909 val_loss= 0.68770 val_acc= 0.57377 time= 0.01563
Epoch: 0007 train_loss= 0.69036 train_acc= 0.53636 val_loss= 0.68676 val_acc= 0.57377 time= 0.00000
Epoch: 0008 train_loss= 0.68960 train_acc= 0.54909 val_loss= 0.68587 val_acc= 0.57377 time= 0.01563
Epoch: 0009 train_loss= 0.68943 train_acc= 0.54182 val_loss= 0.68502 val_acc= 0.57377 time= 0.01562
Epoch: 0010 train_loss= 0.68838 train_acc= 0.54182 val_loss= 0.68419 val_acc= 0.57377 time= 0.01563
Epoch: 0011 train_loss= 0.68751 train_acc= 0.55091 val_loss= 0.68344 val_acc= 0.59016 time= 0.00000
Epoch: 0012 train_loss= 0.68721 train_acc= 0.55091 val_loss= 0.68269 val_acc= 0.59016 time= 0.02229
Epoch: 0013 train_loss= 0.68656 train_acc= 0.56182 val_loss= 0.68176 val_acc= 0.59016 time= 0.00000
Epoch: 0014 train_loss= 0.68536 train_acc= 0.55636 val_loss= 0.68090 val_acc= 0.59016 time= 0.01562
Epoch: 0015 train_loss= 0.68633 train_acc= 0.55455 val_loss= 0.68004 val_acc= 0.59016 time= 0.01563
Epoch: 0016 train_loss= 0.68486 train_acc= 0.56000 val_loss= 0.67926 val_acc= 0.60656 time= 0.00000
Epoch: 0017 train_loss= 0.68390 train_acc= 0.56182 val_loss= 0.67861 val_acc= 0.60656 time= 0.01563
Epoch: 0018 train_loss= 0.68315 train_acc= 0.57091 val_loss= 0.67824 val_acc= 0.60656 time= 0.01563
Epoch: 0019 train_loss= 0.68108 train_acc= 0.57091 val_loss= 0.67802 val_acc= 0.62295 time= 0.00000
Epoch: 0020 train_loss= 0.68080 train_acc= 0.57455 val_loss= 0.67775 val_acc= 0.63934 time= 0.01563
Epoch: 0021 train_loss= 0.68079 train_acc= 0.56182 val_loss= 0.67720 val_acc= 0.63934 time= 0.01563
Epoch: 0022 train_loss= 0.68083 train_acc= 0.57091 val_loss= 0.67668 val_acc= 0.63934 time= 0.01563
Epoch: 0023 train_loss= 0.68063 train_acc= 0.59273 val_loss= 0.67581 val_acc= 0.65574 time= 0.00000
Epoch: 0024 train_loss= 0.67829 train_acc= 0.59091 val_loss= 0.67485 val_acc= 0.65574 time= 0.01563
Epoch: 0025 train_loss= 0.67874 train_acc= 0.59273 val_loss= 0.67369 val_acc= 0.65574 time= 0.01563
Epoch: 0026 train_loss= 0.67703 train_acc= 0.59091 val_loss= 0.67261 val_acc= 0.65574 time= 0.00000
Epoch: 0027 train_loss= 0.67522 train_acc= 0.57818 val_loss= 0.67207 val_acc= 0.67213 time= 0.02083
Epoch: 0028 train_loss= 0.67578 train_acc= 0.58727 val_loss= 0.67177 val_acc= 0.68852 time= 0.01050
Epoch: 0029 train_loss= 0.67544 train_acc= 0.61091 val_loss= 0.67176 val_acc= 0.68852 time= 0.00000
Epoch: 0030 train_loss= 0.67322 train_acc= 0.62909 val_loss= 0.67217 val_acc= 0.70492 time= 0.01563
Epoch: 0031 train_loss= 0.67276 train_acc= 0.66364 val_loss= 0.67199 val_acc= 0.72131 time= 0.00000
