Epoch: 0001 train_loss= 1.39417 train_acc= 0.29609 val_loss= 1.38928 val_acc= 0.37500 time= 0.39065
Epoch: 0002 train_loss= 1.39103 train_acc= 0.30028 val_loss= 1.38530 val_acc= 0.37500 time= 0.01563
Epoch: 0003 train_loss= 1.38848 train_acc= 0.30028 val_loss= 1.38174 val_acc= 0.37500 time= 0.01563
Epoch: 0004 train_loss= 1.38655 train_acc= 0.30028 val_loss= 1.37862 val_acc= 0.37500 time= 0.01563
Epoch: 0005 train_loss= 1.38518 train_acc= 0.30028 val_loss= 1.37589 val_acc= 0.37500 time= 0.01563
Epoch: 0006 train_loss= 1.38394 train_acc= 0.30028 val_loss= 1.37352 val_acc= 0.37500 time= 0.01563
Epoch: 0007 train_loss= 1.38330 train_acc= 0.30028 val_loss= 1.37137 val_acc= 0.37500 time= 0.01563
Epoch: 0008 train_loss= 1.38289 train_acc= 0.30028 val_loss= 1.36938 val_acc= 0.37500 time= 0.01563
Epoch: 0009 train_loss= 1.38247 train_acc= 0.30028 val_loss= 1.36752 val_acc= 0.37500 time= 0.01563
Epoch: 0010 train_loss= 1.38223 train_acc= 0.30028 val_loss= 1.36584 val_acc= 0.37500 time= 0.01563
Epoch: 0011 train_loss= 1.38207 train_acc= 0.30028 val_loss= 1.36424 val_acc= 0.37500 time= 0.01563
Epoch: 0012 train_loss= 1.38206 train_acc= 0.30028 val_loss= 1.36281 val_acc= 0.37500 time= 0.01563
Epoch: 0013 train_loss= 1.38184 train_acc= 0.30028 val_loss= 1.36158 val_acc= 0.37500 time= 0.01563
Epoch: 0014 train_loss= 1.38161 train_acc= 0.30028 val_loss= 1.36051 val_acc= 0.37500 time= 0.01563
Epoch: 0015 train_loss= 1.38153 train_acc= 0.30028 val_loss= 1.35960 val_acc= 0.37500 time= 0.01562
Epoch: 0016 train_loss= 1.38102 train_acc= 0.30028 val_loss= 1.35884 val_acc= 0.37500 time= 0.01563
Epoch: 0017 train_loss= 1.38049 train_acc= 0.30028 val_loss= 1.35811 val_acc= 0.37500 time= 0.01563
Epoch: 0018 train_loss= 1.37998 train_acc= 0.30028 val_loss= 1.35750 val_acc= 0.37500 time= 0.01563
Epoch: 0019 train_loss= 1.38000 train_acc= 0.30028 val_loss= 1.35707 val_acc= 0.37500 time= 0.01563
Epoch: 0020 train_loss= 1.38022 train_acc= 0.30028 val_loss= 1.35674 val_acc= 0.37500 time= 0.01563
Epoch: 0021 train_loss= 1.37945 train_acc= 0.30028 val_loss= 1.35649 val_acc= 0.37500 time= 0.01563
Epoch: 0022 train_loss= 1.37919 train_acc= 0.30028 val_loss= 1.35612 val_acc= 0.37500 time= 0.01563
Epoch: 0023 train_loss= 1.37922 train_acc= 0.30028 val_loss= 1.35565 val_acc= 0.37500 time= 0.01562
Epoch: 0024 train_loss= 1.37936 train_acc= 0.30028 val_loss= 1.35509 val_acc= 0.37500 time= 0.03125
Epoch: 0025 train_loss= 1.37915 train_acc= 0.30028 val_loss= 1.35447 val_acc= 0.37500 time= 0.01563
Epoch: 0026 train_loss= 1.37863 train_acc= 0.30028 val_loss= 1.35387 val_acc= 0.37500 time= 0.01563
Epoch: 0027 train_loss= 1.37854 train_acc= 0.30028 val_loss= 1.35333 val_acc= 0.37500 time= 0.01563
Epoch: 0028 train_loss= 1.37846 train_acc= 0.30028 val_loss= 1.35280 val_acc= 0.37500 time= 0.01563
Epoch: 0029 train_loss= 1.37828 train_acc= 0.30028 val_loss= 1.35242 val_acc= 0.37500 time= 0.01563
Epoch: 0030 train_loss= 1.37807 train_acc= 0.30028 val_loss= 1.35202 val_acc= 0.37500 time= 0.01563
Epoch: 0031 train_loss= 1.37829 train_acc= 0.30028 val_loss= 1.35182 val_acc= 0.37500 time= 0.01563
Epoch: 0032 train_loss= 1.37830 train_acc= 0.30028 val_loss= 1.35168 val_acc= 0.37500 time= 0.01562
Epoch: 0033 train_loss= 1.37784 train_acc= 0.30028 val_loss= 1.35157 val_acc= 0.37500 time= 0.01563
Epoch: 0034 train_loss= 1.37779 train_acc= 0.30028 val_loss= 1.35144 val_acc= 0.37500 time= 0.01563
Epoch: 0035 train_loss= 1.37769 train_acc= 0.30028 val_loss= 1.35124 val_acc= 0.37500 time= 0.01563
