Epoch: 0001 train_loss= 1.19423 train_acc= 0.52727 val_loss= 1.60393 val_acc= 0.44262 time= 0.06250
Epoch: 0002 train_loss= 1.34473 train_acc= 0.49697 val_loss= 1.62377 val_acc= 0.44262 time= 0.01563
Epoch: 0003 train_loss= 1.89339 train_acc= 0.48788 val_loss= 1.31554 val_acc= 0.42623 time= 0.01563
Epoch: 0004 train_loss= 0.95875 train_acc= 0.48788 val_loss= 0.98413 val_acc= 0.39344 time= 0.01563
Epoch: 0005 train_loss= 1.31486 train_acc= 0.49697 val_loss= 0.77616 val_acc= 0.40984 time= 0.00000
Epoch: 0006 train_loss= 0.80802 train_acc= 0.47879 val_loss= 0.77175 val_acc= 0.47541 time= 0.01563
Epoch: 0007 train_loss= 1.19059 train_acc= 0.51212 val_loss= 0.81543 val_acc= 0.50820 time= 0.01563
Epoch: 0008 train_loss= 1.22410 train_acc= 0.50606 val_loss= 0.83530 val_acc= 0.50820 time= 0.01563
Epoch: 0009 train_loss= 0.79579 train_acc= 0.52727 val_loss= 0.85561 val_acc= 0.50820 time= 0.00000
Epoch: 0010 train_loss= 0.75600 train_acc= 0.49394 val_loss= 0.86128 val_acc= 0.50820 time= 0.01563
Epoch: 0011 train_loss= 1.12149 train_acc= 0.49697 val_loss= 0.81695 val_acc= 0.50820 time= 0.01563
Epoch: 0012 train_loss= 0.90423 train_acc= 0.49697 val_loss= 0.80656 val_acc= 0.50820 time= 0.01563
Epoch: 0013 train_loss= 0.74885 train_acc= 0.49697 val_loss= 0.79557 val_acc= 0.50820 time= 0.01563
Epoch: 0014 train_loss= 0.81988 train_acc= 0.48182 val_loss= 0.77713 val_acc= 0.49180 time= 0.00000
Epoch: 0015 train_loss= 0.82293 train_acc= 0.51515 val_loss= 0.75476 val_acc= 0.50820 time= 0.01563
Epoch: 0016 train_loss= 0.74433 train_acc= 0.50606 val_loss= 0.74164 val_acc= 0.45902 time= 0.01563
Epoch: 0017 train_loss= 0.81288 train_acc= 0.51212 val_loss= 0.73053 val_acc= 0.50820 time= 0.01563
Epoch: 0018 train_loss= 0.81218 train_acc= 0.47273 val_loss= 0.72743 val_acc= 0.40984 time= 0.01563
Epoch: 0019 train_loss= 0.77774 train_acc= 0.48485 val_loss= 0.72739 val_acc= 0.42623 time= 0.00000
Epoch: 0020 train_loss= 0.73396 train_acc= 0.49697 val_loss= 0.72978 val_acc= 0.40984 time= 0.01563
Epoch: 0021 train_loss= 0.75824 train_acc= 0.49394 val_loss= 0.73164 val_acc= 0.39344 time= 0.01563
Epoch: 0022 train_loss= 0.80841 train_acc= 0.48485 val_loss= 0.73126 val_acc= 0.39344 time= 0.01563
Epoch: 0023 train_loss= 0.74021 train_acc= 0.49697 val_loss= 0.73290 val_acc= 0.42623 time= 0.01563
Epoch: 0024 train_loss= 0.75524 train_acc= 0.51818 val_loss= 0.73647 val_acc= 0.42623 time= 0.00000
Epoch: 0025 train_loss= 0.90534 train_acc= 0.49091 val_loss= 0.73376 val_acc= 0.42623 time= 0.01562
Epoch: 0026 train_loss= 0.75499 train_acc= 0.53030 val_loss= 0.72930 val_acc= 0.44262 time= 0.01563
Epoch: 0027 train_loss= 0.98034 train_acc= 0.50909 val_loss= 0.72993 val_acc= 0.42623 time= 0.01563
Epoch: 0028 train_loss= 0.74990 train_acc= 0.51515 val_loss= 0.73301 val_acc= 0.42623 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.72135 accuracy= 0.52459 time= 0.01563 
