Epoch: 0001 train_loss= 1.39440 train_acc= 0.19218 val_loss= 1.39232 val_acc= 0.23214 time= 0.12502
Epoch: 0002 train_loss= 1.39128 train_acc= 0.32573 val_loss= 1.39106 val_acc= 0.23214 time= 0.01562
Epoch: 0003 train_loss= 1.38851 train_acc= 0.32573 val_loss= 1.39052 val_acc= 0.23214 time= 0.00000
Epoch: 0004 train_loss= 1.38630 train_acc= 0.32573 val_loss= 1.39061 val_acc= 0.23214 time= 0.01563
Epoch: 0005 train_loss= 1.38452 train_acc= 0.32573 val_loss= 1.39119 val_acc= 0.23214 time= 0.01563
Epoch: 0006 train_loss= 1.38311 train_acc= 0.32573 val_loss= 1.39202 val_acc= 0.23214 time= 0.01563
Epoch: 0007 train_loss= 1.38244 train_acc= 0.32573 val_loss= 1.39303 val_acc= 0.23214 time= 0.01563
Epoch: 0008 train_loss= 1.38170 train_acc= 0.32573 val_loss= 1.39420 val_acc= 0.23214 time= 0.01563
Epoch: 0009 train_loss= 1.38092 train_acc= 0.32573 val_loss= 1.39549 val_acc= 0.23214 time= 0.01563
Epoch: 0010 train_loss= 1.38108 train_acc= 0.32573 val_loss= 1.39688 val_acc= 0.23214 time= 0.01563
Epoch: 0011 train_loss= 1.38044 train_acc= 0.32573 val_loss= 1.39821 val_acc= 0.23214 time= 0.01563
Epoch: 0012 train_loss= 1.38004 train_acc= 0.32573 val_loss= 1.39952 val_acc= 0.23214 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38528 accuracy= 0.31858 time= 0.00000 
