Epoch: 0001 train_loss= 1.39409 train_acc= 0.26059 val_loss= 1.39188 val_acc= 0.26786 time= 0.15626
Epoch: 0002 train_loss= 1.39038 train_acc= 0.31922 val_loss= 1.39069 val_acc= 0.26786 time= 0.01563
Epoch: 0003 train_loss= 1.38710 train_acc= 0.31596 val_loss= 1.39034 val_acc= 0.26786 time= 0.01563
Epoch: 0004 train_loss= 1.38458 train_acc= 0.33225 val_loss= 1.39078 val_acc= 0.26786 time= 0.01563
Epoch: 0005 train_loss= 1.38247 train_acc= 0.32899 val_loss= 1.39174 val_acc= 0.26786 time= 0.01563
Epoch: 0006 train_loss= 1.38106 train_acc= 0.32899 val_loss= 1.39302 val_acc= 0.26786 time= 0.01563
Epoch: 0007 train_loss= 1.38006 train_acc= 0.32899 val_loss= 1.39440 val_acc= 0.26786 time= 0.01563
Epoch: 0008 train_loss= 1.37896 train_acc= 0.32899 val_loss= 1.39573 val_acc= 0.26786 time= 0.01563
Epoch: 0009 train_loss= 1.37895 train_acc= 0.32899 val_loss= 1.39695 val_acc= 0.26786 time= 0.00000
Epoch: 0010 train_loss= 1.37890 train_acc= 0.32899 val_loss= 1.39792 val_acc= 0.26786 time= 0.01563
Epoch: 0011 train_loss= 1.37792 train_acc= 0.32899 val_loss= 1.39881 val_acc= 0.26786 time= 0.01563
Epoch: 0012 train_loss= 1.37784 train_acc= 0.32899 val_loss= 1.39946 val_acc= 0.26786 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38616 accuracy= 0.29204 time= 0.01563 
