Epoch: 0001 train_loss= 0.69882 train_acc= 0.54286 val_loss= 0.69910 val_acc= 0.49180 time= 0.31252
Epoch: 0002 train_loss= 0.69764 train_acc= 0.53247 val_loss= 0.69905 val_acc= 0.49180 time= 0.01562
Epoch: 0003 train_loss= 0.69666 train_acc= 0.53247 val_loss= 0.69907 val_acc= 0.49180 time= 0.00000
Epoch: 0004 train_loss= 0.69584 train_acc= 0.53247 val_loss= 0.69921 val_acc= 0.49180 time= 0.01562
Epoch: 0005 train_loss= 0.69515 train_acc= 0.53247 val_loss= 0.69955 val_acc= 0.49180 time= 0.02468
Epoch: 0006 train_loss= 0.69465 train_acc= 0.53247 val_loss= 0.70003 val_acc= 0.49180 time= 0.00000
Epoch: 0007 train_loss= 0.69403 train_acc= 0.53247 val_loss= 0.70064 val_acc= 0.49180 time= 0.01563
Epoch: 0008 train_loss= 0.69390 train_acc= 0.53247 val_loss= 0.70128 val_acc= 0.49180 time= 0.01563
Epoch: 0009 train_loss= 0.69340 train_acc= 0.53247 val_loss= 0.70188 val_acc= 0.49180 time= 0.01563
Epoch: 0010 train_loss= 0.69321 train_acc= 0.53247 val_loss= 0.70231 val_acc= 0.49180 time= 0.01563
Epoch: 0011 train_loss= 0.69313 train_acc= 0.53247 val_loss= 0.70249 val_acc= 0.49180 time= 0.01563
Epoch: 0012 train_loss= 0.69234 train_acc= 0.53247 val_loss= 0.70243 val_acc= 0.49180 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69644 accuracy= 0.53279 time= 0.00000 
