Epoch: 0001 train_loss= 0.70127 train_acc= 0.49091 val_loss= 0.69821 val_acc= 0.49180 time= 0.20314
Epoch: 0002 train_loss= 0.69844 train_acc= 0.49394 val_loss= 0.69812 val_acc= 0.47541 time= 0.00000
Epoch: 0003 train_loss= 0.69821 train_acc= 0.47879 val_loss= 0.69858 val_acc= 0.49180 time= 0.01562
Epoch: 0004 train_loss= 0.69552 train_acc= 0.50909 val_loss= 0.69948 val_acc= 0.49180 time= 0.00000
Epoch: 0005 train_loss= 0.69466 train_acc= 0.51818 val_loss= 0.70076 val_acc= 0.49180 time= 0.00000
Epoch: 0006 train_loss= 0.69491 train_acc= 0.51212 val_loss= 0.70224 val_acc= 0.49180 time= 0.01563
Epoch: 0007 train_loss= 0.69293 train_acc= 0.52424 val_loss= 0.70369 val_acc= 0.49180 time= 0.00000
Epoch: 0008 train_loss= 0.69438 train_acc= 0.50606 val_loss= 0.70480 val_acc= 0.49180 time= 0.01563
Epoch: 0009 train_loss= 0.69438 train_acc= 0.50909 val_loss= 0.70548 val_acc= 0.49180 time= 0.00000
Epoch: 0010 train_loss= 0.69436 train_acc= 0.52121 val_loss= 0.70566 val_acc= 0.49180 time= 0.00000
Epoch: 0011 train_loss= 0.69197 train_acc= 0.52121 val_loss= 0.70536 val_acc= 0.49180 time= 0.01563
Epoch: 0012 train_loss= 0.69414 train_acc= 0.51818 val_loss= 0.70469 val_acc= 0.49180 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69105 accuracy= 0.54918 time= 0.00000 
