Epoch: 0001 train_loss= 1.39412 train_acc= 0.22486 val_loss= 1.39094 val_acc= 0.35000 time= 0.40627
Epoch: 0002 train_loss= 1.39093 train_acc= 0.30028 val_loss= 1.38755 val_acc= 0.35000 time= 0.01563
Epoch: 0003 train_loss= 1.38837 train_acc= 0.30028 val_loss= 1.38460 val_acc= 0.35000 time= 0.01563
Epoch: 0004 train_loss= 1.38640 train_acc= 0.30028 val_loss= 1.38186 val_acc= 0.35000 time= 0.01563
Epoch: 0005 train_loss= 1.38501 train_acc= 0.30028 val_loss= 1.37949 val_acc= 0.35000 time= 0.01563
Epoch: 0006 train_loss= 1.38420 train_acc= 0.30028 val_loss= 1.37752 val_acc= 0.35000 time= 0.01563
Epoch: 0007 train_loss= 1.38383 train_acc= 0.30028 val_loss= 1.37597 val_acc= 0.35000 time= 0.01563
Epoch: 0008 train_loss= 1.38358 train_acc= 0.30028 val_loss= 1.37472 val_acc= 0.35000 time= 0.01563
Epoch: 0009 train_loss= 1.38324 train_acc= 0.30028 val_loss= 1.37373 val_acc= 0.35000 time= 0.01563
Epoch: 0010 train_loss= 1.38335 train_acc= 0.30028 val_loss= 1.37293 val_acc= 0.35000 time= 0.01563
Epoch: 0011 train_loss= 1.38281 train_acc= 0.30028 val_loss= 1.37229 val_acc= 0.35000 time= 0.01563
Epoch: 0012 train_loss= 1.38312 train_acc= 0.30028 val_loss= 1.37179 val_acc= 0.35000 time= 0.01563
Epoch: 0013 train_loss= 1.38326 train_acc= 0.30028 val_loss= 1.37130 val_acc= 0.35000 time= 0.01563
Epoch: 0014 train_loss= 1.38264 train_acc= 0.30028 val_loss= 1.37103 val_acc= 0.35000 time= 0.01563
Epoch: 0015 train_loss= 1.38252 train_acc= 0.30028 val_loss= 1.37090 val_acc= 0.35000 time= 0.01563
Epoch: 0016 train_loss= 1.38234 train_acc= 0.30028 val_loss= 1.37077 val_acc= 0.35000 time= 0.01563
Epoch: 0017 train_loss= 1.38173 train_acc= 0.30028 val_loss= 1.37059 val_acc= 0.35000 time= 0.01563
Epoch: 0018 train_loss= 1.38162 train_acc= 0.30028 val_loss= 1.37029 val_acc= 0.35000 time= 0.01563
Epoch: 0019 train_loss= 1.38173 train_acc= 0.30028 val_loss= 1.37000 val_acc= 0.35000 time= 0.01563
Epoch: 0020 train_loss= 1.38149 train_acc= 0.30028 val_loss= 1.36968 val_acc= 0.35000 time= 0.01563
Epoch: 0021 train_loss= 1.38142 train_acc= 0.30028 val_loss= 1.36939 val_acc= 0.35000 time= 0.01563
Epoch: 0022 train_loss= 1.38097 train_acc= 0.30028 val_loss= 1.36901 val_acc= 0.35000 time= 0.01563
Epoch: 0023 train_loss= 1.38088 train_acc= 0.30028 val_loss= 1.36876 val_acc= 0.35000 time= 0.01563
Epoch: 0024 train_loss= 1.38132 train_acc= 0.30028 val_loss= 1.36859 val_acc= 0.35000 time= 0.01563
Epoch: 0025 train_loss= 1.38108 train_acc= 0.30028 val_loss= 1.36847 val_acc= 0.35000 time= 0.01563
Epoch: 0026 train_loss= 1.38099 train_acc= 0.30028 val_loss= 1.36833 val_acc= 0.35000 time= 0.01563
Epoch: 0027 train_loss= 1.38076 train_acc= 0.30028 val_loss= 1.36814 val_acc= 0.35000 time= 0.01563
Epoch: 0028 train_loss= 1.38067 train_acc= 0.30028 val_loss= 1.36789 val_acc= 0.35000 time= 0.01563
Epoch: 0029 train_loss= 1.38056 train_acc= 0.30028 val_loss= 1.36764 val_acc= 0.35000 time= 0.01563
Epoch: 0030 train_loss= 1.38074 train_acc= 0.30028 val_loss= 1.36726 val_acc= 0.35000 time= 0.01563
Epoch: 0031 train_loss= 1.38079 train_acc= 0.30028 val_loss= 1.36693 val_acc= 0.35000 time= 0.01563
Epoch: 0032 train_loss= 1.38038 train_acc= 0.30028 val_loss= 1.36674 val_acc= 0.35000 time= 0.01563
Epoch: 0033 train_loss= 1.38059 train_acc= 0.30028 val_loss= 1.36666 val_acc= 0.35000 time= 0.01563
Epoch: 0034 train_loss= 1.38044 train_acc= 0.30028 val_loss= 1.36678 val_acc= 0.35000 time= 0.01563
Epoch: 0035 train_loss= 1.38027 train_acc= 0.30028 val_loss= 1.36695 val_acc= 0.35000 time= 0.01563
Epoch: 0036 train_loss= 1.38033 train_acc= 0.30028 val_loss= 1.36704 val_acc= 0.35000 time= 0.01563
Epoch: 0037 train_loss= 1.38024 train_acc= 0.30028 val_loss= 1.36714 val_acc= 0.35000 time= 0.01562
Epoch: 0038 train_loss= 1.38023 train_acc= 0.30028 val_loss= 1.36719 val_acc= 0.35000 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37851 accuracy= 0.31667 time= 0.01563 
