Epoch: 0001 train_loss= 1.39359 train_acc= 0.18750 val_loss= 1.39046 val_acc= 0.32143 time= 0.34376
Epoch: 0002 train_loss= 1.39089 train_acc= 0.28320 val_loss= 1.38947 val_acc= 0.19643 time= 0.01563
Epoch: 0003 train_loss= 1.38852 train_acc= 0.27734 val_loss= 1.38813 val_acc= 0.19643 time= 0.01563
Epoch: 0004 train_loss= 1.38631 train_acc= 0.27539 val_loss= 1.38638 val_acc= 0.19643 time= 0.01563
Epoch: 0005 train_loss= 1.38526 train_acc= 0.27539 val_loss= 1.38459 val_acc= 0.19643 time= 0.01563
Epoch: 0006 train_loss= 1.38369 train_acc= 0.27734 val_loss= 1.38278 val_acc= 0.19643 time= 0.01563
Epoch: 0007 train_loss= 1.38228 train_acc= 0.27539 val_loss= 1.38101 val_acc= 0.19643 time= 0.01563
Epoch: 0008 train_loss= 1.38138 train_acc= 0.27344 val_loss= 1.37925 val_acc= 0.32143 time= 0.01563
Epoch: 0009 train_loss= 1.37978 train_acc= 0.27734 val_loss= 1.37754 val_acc= 0.32143 time= 0.01563
Epoch: 0010 train_loss= 1.37853 train_acc= 0.28320 val_loss= 1.37593 val_acc= 0.32143 time= 0.01563
Epoch: 0011 train_loss= 1.37786 train_acc= 0.30273 val_loss= 1.37436 val_acc= 0.32143 time= 0.01563
Epoch: 0012 train_loss= 1.37742 train_acc= 0.29688 val_loss= 1.37289 val_acc= 0.32143 time= 0.01562
Epoch: 0013 train_loss= 1.37681 train_acc= 0.29492 val_loss= 1.37160 val_acc= 0.32143 time= 0.00000
Epoch: 0014 train_loss= 1.37529 train_acc= 0.29492 val_loss= 1.37044 val_acc= 0.32143 time= 0.01563
Epoch: 0015 train_loss= 1.37712 train_acc= 0.29492 val_loss= 1.36946 val_acc= 0.32143 time= 0.01563
Epoch: 0016 train_loss= 1.37703 train_acc= 0.29688 val_loss= 1.36876 val_acc= 0.32143 time= 0.01563
Epoch: 0017 train_loss= 1.37619 train_acc= 0.29688 val_loss= 1.36824 val_acc= 0.32143 time= 0.02053
Epoch: 0018 train_loss= 1.37648 train_acc= 0.29688 val_loss= 1.36782 val_acc= 0.32143 time= 0.01100
Epoch: 0019 train_loss= 1.37610 train_acc= 0.29688 val_loss= 1.36751 val_acc= 0.32143 time= 0.01563
Epoch: 0020 train_loss= 1.37646 train_acc= 0.29688 val_loss= 1.36738 val_acc= 0.32143 time= 0.00000
Epoch: 0021 train_loss= 1.37746 train_acc= 0.29688 val_loss= 1.36732 val_acc= 0.32143 time= 0.01563
Epoch: 0022 train_loss= 1.37618 train_acc= 0.29883 val_loss= 1.36739 val_acc= 0.32143 time= 0.01563
Epoch: 0023 train_loss= 1.37586 train_acc= 0.29688 val_loss= 1.36754 val_acc= 0.32143 time= 0.01563
Epoch: 0024 train_loss= 1.37659 train_acc= 0.29297 val_loss= 1.36773 val_acc= 0.32143 time= 0.01563
Epoch: 0025 train_loss= 1.37613 train_acc= 0.29688 val_loss= 1.36796 val_acc= 0.32143 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.39704 accuracy= 0.28319 time= 0.00000 