Epoch: 0036 train_loss= 1.37739 train_acc= 0.30028 val_loss= 1.35092 val_acc= 0.37500 time= 0.01563
Epoch: 0037 train_loss= 1.37755 train_acc= 0.30028 val_loss= 1.35049 val_acc= 0.37500 time= 0.01563
Epoch: 0038 train_loss= 1.37747 train_acc= 0.30028 val_loss= 1.35001 val_acc= 0.37500 time= 0.01563
Epoch: 0039 train_loss= 1.37746 train_acc= 0.30028 val_loss= 1.34961 val_acc= 0.37500 time= 0.01563
Epoch: 0040 train_loss= 1.37724 train_acc= 0.30028 val_loss= 1.34916 val_acc= 0.37500 time= 0.01562
Epoch: 0041 train_loss= 1.37738 train_acc= 0.30028 val_loss= 1.34901 val_acc= 0.37500 time= 0.01563
Epoch: 0042 train_loss= 1.37737 train_acc= 0.30028 val_loss= 1.34865 val_acc= 0.37500 time= 0.01562
Epoch: 0043 train_loss= 1.37714 train_acc= 0.30028 val_loss= 1.34852 val_acc= 0.37500 time= 0.01563
Epoch: 0044 train_loss= 1.37682 train_acc= 0.30028 val_loss= 1.34829 val_acc= 0.37500 time= 0.01563
Epoch: 0045 train_loss= 1.37721 train_acc= 0.30028 val_loss= 1.34819 val_acc= 0.37500 time= 0.01563
Epoch: 0046 train_loss= 1.37719 train_acc= 0.30028 val_loss= 1.34847 val_acc= 0.37500 time= 0.01563
Epoch: 0047 train_loss= 1.37656 train_acc= 0.30028 val_loss= 1.34838 val_acc= 0.37500 time= 0.01563
Epoch: 0048 train_loss= 1.37695 train_acc= 0.30028 val_loss= 1.34814 val_acc= 0.37500 time= 0.01563
Epoch: 0049 train_loss= 1.37687 train_acc= 0.30028 val_loss= 1.34814 val_acc= 0.37500 time= 0.01562
Epoch: 0050 train_loss= 1.37685 train_acc= 0.30028 val_loss= 1.34798 val_acc= 0.37500 time= 0.01563
Epoch: 0051 train_loss= 1.37683 train_acc= 0.30028 val_loss= 1.34777 val_acc= 0.37500 time= 0.03125
Epoch: 0052 train_loss= 1.37674 train_acc= 0.30028 val_loss= 1.34776 val_acc= 0.37500 time= 0.01563
Epoch: 0053 train_loss= 1.37630 train_acc= 0.30028 val_loss= 1.34777 val_acc= 0.37500 time= 0.01563
Epoch: 0054 train_loss= 1.37643 train_acc= 0.30028 val_loss= 1.34788 val_acc= 0.37500 time= 0.01563
Epoch: 0055 train_loss= 1.37632 train_acc= 0.30028 val_loss= 1.34740 val_acc= 0.37500 time= 0.01563
Epoch: 0056 train_loss= 1.37630 train_acc= 0.30028 val_loss= 1.34699 val_acc= 0.37500 time= 0.01563
Epoch: 0057 train_loss= 1.37617 train_acc= 0.30028 val_loss= 1.34705 val_acc= 0.37500 time= 0.01563
Epoch: 0058 train_loss= 1.37709 train_acc= 0.30028 val_loss= 1.34701 val_acc= 0.37500 time= 0.01563
Epoch: 0059 train_loss= 1.37618 train_acc= 0.30028 val_loss= 1.34669 val_acc= 0.37500 time= 0.01563
Epoch: 0060 train_loss= 1.37632 train_acc= 0.30028 val_loss= 1.34659 val_acc= 0.37500 time= 0.01563
Epoch: 0061 train_loss= 1.37599 train_acc= 0.30028 val_loss= 1.34657 val_acc= 0.37500 time= 0.01563
Epoch: 0062 train_loss= 1.37615 train_acc= 0.30028 val_loss= 1.34664 val_acc= 0.37500 time= 0.01563
Epoch: 0063 train_loss= 1.37629 train_acc= 0.30028 val_loss= 1.34663 val_acc= 0.37500 time= 0.01563
Epoch: 0064 train_loss= 1.37643 train_acc= 0.30028 val_loss= 1.34626 val_acc= 0.37500 time= 0.01563
Epoch: 0065 train_loss= 1.37653 train_acc= 0.30028 val_loss= 1.34533 val_acc= 0.37500 time= 0.01563
Epoch: 0066 train_loss= 1.37636 train_acc= 0.30028 val_loss= 1.34474 val_acc= 0.37500 time= 0.01563
Epoch: 0067 train_loss= 1.37622 train_acc= 0.30028 val_loss= 1.34432 val_acc= 0.37500 time= 0.01563
Epoch: 0068 train_loss= 1.37596 train_acc= 0.30028 val_loss= 1.34494 val_acc= 0.37500 time= 0.01563
Epoch: 0069 train_loss= 1.37578 train_acc= 0.30028 val_loss= 1.34628 val_acc= 0.37500 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37689 accuracy= 0.29204 time= 0.00000 
