Epoch: 0001 train_loss= 0.70153 train_acc= 0.42121 val_loss= 0.69884 val_acc= 0.50820 time= 0.12501
Epoch: 0002 train_loss= 0.69788 train_acc= 0.57273 val_loss= 0.69725 val_acc= 0.50820 time= 0.01563
Epoch: 0003 train_loss= 0.69532 train_acc= 0.57576 val_loss= 0.69634 val_acc= 0.50820 time= 0.01563
Epoch: 0004 train_loss= 0.69343 train_acc= 0.57576 val_loss= 0.69601 val_acc= 0.50820 time= 0.00000
Epoch: 0005 train_loss= 0.69203 train_acc= 0.57576 val_loss= 0.69617 val_acc= 0.50820 time= 0.01563
Epoch: 0006 train_loss= 0.69063 train_acc= 0.57576 val_loss= 0.69673 val_acc= 0.50820 time= 0.01563
Epoch: 0007 train_loss= 0.68974 train_acc= 0.57576 val_loss= 0.69758 val_acc= 0.50820 time= 0.00000
Epoch: 0008 train_loss= 0.68925 train_acc= 0.57576 val_loss= 0.69866 val_acc= 0.50820 time= 0.01563
Epoch: 0009 train_loss= 0.68873 train_acc= 0.57576 val_loss= 0.69987 val_acc= 0.50820 time= 0.01563
Epoch: 0010 train_loss= 0.68872 train_acc= 0.57576 val_loss= 0.70113 val_acc= 0.50820 time= 0.00000
Epoch: 0011 train_loss= 0.68808 train_acc= 0.57576 val_loss= 0.70242 val_acc= 0.50820 time= 0.01563
Epoch: 0012 train_loss= 0.68760 train_acc= 0.57576 val_loss= 0.70370 val_acc= 0.50820 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69936 accuracy= 0.54918 time= 0.00000 
