Epoch: 0001 train_loss= 0.70282 train_acc= 0.43939 val_loss= 0.70231 val_acc= 0.44262 time= 0.19404
Epoch: 0002 train_loss= 0.70189 train_acc= 0.44242 val_loss= 0.70042 val_acc= 0.44262 time= 0.00700
Epoch: 0003 train_loss= 0.69975 train_acc= 0.44545 val_loss= 0.69879 val_acc= 0.44262 time= 0.00600
Epoch: 0004 train_loss= 0.69831 train_acc= 0.43333 val_loss= 0.69732 val_acc= 0.52459 time= 0.00600
Epoch: 0005 train_loss= 0.69788 train_acc= 0.49394 val_loss= 0.69601 val_acc= 0.54098 time= 0.00600
Epoch: 0006 train_loss= 0.69500 train_acc= 0.56061 val_loss= 0.69478 val_acc= 0.54098 time= 0.00500
Epoch: 0007 train_loss= 0.69508 train_acc= 0.54242 val_loss= 0.69363 val_acc= 0.54098 time= 0.00600
Epoch: 0008 train_loss= 0.69440 train_acc= 0.55455 val_loss= 0.69254 val_acc= 0.55738 time= 0.00600
Epoch: 0009 train_loss= 0.69360 train_acc= 0.55455 val_loss= 0.69158 val_acc= 0.55738 time= 0.00600
Epoch: 0010 train_loss= 0.69144 train_acc= 0.56061 val_loss= 0.69070 val_acc= 0.55738 time= 0.00500
Epoch: 0011 train_loss= 0.69058 train_acc= 0.56061 val_loss= 0.68991 val_acc= 0.55738 time= 0.00700
Epoch: 0012 train_loss= 0.69134 train_acc= 0.54848 val_loss= 0.68924 val_acc= 0.55738 time= 0.00500
Epoch: 0013 train_loss= 0.68997 train_acc= 0.55455 val_loss= 0.68868 val_acc= 0.55738 time= 0.00600
Epoch: 0014 train_loss= 0.68832 train_acc= 0.56061 val_loss= 0.68825 val_acc= 0.55738 time= 0.00600
Epoch: 0015 train_loss= 0.69061 train_acc= 0.56364 val_loss= 0.68798 val_acc= 0.55738 time= 0.00600
Epoch: 0016 train_loss= 0.68858 train_acc= 0.55152 val_loss= 0.68789 val_acc= 0.55738 time= 0.00600
Epoch: 0017 train_loss= 0.68699 train_acc= 0.55758 val_loss= 0.68789 val_acc= 0.55738 time= 0.00600
Epoch: 0018 train_loss= 0.69019 train_acc= 0.55455 val_loss= 0.68797 val_acc= 0.55738 time= 0.00500
Epoch: 0019 train_loss= 0.69079 train_acc= 0.55455 val_loss= 0.68799 val_acc= 0.55738 time= 0.00600
Epoch: 0020 train_loss= 0.69027 train_acc= 0.55152 val_loss= 0.68792 val_acc= 0.55738 time= 0.00600
Epoch: 0021 train_loss= 0.68793 train_acc= 0.55758 val_loss= 0.68779 val_acc= 0.55738 time= 0.00600
Epoch: 0022 train_loss= 0.68849 train_acc= 0.55758 val_loss= 0.68766 val_acc= 0.55738 time= 0.00600
Epoch: 0023 train_loss= 0.68715 train_acc= 0.56061 val_loss= 0.68753 val_acc= 0.55738 time= 0.00700
Epoch: 0024 train_loss= 0.68934 train_acc= 0.55758 val_loss= 0.68745 val_acc= 0.55738 time= 0.00600
Epoch: 0025 train_loss= 0.68623 train_acc= 0.56061 val_loss= 0.68740 val_acc= 0.55738 time= 0.00600
Epoch: 0026 train_loss= 0.69004 train_acc= 0.55455 val_loss= 0.68735 val_acc= 0.55738 time= 0.00600
Epoch: 0027 train_loss= 0.68876 train_acc= 0.55455 val_loss= 0.68732 val_acc= 0.55738 time= 0.00600
Epoch: 0028 train_loss= 0.68679 train_acc= 0.56061 val_loss= 0.68732 val_acc= 0.55738 time= 0.00600
Epoch: 0029 train_loss= 0.68732 train_acc= 0.56061 val_loss= 0.68732 val_acc= 0.55738 time= 0.00700
Epoch: 0030 train_loss= 0.68746 train_acc= 0.56061 val_loss= 0.68732 val_acc= 0.55738 time= 0.00600
Epoch: 0031 train_loss= 0.68856 train_acc= 0.55758 val_loss= 0.68734 val_acc= 0.55738 time= 0.00600
Epoch: 0032 train_loss= 0.68717 train_acc= 0.55455 val_loss= 0.68736 val_acc= 0.55738 time= 0.00600
Epoch: 0033 train_loss= 0.68808 train_acc= 0.55758 val_loss= 0.68737 val_acc= 0.55738 time= 0.00500
Epoch: 0034 train_loss= 0.68692 train_acc= 0.55758 val_loss= 0.68734 val_acc= 0.55738 time= 0.00500
Epoch: 0035 train_loss= 0.68881 train_acc= 0.55152 val_loss= 0.68731 val_acc= 0.55738 time= 0.00500
Epoch: 0036 train_loss= 0.68794 train_acc= 0.55758 val_loss= 0.68729 val_acc= 0.55738 time= 0.00500
Epoch: 0037 train_loss= 0.68657 train_acc= 0.55455 val_loss= 0.68725 val_acc= 0.55738 time= 0.00600
Epoch: 0038 train_loss= 0.68779 train_acc= 0.55455 val_loss= 0.68722 val_acc= 0.55738 time= 0.00500
Epoch: 0039 train_loss= 0.68792 train_acc= 0.56061 val_loss= 0.68718 val_acc= 0.55738 time= 0.00600
Epoch: 0040 train_loss= 0.68573 train_acc= 0.56061 val_loss= 0.68717 val_acc= 0.55738 time= 0.00600
Epoch: 0041 train_loss= 0.68670 train_acc= 0.55758 val_loss= 0.68719 val_acc= 0.55738 time= 0.00172
Epoch: 0042 train_loss= 0.68804 train_acc= 0.55758 val_loss= 0.68722 val_acc= 0.55738 time= 0.00000
Epoch: 0043 train_loss= 0.68470 train_acc= 0.55758 val_loss= 0.68726 val_acc= 0.55738 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.71577 accuracy= 0.44262 time= 0.00000 
