Epoch: 0001 train_loss= 2.76689 train_acc= 0.23453 val_loss= 1.55084 val_acc= 0.39286 time= 0.36622
Epoch: 0002 train_loss= 1.92019 train_acc= 0.24756 val_loss= 1.88369 val_acc= 0.39286 time= 0.02100
Epoch: 0003 train_loss= 2.58250 train_acc= 0.32573 val_loss= 1.70935 val_acc= 0.39286 time= 0.00673
Epoch: 0004 train_loss= 3.28389 train_acc= 0.31596 val_loss= 1.87183 val_acc= 0.14286 time= 0.03764
Epoch: 0005 train_loss= 2.65264 train_acc= 0.24430 val_loss= 2.18523 val_acc= 0.14286 time= 0.02201
Epoch: 0006 train_loss= 2.24638 train_acc= 0.26710 val_loss= 2.11341 val_acc= 0.16071 time= 0.02426
Epoch: 0007 train_loss= 2.75600 train_acc= 0.24104 val_loss= 1.76145 val_acc= 0.17857 time= 0.02464
Epoch: 0008 train_loss= 2.52458 train_acc= 0.29967 val_loss= 1.79502 val_acc= 0.19643 time= 0.02171
Epoch: 0009 train_loss= 3.81128 train_acc= 0.25081 val_loss= 1.70599 val_acc= 0.23214 time= 0.02125
Epoch: 0010 train_loss= 2.14973 train_acc= 0.23127 val_loss= 1.77576 val_acc= 0.21429 time= 0.01822
Epoch: 0011 train_loss= 1.66716 train_acc= 0.22476 val_loss= 1.78526 val_acc= 0.25000 time= 0.01562
Epoch: 0012 train_loss= 3.18891 train_acc= 0.24430 val_loss= 1.76397 val_acc= 0.25000 time= 0.01563
Epoch: 0013 train_loss= 4.84465 train_acc= 0.19218 val_loss= 1.74865 val_acc= 0.25000 time= 0.03125
Epoch: 0014 train_loss= 1.99610 train_acc= 0.23453 val_loss= 1.67444 val_acc= 0.25000 time= 0.01563
Epoch: 0015 train_loss= 2.60335 train_acc= 0.26384 val_loss= 1.57507 val_acc= 0.25000 time= 0.01562
Epoch: 0016 train_loss= 1.69446 train_acc= 0.32248 val_loss= 1.50703 val_acc= 0.25000 time= 0.03359
Epoch: 0017 train_loss= 1.70527 train_acc= 0.18241 val_loss= 1.44433 val_acc= 0.25000 time= 0.02326
Epoch: 0018 train_loss= 1.45179 train_acc= 0.28664 val_loss= 1.38646 val_acc= 0.30357 time= 0.02234
Epoch: 0019 train_loss= 1.83343 train_acc= 0.27687 val_loss= 1.34947 val_acc= 0.41071 time= 0.02408
Epoch: 0020 train_loss= 1.37654 train_acc= 0.28990 val_loss= 1.34277 val_acc= 0.39286 time= 0.02223
Epoch: 0021 train_loss= 1.74634 train_acc= 0.32899 val_loss= 1.34234 val_acc= 0.39286 time= 0.01707
Epoch: 0022 train_loss= 1.95829 train_acc= 0.22801 val_loss= 1.34215 val_acc= 0.39286 time= 0.01568
Epoch: 0023 train_loss= 2.20738 train_acc= 0.34202 val_loss= 1.34351 val_acc= 0.39286 time= 0.01563
Epoch: 0024 train_loss= 3.30003 train_acc= 0.30945 val_loss= 1.35915 val_acc= 0.41071 time= 0.03408
Epoch: 0025 train_loss= 1.80771 train_acc= 0.26059 val_loss= 1.38246 val_acc= 0.32143 time= 0.02000
Epoch: 0026 train_loss= 1.50018 train_acc= 0.32573 val_loss= 1.38349 val_acc= 0.30357 time= 0.02110
Early stopping...
Optimization Finished!
Test set results: cost= 1.40102 accuracy= 0.29204 time= 0.01000 
