Epoch: 0001 train_loss= 0.70107 train_acc= 0.50364 val_loss= 0.69771 val_acc= 0.56452 time= 0.48483
Epoch: 0002 train_loss= 0.69793 train_acc= 0.54909 val_loss= 0.69506 val_acc= 0.58065 time= 0.01563
Epoch: 0003 train_loss= 0.69570 train_acc= 0.55455 val_loss= 0.69295 val_acc= 0.58065 time= 0.00000
Epoch: 0004 train_loss= 0.69387 train_acc= 0.54545 val_loss= 0.69134 val_acc= 0.58065 time= 0.01563
Epoch: 0005 train_loss= 0.69278 train_acc= 0.55091 val_loss= 0.69021 val_acc= 0.58065 time= 0.01563
Epoch: 0006 train_loss= 0.69144 train_acc= 0.55636 val_loss= 0.68951 val_acc= 0.58065 time= 0.00000
Epoch: 0007 train_loss= 0.69065 train_acc= 0.55455 val_loss= 0.68919 val_acc= 0.59677 time= 0.01563
Epoch: 0008 train_loss= 0.68980 train_acc= 0.55818 val_loss= 0.68903 val_acc= 0.59677 time= 0.00000
Epoch: 0009 train_loss= 0.68897 train_acc= 0.57636 val_loss= 0.68890 val_acc= 0.58065 time= 0.01562
Epoch: 0010 train_loss= 0.68892 train_acc= 0.58000 val_loss= 0.68865 val_acc= 0.58065 time= 0.01563
Epoch: 0011 train_loss= 0.68782 train_acc= 0.58727 val_loss= 0.68826 val_acc= 0.58065 time= 0.00000
Epoch: 0012 train_loss= 0.68812 train_acc= 0.60909 val_loss= 0.68777 val_acc= 0.58065 time= 0.01563
Epoch: 0013 train_loss= 0.68730 train_acc= 0.59455 val_loss= 0.68726 val_acc= 0.58065 time= 0.00000
Epoch: 0014 train_loss= 0.68748 train_acc= 0.59636 val_loss= 0.68669 val_acc= 0.58065 time= 0.01563
Epoch: 0015 train_loss= 0.68601 train_acc= 0.59818 val_loss= 0.68621 val_acc= 0.58065 time= 0.01563
Epoch: 0016 train_loss= 0.68537 train_acc= 0.59091 val_loss= 0.68591 val_acc= 0.58065 time= 0.02578
Epoch: 0017 train_loss= 0.68395 train_acc= 0.60000 val_loss= 0.68563 val_acc= 0.58065 time= 0.01563
Epoch: 0018 train_loss= 0.68189 train_acc= 0.62727 val_loss= 0.68532 val_acc= 0.58065 time= 0.01562
Epoch: 0019 train_loss= 0.68251 train_acc= 0.61636 val_loss= 0.68504 val_acc= 0.59677 time= 0.00202
Epoch: 0020 train_loss= 0.68096 train_acc= 0.61273 val_loss= 0.68479 val_acc= 0.59677 time= 0.01401
Epoch: 0021 train_loss= 0.68224 train_acc= 0.60909 val_loss= 0.68457 val_acc= 0.59677 time= 0.01563
Epoch: 0022 train_loss= 0.68161 train_acc= 0.63091 val_loss= 0.68415 val_acc= 0.61290 time= 0.01563
Epoch: 0023 train_loss= 0.67986 train_acc= 0.62909 val_loss= 0.68360 val_acc= 0.59677 time= 0.01563
Epoch: 0024 train_loss= 0.67922 train_acc= 0.61091 val_loss= 0.68329 val_acc= 0.62903 time= 0.01562
Epoch: 0025 train_loss= 0.67840 train_acc= 0.66727 val_loss= 0.68272 val_acc= 0.61290 time= 0.00000
Epoch: 0026 train_loss= 0.67580 train_acc= 0.60727 val_loss= 0.68261 val_acc= 0.64516 time= 0.01563
Epoch: 0027 train_loss= 0.67508 train_acc= 0.61818 val_loss= 0.68257 val_acc= 0.67742 time= 0.01563
Epoch: 0028 train_loss= 0.67476 train_acc= 0.66909 val_loss= 0.68207 val_acc= 0.67742 time= 0.01563
Epoch: 0029 train_loss= 0.67291 train_acc= 0.62182 val_loss= 0.68200 val_acc= 0.67742 time= 0.01563
Epoch: 0030 train_loss= 0.67323 train_acc= 0.65273 val_loss= 0.68184 val_acc= 0.67742 time= 0.01563
Epoch: 0031 train_loss= 0.67655 train_acc= 0.68182 val_loss= 0.68071 val_acc= 0.67742 time= 0.00000
Epoch: 0032 train_loss= 0.67707 train_acc= 0.66909 val_loss= 0.67948 val_acc= 0.64516 time= 0.01563
Epoch: 0033 train_loss= 0.67199 train_acc= 0.65818 val_loss= 0.67829 val_acc= 0.61290 time= 0.01563
Epoch: 0034 train_loss= 0.67638 train_acc= 0.61091 val_loss= 0.67763 val_acc= 0.59677 time= 0.01563
Epoch: 0035 train_loss= 0.66893 train_acc= 0.62909 val_loss= 0.67749 val_acc= 0.61290 time= 0.01563
Epoch: 0036 train_loss= 0.66784 train_acc= 0.62364 val_loss= 0.67810 val_acc= 0.66129 time= 0.00000
Epoch: 0037 train_loss= 0.67045 train_acc= 0.68727 val_loss= 0.67860 val_acc= 0.67742 time= 0.01562
Epoch: 0038 train_loss= 0.66551 train_acc= 0.71636 val_loss= 0.67873 val_acc= 0.70968 time= 0.01563
Epoch: 0039 train_loss= 0.66746 train_acc= 0.65636 val_loss= 0.67862 val_acc= 0.74194 time= 0.00000
Epoch: 0040 train_loss= 0.66763 train_acc= 0.70364 val_loss= 0.67741 val_acc= 0.69355 time= 0.01563
Epoch: 0041 train_loss= 0.66669 train_acc= 0.66727 val_loss= 0.67578 val_acc= 0.66129 time= 0.01563
Epoch: 0042 train_loss= 0.66419 train_acc= 0.69818 val_loss= 0.67418 val_acc= 0.61290 time= 0.01563
Epoch: 0043 train_loss= 0.66733 train_acc= 0.67091 val_loss= 0.67330 val_acc= 0.59677 time= 0.01563
Epoch: 0044 train_loss= 0.66693 train_acc= 0.62727 val_loss= 0.67293 val_acc= 0.59677 time= 0.00000
Epoch: 0045 train_loss= 0.66410 train_acc= 0.63818 val_loss= 0.67308 val_acc= 0.61290 time= 0.01562
Epoch: 0046 train_loss= 0.66097 train_acc= 0.68727 val_loss= 0.67356 val_acc= 0.66129 time= 0.01563
Epoch: 0047 train_loss= 0.66929 train_acc= 0.63273 val_loss= 0.67567 val_acc= 0.74194 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.66450 accuracy= 0.77419 time= 0.01563 
