Epoch: 0001 train_loss= 1.39145 train_acc= 0.33550 val_loss= 1.41586 val_acc= 0.25000 time= 0.27986
Epoch: 0002 train_loss= 1.38481 train_acc= 0.33225 val_loss= 1.41215 val_acc= 0.25000 time= 0.01373
Epoch: 0003 train_loss= 1.38290 train_acc= 0.33225 val_loss= 1.40884 val_acc= 0.25000 time= 0.01033
Epoch: 0004 train_loss= 1.37906 train_acc= 0.33225 val_loss= 1.40596 val_acc= 0.25000 time= 0.01163
Epoch: 0005 train_loss= 1.37709 train_acc= 0.33225 val_loss= 1.40351 val_acc= 0.25000 time= 0.00975
Epoch: 0006 train_loss= 1.37569 train_acc= 0.33225 val_loss= 1.40144 val_acc= 0.25000 time= 0.01017
Epoch: 0007 train_loss= 1.37568 train_acc= 0.33225 val_loss= 1.39965 val_acc= 0.25000 time= 0.01014
Epoch: 0008 train_loss= 1.37480 train_acc= 0.33225 val_loss= 1.39811 val_acc= 0.25000 time= 0.01115
Epoch: 0009 train_loss= 1.37297 train_acc= 0.33225 val_loss= 1.39662 val_acc= 0.25000 time= 0.00994
Epoch: 0010 train_loss= 1.36806 train_acc= 0.33225 val_loss= 1.39546 val_acc= 0.25000 time= 0.00975
Epoch: 0011 train_loss= 1.37136 train_acc= 0.33225 val_loss= 1.39442 val_acc= 0.25000 time= 0.01182
Epoch: 0012 train_loss= 1.36797 train_acc= 0.33225 val_loss= 1.39359 val_acc= 0.25000 time= 0.01169
Epoch: 0013 train_loss= 1.36832 train_acc= 0.33225 val_loss= 1.39295 val_acc= 0.25000 time= 0.01012
Epoch: 0014 train_loss= 1.36695 train_acc= 0.33225 val_loss= 1.39247 val_acc= 0.25000 time= 0.01116
Epoch: 0015 train_loss= 1.36948 train_acc= 0.33225 val_loss= 1.39209 val_acc= 0.25000 time= 0.00989
Epoch: 0016 train_loss= 1.37100 train_acc= 0.33225 val_loss= 1.39177 val_acc= 0.25000 time= 0.00962
Epoch: 0017 train_loss= 1.36690 train_acc= 0.33225 val_loss= 1.39153 val_acc= 0.25000 time= 0.00900
Epoch: 0018 train_loss= 1.37087 train_acc= 0.33225 val_loss= 1.39137 val_acc= 0.25000 time= 0.01000
Epoch: 0019 train_loss= 1.36918 train_acc= 0.33225 val_loss= 1.39122 val_acc= 0.25000 time= 0.01299
Epoch: 0020 train_loss= 1.36955 train_acc= 0.33225 val_loss= 1.39104 val_acc= 0.25000 time= 0.01222
Epoch: 0021 train_loss= 1.36780 train_acc= 0.33225 val_loss= 1.39094 val_acc= 0.25000 time= 0.01008
Epoch: 0022 train_loss= 1.37066 train_acc= 0.33225 val_loss= 1.39089 val_acc= 0.25000 time= 0.01319
Epoch: 0023 train_loss= 1.36827 train_acc= 0.33225 val_loss= 1.39079 val_acc= 0.25000 time= 0.01099
Epoch: 0024 train_loss= 1.36836 train_acc= 0.33225 val_loss= 1.39039 val_acc= 0.25000 time= 0.01318
Epoch: 0025 train_loss= 1.36831 train_acc= 0.33225 val_loss= 1.39039 val_acc= 0.25000 time= 0.01001
Epoch: 0026 train_loss= 1.36924 train_acc= 0.33225 val_loss= 1.39033 val_acc= 0.25000 time= 0.01227
Epoch: 0027 train_loss= 1.36945 train_acc= 0.33225 val_loss= 1.39032 val_acc= 0.25000 time= 0.01014
Epoch: 0028 train_loss= 1.36577 train_acc= 0.33225 val_loss= 1.39023 val_acc= 0.25000 time= 0.01210
Epoch: 0029 train_loss= 1.36658 train_acc= 0.33225 val_loss= 1.39034 val_acc= 0.25000 time= 0.00928
Epoch: 0030 train_loss= 1.36692 train_acc= 0.33225 val_loss= 1.39047 val_acc= 0.25000 time= 0.01101
Epoch: 0031 train_loss= 1.36639 train_acc= 0.33225 val_loss= 1.39072 val_acc= 0.25000 time= 0.00996
Early stopping...
Optimization Finished!
Test set results: cost= 1.33962 accuracy= 0.36283 time= 0.00400 
