Epoch: 0001 train_loss= 2.08712 train_acc= 0.15472 val_loss= 2.08352 val_acc= 0.31034 time= 0.25865
Epoch: 0002 train_loss= 2.08460 train_acc= 0.12075 val_loss= 2.08036 val_acc= 0.31034 time= 0.01563
Epoch: 0003 train_loss= 2.08234 train_acc= 0.12830 val_loss= 2.07713 val_acc= 0.31034 time= 0.00000
Epoch: 0004 train_loss= 2.08031 train_acc= 0.12830 val_loss= 2.07392 val_acc= 0.31034 time= 0.01563
Epoch: 0005 train_loss= 2.07883 train_acc= 0.13208 val_loss= 2.07082 val_acc= 0.31034 time= 0.01563
Epoch: 0006 train_loss= 2.07678 train_acc= 0.12830 val_loss= 2.06766 val_acc= 0.31034 time= 0.00000
Epoch: 0007 train_loss= 2.07615 train_acc= 0.13962 val_loss= 2.06466 val_acc= 0.17241 time= 0.01563
Epoch: 0008 train_loss= 2.07526 train_acc= 0.13585 val_loss= 2.06180 val_acc= 0.17241 time= 0.00000
Epoch: 0009 train_loss= 2.07347 train_acc= 0.15472 val_loss= 2.05898 val_acc= 0.17241 time= 0.01563
Epoch: 0010 train_loss= 2.07277 train_acc= 0.15472 val_loss= 2.05642 val_acc= 0.17241 time= 0.01563
Epoch: 0011 train_loss= 2.07219 train_acc= 0.15472 val_loss= 2.05394 val_acc= 0.17241 time= 0.00000
Epoch: 0012 train_loss= 2.07215 train_acc= 0.15472 val_loss= 2.05190 val_acc= 0.17241 time= 0.01563
Epoch: 0013 train_loss= 2.07077 train_acc= 0.15472 val_loss= 2.05028 val_acc= 0.17241 time= 0.00000
Epoch: 0014 train_loss= 2.06964 train_acc= 0.15472 val_loss= 2.04893 val_acc= 0.17241 time= 0.01563
Epoch: 0015 train_loss= 2.06961 train_acc= 0.15472 val_loss= 2.04787 val_acc= 0.17241 time= 0.01563
Epoch: 0016 train_loss= 2.06861 train_acc= 0.15472 val_loss= 2.04717 val_acc= 0.17241 time= 0.00000
Epoch: 0017 train_loss= 2.06774 train_acc= 0.15472 val_loss= 2.04675 val_acc= 0.17241 time= 0.01563
Epoch: 0018 train_loss= 2.06668 train_acc= 0.15472 val_loss= 2.04659 val_acc= 0.17241 time= 0.00000
Epoch: 0019 train_loss= 2.06725 train_acc= 0.15472 val_loss= 2.04665 val_acc= 0.17241 time= 0.01563
Epoch: 0020 train_loss= 2.06587 train_acc= 0.15472 val_loss= 2.04666 val_acc= 0.17241 time= 0.01563
Epoch: 0021 train_loss= 2.06560 train_acc= 0.15472 val_loss= 2.04684 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.06458 train_acc= 0.15472 val_loss= 2.04704 val_acc= 0.17241 time= 0.01563
Epoch: 0023 train_loss= 2.06450 train_acc= 0.15472 val_loss= 2.04724 val_acc= 0.17241 time= 0.00000
Epoch: 0024 train_loss= 2.06415 train_acc= 0.15472 val_loss= 2.04741 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.08432 accuracy= 0.16949 time= 0.00000 
