Epoch: 0001 train_loss= 0.69813 train_acc= 0.50364 val_loss= 0.70006 val_acc= 0.50820 time= 0.34336
Epoch: 0002 train_loss= 0.69784 train_acc= 0.50364 val_loss= 0.70022 val_acc= 0.50820 time= 0.01562
Epoch: 0003 train_loss= 0.69706 train_acc= 0.50727 val_loss= 0.70044 val_acc= 0.50820 time= 0.00000
Epoch: 0004 train_loss= 0.69752 train_acc= 0.50545 val_loss= 0.69995 val_acc= 0.50820 time= 0.01563
Epoch: 0005 train_loss= 0.69712 train_acc= 0.50364 val_loss= 0.69929 val_acc= 0.50820 time= 0.01563
Epoch: 0006 train_loss= 0.69652 train_acc= 0.50364 val_loss= 0.69854 val_acc= 0.50820 time= 0.00000
Epoch: 0007 train_loss= 0.69580 train_acc= 0.50545 val_loss= 0.69796 val_acc= 0.50820 time= 0.01563
Epoch: 0008 train_loss= 0.69589 train_acc= 0.50364 val_loss= 0.69743 val_acc= 0.50820 time= 0.01563
Epoch: 0009 train_loss= 0.69518 train_acc= 0.51091 val_loss= 0.69707 val_acc= 0.50820 time= 0.00000
Epoch: 0010 train_loss= 0.69548 train_acc= 0.49818 val_loss= 0.69673 val_acc= 0.50820 time= 0.01563
Epoch: 0011 train_loss= 0.69490 train_acc= 0.50545 val_loss= 0.69653 val_acc= 0.50820 time= 0.01563
Epoch: 0012 train_loss= 0.69488 train_acc= 0.50182 val_loss= 0.69635 val_acc= 0.50820 time= 0.00000
Epoch: 0013 train_loss= 0.69486 train_acc= 0.50364 val_loss= 0.69618 val_acc= 0.50820 time= 0.01563
Epoch: 0014 train_loss= 0.69447 train_acc= 0.50182 val_loss= 0.69606 val_acc= 0.50820 time= 0.01563
Epoch: 0015 train_loss= 0.69393 train_acc= 0.50364 val_loss= 0.69607 val_acc= 0.50820 time= 0.00000
Epoch: 0016 train_loss= 0.69448 train_acc= 0.50182 val_loss= 0.69601 val_acc= 0.50820 time= 0.01563
Epoch: 0017 train_loss= 0.69367 train_acc= 0.50364 val_loss= 0.69602 val_acc= 0.50820 time= 0.01563
Epoch: 0018 train_loss= 0.69372 train_acc= 0.50364 val_loss= 0.69601 val_acc= 0.50820 time= 0.00000
Epoch: 0019 train_loss= 0.69362 train_acc= 0.50182 val_loss= 0.69597 val_acc= 0.50820 time= 0.01562
Epoch: 0020 train_loss= 0.69346 train_acc= 0.50364 val_loss= 0.69591 val_acc= 0.50820 time= 0.01563
Epoch: 0021 train_loss= 0.69351 train_acc= 0.50364 val_loss= 0.69579 val_acc= 0.50820 time= 0.00000
Epoch: 0022 train_loss= 0.69321 train_acc= 0.50364 val_loss= 0.69567 val_acc= 0.50820 time= 0.01563
Epoch: 0023 train_loss= 0.69322 train_acc= 0.50364 val_loss= 0.69556 val_acc= 0.50820 time= 0.01563
Epoch: 0024 train_loss= 0.69322 train_acc= 0.50545 val_loss= 0.69545 val_acc= 0.50820 time= 0.00000
Epoch: 0025 train_loss= 0.69306 train_acc= 0.50545 val_loss= 0.69534 val_acc= 0.50820 time= 0.01563
Epoch: 0026 train_loss= 0.69310 train_acc= 0.50364 val_loss= 0.69529 val_acc= 0.50820 time= 0.01563
Epoch: 0027 train_loss= 0.69334 train_acc= 0.50364 val_loss= 0.69517 val_acc= 0.50820 time= 0.01563
Epoch: 0028 train_loss= 0.69308 train_acc= 0.50364 val_loss= 0.69503 val_acc= 0.50820 time= 0.00000
Epoch: 0029 train_loss= 0.69305 train_acc= 0.50182 val_loss= 0.69498 val_acc= 0.50820 time= 0.01562
Epoch: 0030 train_loss= 0.69291 train_acc= 0.50182 val_loss= 0.69506 val_acc= 0.50820 time= 0.01563
Epoch: 0031 train_loss= 0.69338 train_acc= 0.50364 val_loss= 0.69509 val_acc= 0.50820 time= 0.00000
Epoch: 0032 train_loss= 0.69274 train_acc= 0.50909 val_loss= 0.69518 val_acc= 0.50820 time= 0.01562
Epoch: 0033 train_loss= 0.69285 train_acc= 0.50364 val_loss= 0.69528 val_acc= 0.50820 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69241 accuracy= 0.52459 time= 0.00000 
