Epoch: 0001 train_loss= 2.08534 train_acc= 0.12830 val_loss= 2.08591 val_acc= 0.10345 time= 0.22647
Epoch: 0002 train_loss= 2.08396 train_acc= 0.12830 val_loss= 2.08716 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08282 train_acc= 0.13208 val_loss= 2.08852 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.08180 train_acc= 0.13585 val_loss= 2.08995 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.08040 train_acc= 0.13208 val_loss= 2.09142 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07963 train_acc= 0.13208 val_loss= 2.09293 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.07849 train_acc= 0.12830 val_loss= 2.09444 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.07602 train_acc= 0.13208 val_loss= 2.09613 val_acc= 0.10345 time= 0.01562
Epoch: 0009 train_loss= 2.07513 train_acc= 0.13208 val_loss= 2.09791 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.07343 train_acc= 0.13208 val_loss= 2.09982 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.07255 train_acc= 0.13208 val_loss= 2.10201 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.07039 train_acc= 0.13208 val_loss= 2.10433 val_acc= 0.10345 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 2.08155 accuracy= 0.06780 time= 0.00000 
