Epoch: 0001 train_loss= 0.91411 train_acc= 0.46909 val_loss= 0.83597 val_acc= 0.45902 time= 0.18183
Epoch: 0002 train_loss= 0.80466 train_acc= 0.49273 val_loss= 0.68460 val_acc= 0.57377 time= 0.01563
Epoch: 0003 train_loss= 0.77336 train_acc= 0.51818 val_loss= 0.66878 val_acc= 0.59016 time= 0.01563
Epoch: 0004 train_loss= 0.72835 train_acc= 0.54545 val_loss= 0.70790 val_acc= 0.60656 time= 0.01563
Epoch: 0005 train_loss= 0.78464 train_acc= 0.50545 val_loss= 0.74087 val_acc= 0.60656 time= 0.01563
Epoch: 0006 train_loss= 1.20636 train_acc= 0.52727 val_loss= 0.72719 val_acc= 0.60656 time= 0.00000
Epoch: 0007 train_loss= 0.80750 train_acc= 0.53636 val_loss= 0.70237 val_acc= 0.60656 time= 0.01563
Epoch: 0008 train_loss= 0.72786 train_acc= 0.49818 val_loss= 0.68739 val_acc= 0.60656 time= 0.01563
Epoch: 0009 train_loss= 0.85175 train_acc= 0.52182 val_loss= 0.67193 val_acc= 0.60656 time= 0.01563
Epoch: 0010 train_loss= 0.73906 train_acc= 0.52182 val_loss= 0.67311 val_acc= 0.59016 time= 0.01563
Epoch: 0011 train_loss= 0.71832 train_acc= 0.47273 val_loss= 0.68453 val_acc= 0.60656 time= 0.01563
Epoch: 0012 train_loss= 0.78939 train_acc= 0.53636 val_loss= 0.70954 val_acc= 0.47541 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.73011 accuracy= 0.39344 time= 0.00000 
