Epoch: 0001 train_loss= 1.39393 train_acc= 0.30447 val_loss= 1.39274 val_acc= 0.26786 time= 0.37502
Epoch: 0002 train_loss= 1.39005 train_acc= 0.30307 val_loss= 1.39241 val_acc= 0.26786 time= 0.01563
Epoch: 0003 train_loss= 1.38696 train_acc= 0.30307 val_loss= 1.39285 val_acc= 0.26786 time= 0.01563
Epoch: 0004 train_loss= 1.38437 train_acc= 0.30307 val_loss= 1.39383 val_acc= 0.26786 time= 0.01563
Epoch: 0005 train_loss= 1.38265 train_acc= 0.30307 val_loss= 1.39506 val_acc= 0.26786 time= 0.01563
Epoch: 0006 train_loss= 1.38167 train_acc= 0.30307 val_loss= 1.39637 val_acc= 0.26786 time= 0.01563
Epoch: 0007 train_loss= 1.38111 train_acc= 0.30307 val_loss= 1.39744 val_acc= 0.26786 time= 0.01563
Epoch: 0008 train_loss= 1.38056 train_acc= 0.30307 val_loss= 1.39815 val_acc= 0.26786 time= 0.01563
Epoch: 0009 train_loss= 1.38040 train_acc= 0.30307 val_loss= 1.39839 val_acc= 0.26786 time= 0.01562
Epoch: 0010 train_loss= 1.38051 train_acc= 0.30307 val_loss= 1.39820 val_acc= 0.26786 time= 0.01563
Epoch: 0011 train_loss= 1.38050 train_acc= 0.30307 val_loss= 1.39768 val_acc= 0.26786 time= 0.01563
Epoch: 0012 train_loss= 1.37988 train_acc= 0.30307 val_loss= 1.39693 val_acc= 0.26786 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38066 accuracy= 0.30088 time= 0.01563 
