Epoch: 0001 train_loss= 0.69974 train_acc= 0.42424 val_loss= 0.70141 val_acc= 0.40984 time= 0.09375
Epoch: 0002 train_loss= 0.69770 train_acc= 0.54545 val_loss= 0.70383 val_acc= 0.40984 time= 0.01564
Epoch: 0003 train_loss= 0.69620 train_acc= 0.54545 val_loss= 0.70647 val_acc= 0.40984 time= 0.01562
Epoch: 0004 train_loss= 0.69463 train_acc= 0.54545 val_loss= 0.70925 val_acc= 0.40984 time= 0.01563
Epoch: 0005 train_loss= 0.69455 train_acc= 0.54545 val_loss= 0.71158 val_acc= 0.40984 time= 0.00000
Epoch: 0006 train_loss= 0.69372 train_acc= 0.54545 val_loss= 0.71387 val_acc= 0.40984 time= 0.01563
Epoch: 0007 train_loss= 0.69247 train_acc= 0.54545 val_loss= 0.71629 val_acc= 0.40984 time= 0.01563
Epoch: 0008 train_loss= 0.69233 train_acc= 0.54545 val_loss= 0.71880 val_acc= 0.40984 time= 0.01563
Epoch: 0009 train_loss= 0.69149 train_acc= 0.54545 val_loss= 0.72127 val_acc= 0.40984 time= 0.01563
Epoch: 0010 train_loss= 0.69125 train_acc= 0.54545 val_loss= 0.72362 val_acc= 0.40984 time= 0.00000
Epoch: 0011 train_loss= 0.69201 train_acc= 0.54545 val_loss= 0.72549 val_acc= 0.40984 time= 0.01563
Epoch: 0012 train_loss= 0.69120 train_acc= 0.54545 val_loss= 0.72683 val_acc= 0.40984 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.68717 accuracy= 0.54918 time= 0.00000 
