Epoch: 0001 train_loss= 0.69698 train_acc= 0.54545 val_loss= 0.69207 val_acc= 0.59016 time= 0.86049
Epoch: 0002 train_loss= 0.69776 train_acc= 0.53766 val_loss= 0.69161 val_acc= 0.59016 time= 0.00000
Epoch: 0003 train_loss= 0.69565 train_acc= 0.54545 val_loss= 0.69147 val_acc= 0.59016 time= 0.01563
Epoch: 0004 train_loss= 0.69591 train_acc= 0.53766 val_loss= 0.69144 val_acc= 0.59016 time= 0.00000
Epoch: 0005 train_loss= 0.69587 train_acc= 0.53896 val_loss= 0.69109 val_acc= 0.59016 time= 0.01563
Epoch: 0006 train_loss= 0.69554 train_acc= 0.53896 val_loss= 0.69084 val_acc= 0.59016 time= 0.00000
Epoch: 0007 train_loss= 0.69352 train_acc= 0.53506 val_loss= 0.69047 val_acc= 0.59016 time= 0.01562
Epoch: 0008 train_loss= 0.69279 train_acc= 0.54156 val_loss= 0.68998 val_acc= 0.59016 time= 0.00000
Epoch: 0009 train_loss= 0.69275 train_acc= 0.54026 val_loss= 0.68941 val_acc= 0.59016 time= 0.00000
Epoch: 0010 train_loss= 0.69312 train_acc= 0.54416 val_loss= 0.68901 val_acc= 0.59016 time= 0.01563
Epoch: 0011 train_loss= 0.69313 train_acc= 0.53377 val_loss= 0.68880 val_acc= 0.59016 time= 0.00000
Epoch: 0012 train_loss= 0.69366 train_acc= 0.53766 val_loss= 0.68872 val_acc= 0.59016 time= 0.01563
Epoch: 0013 train_loss= 0.69281 train_acc= 0.54416 val_loss= 0.68869 val_acc= 0.59016 time= 0.00000
Epoch: 0014 train_loss= 0.69152 train_acc= 0.53636 val_loss= 0.68865 val_acc= 0.59016 time= 0.01563
Epoch: 0015 train_loss= 0.69176 train_acc= 0.53506 val_loss= 0.68862 val_acc= 0.59016 time= 0.00000
Epoch: 0016 train_loss= 0.69121 train_acc= 0.54026 val_loss= 0.68865 val_acc= 0.59016 time= 0.00000
Epoch: 0017 train_loss= 0.69247 train_acc= 0.54026 val_loss= 0.68857 val_acc= 0.59016 time= 0.01563
Epoch: 0018 train_loss= 0.69084 train_acc= 0.54416 val_loss= 0.68851 val_acc= 0.59016 time= 0.00000
Epoch: 0019 train_loss= 0.69061 train_acc= 0.54026 val_loss= 0.68838 val_acc= 0.59016 time= 0.01563
Epoch: 0020 train_loss= 0.69169 train_acc= 0.52987 val_loss= 0.68828 val_acc= 0.59016 time= 0.00000
Epoch: 0021 train_loss= 0.69119 train_acc= 0.54545 val_loss= 0.68823 val_acc= 0.59016 time= 0.00000
Epoch: 0022 train_loss= 0.68923 train_acc= 0.54286 val_loss= 0.68797 val_acc= 0.59016 time= 0.01563
Epoch: 0023 train_loss= 0.69147 train_acc= 0.53766 val_loss= 0.68777 val_acc= 0.59016 time= 0.00000
Epoch: 0024 train_loss= 0.69085 train_acc= 0.54545 val_loss= 0.68761 val_acc= 0.59016 time= 0.01563
Epoch: 0025 train_loss= 0.69078 train_acc= 0.53896 val_loss= 0.68758 val_acc= 0.59016 time= 0.00000
Epoch: 0026 train_loss= 0.69036 train_acc= 0.53896 val_loss= 0.68756 val_acc= 0.59016 time= 0.00000
Epoch: 0027 train_loss= 0.69080 train_acc= 0.53766 val_loss= 0.68762 val_acc= 0.59016 time= 0.01563
Epoch: 0028 train_loss= 0.69010 train_acc= 0.53636 val_loss= 0.68757 val_acc= 0.59016 time= 0.00000
Epoch: 0029 train_loss= 0.68969 train_acc= 0.53766 val_loss= 0.68746 val_acc= 0.59016 time= 0.01563
Epoch: 0030 train_loss= 0.69123 train_acc= 0.53636 val_loss= 0.68748 val_acc= 0.59016 time= 0.00000
Epoch: 0031 train_loss= 0.69112 train_acc= 0.54156 val_loss= 0.68737 val_acc= 0.59016 time= 0.00000
Epoch: 0032 train_loss= 0.69072 train_acc= 0.53247 val_loss= 0.68721 val_acc= 0.59016 time= 0.01563
Epoch: 0033 train_loss= 0.68998 train_acc= 0.54026 val_loss= 0.68716 val_acc= 0.59016 time= 0.00000
Epoch: 0034 train_loss= 0.68965 train_acc= 0.54675 val_loss= 0.68718 val_acc= 0.59016 time= 0.01563
Epoch: 0035 train_loss= 0.69031 train_acc= 0.53377 val_loss= 0.68720 val_acc= 0.59016 time= 0.00000
Epoch: 0036 train_loss= 0.69074 train_acc= 0.53896 val_loss= 0.68729 val_acc= 0.59016 time= 0.00000
Epoch: 0037 train_loss= 0.69030 train_acc= 0.54416 val_loss= 0.68735 val_acc= 0.59016 time= 0.01563
Epoch: 0038 train_loss= 0.69143 train_acc= 0.53896 val_loss= 0.68743 val_acc= 0.59016 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.70748 accuracy= 0.44262 time= 0.00000 
