Epoch: 0001 train_loss= 2.22651 train_acc= 0.26953 val_loss= 1.73524 val_acc= 0.19643 time= 0.62504
Epoch: 0002 train_loss= 2.23862 train_acc= 0.25391 val_loss= 1.71488 val_acc= 0.19643 time= 0.03125
Epoch: 0003 train_loss= 2.22473 train_acc= 0.24609 val_loss= 1.59721 val_acc= 0.19643 time= 0.01563
Epoch: 0004 train_loss= 2.27657 train_acc= 0.23047 val_loss= 1.54012 val_acc= 0.19643 time= 0.03125
Epoch: 0005 train_loss= 2.67101 train_acc= 0.24219 val_loss= 1.47441 val_acc= 0.14286 time= 0.03125
Epoch: 0006 train_loss= 2.19352 train_acc= 0.24805 val_loss= 1.42254 val_acc= 0.25000 time= 0.01563
Epoch: 0007 train_loss= 1.89225 train_acc= 0.27344 val_loss= 1.42346 val_acc= 0.26786 time= 0.03125
Epoch: 0008 train_loss= 2.38484 train_acc= 0.25391 val_loss= 1.40132 val_acc= 0.21429 time= 0.01563
Epoch: 0009 train_loss= 1.72964 train_acc= 0.25977 val_loss= 1.38551 val_acc= 0.32143 time= 0.03125
Epoch: 0010 train_loss= 2.02240 train_acc= 0.22852 val_loss= 1.38676 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.45800 train_acc= 0.25977 val_loss= 1.39217 val_acc= 0.30357 time= 0.03125
Epoch: 0012 train_loss= 1.52198 train_acc= 0.29883 val_loss= 1.39757 val_acc= 0.26786 time= 0.01563
Epoch: 0013 train_loss= 1.98461 train_acc= 0.27344 val_loss= 1.39071 val_acc= 0.30357 time= 0.03125
Epoch: 0014 train_loss= 1.43020 train_acc= 0.28516 val_loss= 1.38662 val_acc= 0.30357 time= 0.01562
Epoch: 0015 train_loss= 1.65917 train_acc= 0.27148 val_loss= 1.38587 val_acc= 0.30357 time= 0.01563
Epoch: 0016 train_loss= 1.39741 train_acc= 0.29688 val_loss= 1.38600 val_acc= 0.30357 time= 0.03125
Epoch: 0017 train_loss= 1.41150 train_acc= 0.29688 val_loss= 1.38612 val_acc= 0.30357 time= 0.02071
Epoch: 0018 train_loss= 1.39306 train_acc= 0.27539 val_loss= 1.38624 val_acc= 0.30357 time= 0.02664
Epoch: 0019 train_loss= 1.38791 train_acc= 0.31055 val_loss= 1.38638 val_acc= 0.30357 time= 0.01562
Epoch: 0020 train_loss= 1.40327 train_acc= 0.30078 val_loss= 1.38649 val_acc= 0.30357 time= 0.01563
Epoch: 0021 train_loss= 1.41173 train_acc= 0.30273 val_loss= 1.38658 val_acc= 0.30357 time= 0.03125
Epoch: 0022 train_loss= 1.42705 train_acc= 0.29102 val_loss= 1.38671 val_acc= 0.30357 time= 0.01563
Epoch: 0023 train_loss= 1.40484 train_acc= 0.29102 val_loss= 1.38688 val_acc= 0.30357 time= 0.03125
Early stopping...
Optimization Finished!
Test set results: cost= 1.38855 accuracy= 0.28319 time= 0.00000 
