Epoch: 0001 train_loss= 1.43043 train_acc= 0.24756 val_loss= 1.43458 val_acc= 0.17857 time= 0.22031
Epoch: 0002 train_loss= 1.42068 train_acc= 0.24756 val_loss= 1.42702 val_acc= 0.17857 time= 0.01124
Epoch: 0003 train_loss= 1.41548 train_acc= 0.24430 val_loss= 1.42022 val_acc= 0.17857 time= 0.01309
Epoch: 0004 train_loss= 1.40801 train_acc= 0.24756 val_loss= 1.41414 val_acc= 0.17857 time= 0.00800
Epoch: 0005 train_loss= 1.40172 train_acc= 0.24430 val_loss= 1.40877 val_acc= 0.17857 time= 0.00600
Epoch: 0006 train_loss= 1.39961 train_acc= 0.24430 val_loss= 1.40404 val_acc= 0.17857 time= 0.00720
Epoch: 0007 train_loss= 1.39585 train_acc= 0.25081 val_loss= 1.39991 val_acc= 0.17857 time= 0.00700
Epoch: 0008 train_loss= 1.39047 train_acc= 0.22476 val_loss= 1.39632 val_acc= 0.26786 time= 0.00600
Epoch: 0009 train_loss= 1.38571 train_acc= 0.31270 val_loss= 1.39324 val_acc= 0.26786 time= 0.00713
Epoch: 0010 train_loss= 1.38364 train_acc= 0.29642 val_loss= 1.39059 val_acc= 0.26786 time= 0.00705
Epoch: 0011 train_loss= 1.38432 train_acc= 0.32248 val_loss= 1.38835 val_acc= 0.26786 time= 0.00600
Epoch: 0012 train_loss= 1.37865 train_acc= 0.32248 val_loss= 1.38647 val_acc= 0.26786 time= 0.00700
Epoch: 0013 train_loss= 1.37942 train_acc= 0.31922 val_loss= 1.38490 val_acc= 0.26786 time= 0.00700
Epoch: 0014 train_loss= 1.37655 train_acc= 0.31596 val_loss= 1.38363 val_acc= 0.26786 time= 0.00700
Epoch: 0015 train_loss= 1.37638 train_acc= 0.31596 val_loss= 1.38259 val_acc= 0.26786 time= 0.00700
Epoch: 0016 train_loss= 1.37694 train_acc= 0.31596 val_loss= 1.38169 val_acc= 0.26786 time= 0.00600
Epoch: 0017 train_loss= 1.37664 train_acc= 0.31596 val_loss= 1.38097 val_acc= 0.26786 time= 0.00600
Epoch: 0018 train_loss= 1.37266 train_acc= 0.31596 val_loss= 1.38025 val_acc= 0.26786 time= 0.00800
Epoch: 0019 train_loss= 1.37692 train_acc= 0.31922 val_loss= 1.37952 val_acc= 0.26786 time= 0.00600
Epoch: 0020 train_loss= 1.37433 train_acc= 0.31596 val_loss= 1.37888 val_acc= 0.26786 time= 0.00600
Epoch: 0021 train_loss= 1.37475 train_acc= 0.31270 val_loss= 1.37827 val_acc= 0.26786 time= 0.00700
Epoch: 0022 train_loss= 1.37589 train_acc= 0.31596 val_loss= 1.37770 val_acc= 0.26786 time= 0.00700
Epoch: 0023 train_loss= 1.37831 train_acc= 0.31596 val_loss= 1.37718 val_acc= 0.26786 time= 0.00600
Epoch: 0024 train_loss= 1.37523 train_acc= 0.31596 val_loss= 1.37672 val_acc= 0.26786 time= 0.00597
Epoch: 0025 train_loss= 1.37601 train_acc= 0.31596 val_loss= 1.37630 val_acc= 0.26786 time= 0.00631
Epoch: 0026 train_loss= 1.37336 train_acc= 0.31596 val_loss= 1.37593 val_acc= 0.26786 time= 0.00600
Epoch: 0027 train_loss= 1.37342 train_acc= 0.31922 val_loss= 1.37564 val_acc= 0.26786 time= 0.00600
Epoch: 0028 train_loss= 1.37348 train_acc= 0.31596 val_loss= 1.37543 val_acc= 0.26786 time= 0.00700
Epoch: 0029 train_loss= 1.37626 train_acc= 0.31596 val_loss= 1.37529 val_acc= 0.26786 time= 0.00700
Epoch: 0030 train_loss= 1.37938 train_acc= 0.31596 val_loss= 1.37519 val_acc= 0.26786 time= 0.00600
Epoch: 0031 train_loss= 1.37554 train_acc= 0.31270 val_loss= 1.37528 val_acc= 0.26786 time= 0.00607
Epoch: 0032 train_loss= 1.37418 train_acc= 0.31596 val_loss= 1.37557 val_acc= 0.26786 time= 0.00700
Epoch: 0033 train_loss= 1.37309 train_acc= 0.31596 val_loss= 1.37596 val_acc= 0.26786 time= 0.00700
Early stopping...
Optimization Finished!
Test set results: cost= 1.37611 accuracy= 0.29204 time= 0.00300 
