Epoch: 0001 train_loss= 2.08978 train_acc= 0.06918 val_loss= 2.07999 val_acc= 0.10345 time= 0.10938
Epoch: 0002 train_loss= 2.08779 train_acc= 0.07547 val_loss= 2.07950 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08601 train_acc= 0.08176 val_loss= 2.07931 val_acc= 0.13793 time= 0.00000
Epoch: 0004 train_loss= 2.08456 train_acc= 0.16352 val_loss= 2.07930 val_acc= 0.13793 time= 0.01562
Epoch: 0005 train_loss= 2.08317 train_acc= 0.16352 val_loss= 2.07949 val_acc= 0.13793 time= 0.00000
Epoch: 0006 train_loss= 2.08256 train_acc= 0.16352 val_loss= 2.07944 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.08160 train_acc= 0.16352 val_loss= 2.07908 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.08049 train_acc= 0.16352 val_loss= 2.07880 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.07958 train_acc= 0.16981 val_loss= 2.07858 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.07847 train_acc= 0.16352 val_loss= 2.07843 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.07813 train_acc= 0.16352 val_loss= 2.07835 val_acc= 0.13793 time= 0.01562
Epoch: 0012 train_loss= 2.07625 train_acc= 0.15094 val_loss= 2.07833 val_acc= 0.13793 time= 0.00000
Epoch: 0013 train_loss= 2.07477 train_acc= 0.16352 val_loss= 2.07838 val_acc= 0.13793 time= 0.01563
Epoch: 0014 train_loss= 2.07421 train_acc= 0.16352 val_loss= 2.07851 val_acc= 0.13793 time= 0.00000
Epoch: 0015 train_loss= 2.07169 train_acc= 0.18239 val_loss= 2.07874 val_acc= 0.13793 time= 0.01563
Epoch: 0016 train_loss= 2.07122 train_acc= 0.16352 val_loss= 2.07906 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.07957 accuracy= 0.11864 time= 0.01563 