Epoch: 0032 train_loss= 0.67038 train_acc= 0.62909 val_loss= 0.67167 val_acc= 0.72131 time= 0.01563
Epoch: 0033 train_loss= 0.66862 train_acc= 0.62909 val_loss= 0.67179 val_acc= 0.73770 time= 0.01563
Epoch: 0034 train_loss= 0.67052 train_acc= 0.65636 val_loss= 0.67049 val_acc= 0.72131 time= 0.00000
Epoch: 0035 train_loss= 0.67325 train_acc= 0.68727 val_loss= 0.66798 val_acc= 0.70492 time= 0.01563
Epoch: 0036 train_loss= 0.66908 train_acc= 0.67091 val_loss= 0.66514 val_acc= 0.68852 time= 0.01563
Epoch: 0037 train_loss= 0.66985 train_acc= 0.58727 val_loss= 0.66354 val_acc= 0.68852 time= 0.00000
Epoch: 0038 train_loss= 0.66671 train_acc= 0.61636 val_loss= 0.66298 val_acc= 0.68852 time= 0.01563
Epoch: 0039 train_loss= 0.66582 train_acc= 0.60000 val_loss= 0.66353 val_acc= 0.72131 time= 0.00000
Epoch: 0040 train_loss= 0.66359 train_acc= 0.64727 val_loss= 0.66405 val_acc= 0.72131 time= 0.01563
Epoch: 0041 train_loss= 0.66765 train_acc= 0.64545 val_loss= 0.66437 val_acc= 0.73770 time= 0.01563
Epoch: 0042 train_loss= 0.66660 train_acc= 0.67273 val_loss= 0.66418 val_acc= 0.75410 time= 0.00000
Epoch: 0043 train_loss= 0.66255 train_acc= 0.65273 val_loss= 0.66375 val_acc= 0.77049 time= 0.01563
Epoch: 0044 train_loss= 0.66024 train_acc= 0.64364 val_loss= 0.66347 val_acc= 0.77049 time= 0.00000
Epoch: 0045 train_loss= 0.65877 train_acc= 0.66727 val_loss= 0.66278 val_acc= 0.77049 time= 0.00000
Epoch: 0046 train_loss= 0.66333 train_acc= 0.66909 val_loss= 0.66143 val_acc= 0.77049 time= 0.01563
Epoch: 0047 train_loss= 0.66387 train_acc= 0.66000 val_loss= 0.66042 val_acc= 0.77049 time= 0.01563
Epoch: 0048 train_loss= 0.65350 train_acc= 0.69091 val_loss= 0.65964 val_acc= 0.77049 time= 0.00000
Epoch: 0049 train_loss= 0.66110 train_acc= 0.67091 val_loss= 0.65902 val_acc= 0.77049 time= 0.01563
Epoch: 0050 train_loss= 0.66186 train_acc= 0.67636 val_loss= 0.65704 val_acc= 0.75410 time= 0.01563
Epoch: 0051 train_loss= 0.65616 train_acc= 0.68182 val_loss= 0.65370 val_acc= 0.72131 time= 0.00000
Epoch: 0052 train_loss= 0.65148 train_acc= 0.69818 val_loss= 0.65175 val_acc= 0.73770 time= 0.01563
Epoch: 0053 train_loss= 0.65397 train_acc= 0.63636 val_loss= 0.65104 val_acc= 0.73770 time= 0.01563
Epoch: 0054 train_loss= 0.65564 train_acc= 0.68000 val_loss= 0.65141 val_acc= 0.72131 time= 0.00000
Epoch: 0055 train_loss= 0.65548 train_acc= 0.63455 val_loss= 0.65350 val_acc= 0.77049 time= 0.01563
Epoch: 0056 train_loss= 0.65650 train_acc= 0.67455 val_loss= 0.65623 val_acc= 0.75410 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.65700 accuracy= 0.73770 time= 0.01563 
