Epoch: 0001 train_loss= 0.69759 train_acc= 0.56364 val_loss= 0.70258 val_acc= 0.47541 time= 0.09420
Epoch: 0002 train_loss= 0.69682 train_acc= 0.55758 val_loss= 0.70414 val_acc= 0.47541 time= 0.00000
Epoch: 0003 train_loss= 0.69572 train_acc= 0.56667 val_loss= 0.70550 val_acc= 0.47541 time= 0.01551
Epoch: 0004 train_loss= 0.69464 train_acc= 0.56667 val_loss= 0.70707 val_acc= 0.47541 time= 0.01563
Epoch: 0005 train_loss= 0.69434 train_acc= 0.56667 val_loss= 0.70872 val_acc= 0.47541 time= 0.00000
Epoch: 0006 train_loss= 0.69322 train_acc= 0.56667 val_loss= 0.71055 val_acc= 0.47541 time= 0.01563
Epoch: 0007 train_loss= 0.69271 train_acc= 0.56667 val_loss= 0.71246 val_acc= 0.47541 time= 0.01563
Epoch: 0008 train_loss= 0.69196 train_acc= 0.56667 val_loss= 0.71447 val_acc= 0.47541 time= 0.00000
Epoch: 0009 train_loss= 0.69172 train_acc= 0.56667 val_loss= 0.71641 val_acc= 0.47541 time= 0.01563
Epoch: 0010 train_loss= 0.69018 train_acc= 0.56667 val_loss= 0.71818 val_acc= 0.47541 time= 0.01563
Epoch: 0011 train_loss= 0.69119 train_acc= 0.56667 val_loss= 0.71948 val_acc= 0.47541 time= 0.00000
Epoch: 0012 train_loss= 0.69116 train_acc= 0.56667 val_loss= 0.72031 val_acc= 0.47541 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69530 accuracy= 0.54098 time= 0.00000 
