Epoch: 0001 train_loss= 0.78339 train_acc= 0.50909 val_loss= 0.86776 val_acc= 0.55738 time= 0.07814
Epoch: 0002 train_loss= 0.92101 train_acc= 0.53939 val_loss= 0.80001 val_acc= 0.50820 time= 0.01562
Epoch: 0003 train_loss= 0.83764 train_acc= 0.52424 val_loss= 0.78602 val_acc= 0.52459 time= 0.01563
Epoch: 0004 train_loss= 0.82200 train_acc= 0.50303 val_loss= 0.79884 val_acc= 0.52459 time= 0.01563
Epoch: 0005 train_loss= 1.07987 train_acc= 0.49091 val_loss= 0.80027 val_acc= 0.52459 time= 0.01563
Epoch: 0006 train_loss= 0.82108 train_acc= 0.51515 val_loss= 0.80909 val_acc= 0.57377 time= 0.00000
Epoch: 0007 train_loss= 0.91429 train_acc= 0.53636 val_loss= 0.81020 val_acc= 0.57377 time= 0.01563
Epoch: 0008 train_loss= 0.87321 train_acc= 0.54848 val_loss= 0.79646 val_acc= 0.54098 time= 0.01563
Epoch: 0009 train_loss= 0.80678 train_acc= 0.50000 val_loss= 0.77382 val_acc= 0.55738 time= 0.01563
Epoch: 0010 train_loss= 0.87930 train_acc= 0.44848 val_loss= 0.75808 val_acc= 0.57377 time= 0.01563
Epoch: 0011 train_loss= 0.72454 train_acc= 0.51818 val_loss= 0.74944 val_acc= 0.57377 time= 0.00000
Epoch: 0012 train_loss= 0.76900 train_acc= 0.50000 val_loss= 0.74857 val_acc= 0.55738 time= 0.01563
Epoch: 0013 train_loss= 0.82506 train_acc= 0.53939 val_loss= 0.73667 val_acc= 0.55738 time= 0.01563
Epoch: 0014 train_loss= 0.85625 train_acc= 0.50000 val_loss= 0.72053 val_acc= 0.57377 time= 0.01562
Epoch: 0015 train_loss= 0.72513 train_acc= 0.52424 val_loss= 0.70718 val_acc= 0.59016 time= 0.00000
Epoch: 0016 train_loss= 0.84756 train_acc= 0.49697 val_loss= 0.70358 val_acc= 0.50820 time= 0.01563
Epoch: 0017 train_loss= 0.73337 train_acc= 0.53636 val_loss= 0.70187 val_acc= 0.49180 time= 0.01563
Epoch: 0018 train_loss= 0.76451 train_acc= 0.48182 val_loss= 0.70518 val_acc= 0.50820 time= 0.01563
Epoch: 0019 train_loss= 0.77303 train_acc= 0.50303 val_loss= 0.70705 val_acc= 0.52459 time= 0.01563
Epoch: 0020 train_loss= 0.70783 train_acc= 0.53939 val_loss= 0.70851 val_acc= 0.54098 time= 0.00000
Epoch: 0021 train_loss= 0.72897 train_acc= 0.48182 val_loss= 0.71119 val_acc= 0.57377 time= 0.01563
Epoch: 0022 train_loss= 1.01137 train_acc= 0.50606 val_loss= 0.71638 val_acc= 0.57377 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.71485 accuracy= 0.46721 time= 0.00000 
