Epoch: 0001 train_loss= 1.39016 train_acc= 0.25838 val_loss= 1.38815 val_acc= 0.33929 time= 0.32816
Epoch: 0002 train_loss= 1.38813 train_acc= 0.31145 val_loss= 1.38646 val_acc= 0.33929 time= 0.01562
Epoch: 0003 train_loss= 1.38691 train_acc= 0.31145 val_loss= 1.38493 val_acc= 0.33929 time= 0.01563
Epoch: 0004 train_loss= 1.38532 train_acc= 0.31145 val_loss= 1.38361 val_acc= 0.33929 time= 0.01563
Epoch: 0005 train_loss= 1.38371 train_acc= 0.31145 val_loss= 1.38255 val_acc= 0.33929 time= 0.01563
Epoch: 0006 train_loss= 1.38248 train_acc= 0.31145 val_loss= 1.38172 val_acc= 0.33929 time= 0.01563
Epoch: 0007 train_loss= 1.38110 train_acc= 0.31145 val_loss= 1.38117 val_acc= 0.33929 time= 0.01563
Epoch: 0008 train_loss= 1.37993 train_acc= 0.31145 val_loss= 1.38090 val_acc= 0.33929 time= 0.01563
Epoch: 0009 train_loss= 1.37888 train_acc= 0.31145 val_loss= 1.38091 val_acc= 0.33929 time= 0.01563
Epoch: 0010 train_loss= 1.37816 train_acc= 0.31145 val_loss= 1.38134 val_acc= 0.33929 time= 0.01563
Epoch: 0011 train_loss= 1.37738 train_acc= 0.31145 val_loss= 1.38216 val_acc= 0.33929 time= 0.03125
Epoch: 0012 train_loss= 1.37692 train_acc= 0.31145 val_loss= 1.38332 val_acc= 0.33929 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37649 accuracy= 0.29204 time= 0.00000 
