Epoch: 0001 train_loss= 0.93154 train_acc= 0.53273 val_loss= 0.74211 val_acc= 0.55738 time= 0.17188
Epoch: 0002 train_loss= 1.27026 train_acc= 0.47273 val_loss= 0.73772 val_acc= 0.57377 time= 0.00000
Epoch: 0003 train_loss= 1.23555 train_acc= 0.48727 val_loss= 0.71900 val_acc= 0.55738 time= 0.01563
Epoch: 0004 train_loss= 1.55395 train_acc= 0.51091 val_loss= 0.78905 val_acc= 0.49180 time= 0.01563
Epoch: 0005 train_loss= 1.01346 train_acc= 0.49273 val_loss= 0.98436 val_acc= 0.42623 time= 0.01563
Epoch: 0006 train_loss= 0.80539 train_acc= 0.49273 val_loss= 1.23625 val_acc= 0.39344 time= 0.01563
Epoch: 0007 train_loss= 0.79999 train_acc= 0.51273 val_loss= 1.40376 val_acc= 0.39344 time= 0.00000
Epoch: 0008 train_loss= 0.92182 train_acc= 0.49273 val_loss= 1.47499 val_acc= 0.39344 time= 0.01563
Epoch: 0009 train_loss= 1.19285 train_acc= 0.51091 val_loss= 1.43112 val_acc= 0.40984 time= 0.01563
Epoch: 0010 train_loss= 0.94070 train_acc= 0.48909 val_loss= 1.34759 val_acc= 0.40984 time= 0.01563
Epoch: 0011 train_loss= 0.91901 train_acc= 0.51636 val_loss= 1.24417 val_acc= 0.44262 time= 0.01563
Epoch: 0012 train_loss= 0.85802 train_acc= 0.52727 val_loss= 1.14482 val_acc= 0.40984 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.11014 accuracy= 0.45902 time= 0.00000 
