Epoch: 0001 train_loss= 2.07941 train_acc= 0.15849 val_loss= 2.09096 val_acc= 0.10345 time= 0.45878
Epoch: 0002 train_loss= 2.07557 train_acc= 0.15849 val_loss= 2.08411 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.07293 train_acc= 0.15849 val_loss= 2.07699 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.07008 train_acc= 0.15849 val_loss= 2.07100 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.06885 train_acc= 0.15849 val_loss= 2.06526 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.06679 train_acc= 0.15849 val_loss= 2.05947 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.06385 train_acc= 0.15849 val_loss= 2.05381 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.06544 train_acc= 0.16226 val_loss= 2.04833 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.06388 train_acc= 0.16604 val_loss= 2.04329 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.06004 train_acc= 0.17736 val_loss= 2.03836 val_acc= 0.24138 time= 0.00000
Epoch: 0011 train_loss= 2.05856 train_acc= 0.14340 val_loss= 2.03353 val_acc= 0.24138 time= 0.01562
Epoch: 0012 train_loss= 2.05908 train_acc= 0.15094 val_loss= 2.02897 val_acc= 0.24138 time= 0.00000
Epoch: 0013 train_loss= 2.05834 train_acc= 0.15849 val_loss= 2.02507 val_acc= 0.24138 time= 0.00000
Epoch: 0014 train_loss= 2.05915 train_acc= 0.15472 val_loss= 2.02127 val_acc= 0.24138 time= 0.01563
Epoch: 0015 train_loss= 2.05771 train_acc= 0.15472 val_loss= 2.01801 val_acc= 0.24138 time= 0.00000
Epoch: 0016 train_loss= 2.06077 train_acc= 0.15094 val_loss= 2.01530 val_acc= 0.24138 time= 0.00000
Epoch: 0017 train_loss= 2.05978 train_acc= 0.15472 val_loss= 2.01325 val_acc= 0.24138 time= 0.01563
Epoch: 0018 train_loss= 2.05823 train_acc= 0.16226 val_loss= 2.01169 val_acc= 0.24138 time= 0.00000
Epoch: 0019 train_loss= 2.05697 train_acc= 0.15849 val_loss= 2.01049 val_acc= 0.24138 time= 0.00000
Epoch: 0020 train_loss= 2.05958 train_acc= 0.15849 val_loss= 2.01012 val_acc= 0.24138 time= 0.01563
Epoch: 0021 train_loss= 2.05930 train_acc= 0.16604 val_loss= 2.01021 val_acc= 0.24138 time= 0.00000
Epoch: 0022 train_loss= 2.06002 train_acc= 0.15849 val_loss= 2.01076 val_acc= 0.24138 time= 0.00000
Epoch: 0023 train_loss= 2.05615 train_acc= 0.15472 val_loss= 2.01153 val_acc= 0.24138 time= 0.01563
Epoch: 0024 train_loss= 2.06057 train_acc= 0.15472 val_loss= 2.01245 val_acc= 0.24138 time= 0.00000
Epoch: 0025 train_loss= 2.05847 train_acc= 0.15849 val_loss= 2.01360 val_acc= 0.24138 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.03918 accuracy= 0.16949 time= 0.01563 
