Epoch: 0001 train_loss= 1.60646 train_acc= 0.24219 val_loss= 1.34687 val_acc= 0.33333 time= 0.64079
Epoch: 0002 train_loss= 1.53942 train_acc= 0.25391 val_loss= 1.41067 val_acc= 0.36667 time= 0.03125
Epoch: 0003 train_loss= 1.70150 train_acc= 0.29492 val_loss= 1.42665 val_acc= 0.36667 time= 0.01562
Epoch: 0004 train_loss= 1.58458 train_acc= 0.26758 val_loss= 1.40280 val_acc= 0.36667 time= 0.03125
Epoch: 0005 train_loss= 1.54682 train_acc= 0.29297 val_loss= 1.39471 val_acc= 0.40000 time= 0.01563
Epoch: 0006 train_loss= 1.89002 train_acc= 0.24414 val_loss= 1.42446 val_acc= 0.40000 time= 0.03125
Epoch: 0007 train_loss= 1.70989 train_acc= 0.28320 val_loss= 1.39504 val_acc= 0.33333 time= 0.01563
Epoch: 0008 train_loss= 1.50078 train_acc= 0.25781 val_loss= 1.39820 val_acc= 0.26667 time= 0.03125
Epoch: 0009 train_loss= 1.53407 train_acc= 0.29492 val_loss= 1.42022 val_acc= 0.25000 time= 0.01563
Epoch: 0010 train_loss= 1.54704 train_acc= 0.25586 val_loss= 1.41075 val_acc= 0.25000 time= 0.03125
Epoch: 0011 train_loss= 1.44667 train_acc= 0.26758 val_loss= 1.39647 val_acc= 0.25000 time= 0.01563
Epoch: 0012 train_loss= 1.42607 train_acc= 0.24219 val_loss= 1.38587 val_acc= 0.26667 time= 0.03125
Epoch: 0013 train_loss= 1.43194 train_acc= 0.27930 val_loss= 1.38475 val_acc= 0.28333 time= 0.01563
Epoch: 0014 train_loss= 1.41976 train_acc= 0.24219 val_loss= 1.38315 val_acc= 0.31667 time= 0.01563
Epoch: 0015 train_loss= 1.38781 train_acc= 0.27930 val_loss= 1.38200 val_acc= 0.33333 time= 0.03125
Epoch: 0016 train_loss= 1.38984 train_acc= 0.25391 val_loss= 1.38085 val_acc= 0.31667 time= 0.01563
Epoch: 0017 train_loss= 1.42814 train_acc= 0.26172 val_loss= 1.37954 val_acc= 0.33333 time= 0.03125
Epoch: 0018 train_loss= 1.39208 train_acc= 0.27344 val_loss= 1.37870 val_acc= 0.35000 time= 0.01562
Epoch: 0019 train_loss= 1.39705 train_acc= 0.26172 val_loss= 1.37818 val_acc= 0.35000 time= 0.03125
Epoch: 0020 train_loss= 1.39574 train_acc= 0.30664 val_loss= 1.37784 val_acc= 0.33333 time= 0.01563
Epoch: 0021 train_loss= 1.39343 train_acc= 0.26562 val_loss= 1.37777 val_acc= 0.33333 time= 0.03125
Epoch: 0022 train_loss= 1.40605 train_acc= 0.25781 val_loss= 1.37770 val_acc= 0.33333 time= 0.03125
Epoch: 0023 train_loss= 1.39647 train_acc= 0.22852 val_loss= 1.37744 val_acc= 0.33333 time= 0.01563
Epoch: 0024 train_loss= 1.39066 train_acc= 0.25391 val_loss= 1.37721 val_acc= 0.31667 time= 0.03125
Epoch: 0025 train_loss= 1.39392 train_acc= 0.25391 val_loss= 1.37689 val_acc= 0.28333 time= 0.03125
Epoch: 0026 train_loss= 1.39024 train_acc= 0.26953 val_loss= 1.37661 val_acc= 0.30000 time= 0.01563
Epoch: 0027 train_loss= 1.39793 train_acc= 0.25000 val_loss= 1.37627 val_acc= 0.30000 time= 0.03125
Epoch: 0028 train_loss= 1.38731 train_acc= 0.28125 val_loss= 1.37595 val_acc= 0.30000 time= 0.01563
Epoch: 0029 train_loss= 1.38623 train_acc= 0.31250 val_loss= 1.37582 val_acc= 0.31667 time= 0.03125
Epoch: 0030 train_loss= 1.38516 train_acc= 0.29883 val_loss= 1.37578 val_acc= 0.31667 time= 0.01563
Epoch: 0031 train_loss= 1.42225 train_acc= 0.29883 val_loss= 1.37602 val_acc= 0.31667 time= 0.01563
Epoch: 0032 train_loss= 1.37952 train_acc= 0.31055 val_loss= 1.37635 val_acc= 0.31667 time= 0.03125
Epoch: 0033 train_loss= 1.39145 train_acc= 0.30469 val_loss= 1.37696 val_acc= 0.33333 time= 0.01713
Early stopping...
Optimization Finished!
Test set results: cost= 1.42104 accuracy= 0.27500 time= 0.01651 
