Epoch: 0001 train_loss= 1.39263 train_acc= 0.22656 val_loss= 1.40095 val_acc= 0.23214 time= 0.26564
Epoch: 0002 train_loss= 1.39096 train_acc= 0.23047 val_loss= 1.40056 val_acc= 0.16071 time= 0.01563
Epoch: 0003 train_loss= 1.38971 train_acc= 0.31055 val_loss= 1.40039 val_acc= 0.16071 time= 0.01563
Epoch: 0004 train_loss= 1.38810 train_acc= 0.31055 val_loss= 1.40119 val_acc= 0.16071 time= 0.01562
Epoch: 0005 train_loss= 1.38698 train_acc= 0.31055 val_loss= 1.40191 val_acc= 0.16071 time= 0.03125
Epoch: 0006 train_loss= 1.38579 train_acc= 0.31055 val_loss= 1.40252 val_acc= 0.16071 time= 0.01563
Epoch: 0007 train_loss= 1.38476 train_acc= 0.31055 val_loss= 1.40316 val_acc= 0.16071 time= 0.01563
Epoch: 0008 train_loss= 1.38369 train_acc= 0.31055 val_loss= 1.40381 val_acc= 0.16071 time= 0.01563
Epoch: 0009 train_loss= 1.38254 train_acc= 0.31055 val_loss= 1.40447 val_acc= 0.16071 time= 0.01563
Epoch: 0010 train_loss= 1.38147 train_acc= 0.31055 val_loss= 1.40503 val_acc= 0.16071 time= 0.01563
Epoch: 0011 train_loss= 1.38034 train_acc= 0.31055 val_loss= 1.40545 val_acc= 0.16071 time= 0.01563
Epoch: 0012 train_loss= 1.37927 train_acc= 0.31055 val_loss= 1.40572 val_acc= 0.16071 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38443 accuracy= 0.28319 time= 0.00000 
