Epoch: 0001 train_loss= 0.70118 train_acc= 0.45091 val_loss= 0.69916 val_acc= 0.49180 time= 0.25003
Epoch: 0002 train_loss= 0.69743 train_acc= 0.55273 val_loss= 0.69821 val_acc= 0.49180 time= 0.01562
Epoch: 0003 train_loss= 0.69466 train_acc= 0.55273 val_loss= 0.69820 val_acc= 0.49180 time= 0.00000
Epoch: 0004 train_loss= 0.69266 train_acc= 0.55273 val_loss= 0.69892 val_acc= 0.49180 time= 0.01562
Epoch: 0005 train_loss= 0.69107 train_acc= 0.55273 val_loss= 0.70022 val_acc= 0.49180 time= 0.01563
Epoch: 0006 train_loss= 0.69047 train_acc= 0.55273 val_loss= 0.70178 val_acc= 0.49180 time= 0.01563
Epoch: 0007 train_loss= 0.69013 train_acc= 0.55273 val_loss= 0.70339 val_acc= 0.49180 time= 0.00000
Epoch: 0008 train_loss= 0.69012 train_acc= 0.55273 val_loss= 0.70485 val_acc= 0.49180 time= 0.01563
Epoch: 0009 train_loss= 0.68994 train_acc= 0.55273 val_loss= 0.70600 val_acc= 0.49180 time= 0.01563
Epoch: 0010 train_loss= 0.68959 train_acc= 0.55273 val_loss= 0.70672 val_acc= 0.49180 time= 0.00000
Epoch: 0011 train_loss= 0.68951 train_acc= 0.55273 val_loss= 0.70695 val_acc= 0.49180 time= 0.01563
Epoch: 0012 train_loss= 0.68901 train_acc= 0.55273 val_loss= 0.70680 val_acc= 0.49180 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.70234 accuracy= 0.48361 time= 0.00000 
