Epoch: 0001 train_loss= 0.69544 train_acc= 0.53091 val_loss= 0.68022 val_acc= 0.55738 time= 0.54691
Epoch: 0002 train_loss= 0.69093 train_acc= 0.53273 val_loss= 0.68159 val_acc= 0.55738 time= 0.01562
Epoch: 0003 train_loss= 0.69072 train_acc= 0.54727 val_loss= 0.68282 val_acc= 0.55738 time= 0.00000
Epoch: 0004 train_loss= 0.69142 train_acc= 0.53091 val_loss= 0.68415 val_acc= 0.55738 time= 0.00000
Epoch: 0005 train_loss= 0.69195 train_acc= 0.52545 val_loss= 0.68521 val_acc= 0.55738 time= 0.00000
Epoch: 0006 train_loss= 0.69100 train_acc= 0.53636 val_loss= 0.68603 val_acc= 0.55738 time= 0.01563
Epoch: 0007 train_loss= 0.69252 train_acc= 0.51818 val_loss= 0.68640 val_acc= 0.55738 time= 0.00000
Epoch: 0008 train_loss= 0.68948 train_acc= 0.53818 val_loss= 0.68644 val_acc= 0.55738 time= 0.01563
Epoch: 0009 train_loss= 0.69169 train_acc= 0.53091 val_loss= 0.68627 val_acc= 0.55738 time= 0.00000
Epoch: 0010 train_loss= 0.69086 train_acc= 0.52909 val_loss= 0.68579 val_acc= 0.55738 time= 0.00000
Epoch: 0011 train_loss= 0.69260 train_acc= 0.52545 val_loss= 0.68516 val_acc= 0.55738 time= 0.00000
Epoch: 0012 train_loss= 0.69191 train_acc= 0.52909 val_loss= 0.68456 val_acc= 0.55738 time= 0.01563
Epoch: 0013 train_loss= 0.69337 train_acc= 0.52727 val_loss= 0.68409 val_acc= 0.55738 time= 0.00000
Epoch: 0014 train_loss= 0.69016 train_acc= 0.53455 val_loss= 0.68354 val_acc= 0.55738 time= 0.00000
Epoch: 0015 train_loss= 0.69101 train_acc= 0.53455 val_loss= 0.68295 val_acc= 0.55738 time= 0.01563
Epoch: 0016 train_loss= 0.69113 train_acc= 0.53455 val_loss= 0.68257 val_acc= 0.55738 time= 0.00000
Epoch: 0017 train_loss= 0.69120 train_acc= 0.53636 val_loss= 0.68227 val_acc= 0.55738 time= 0.00000
Epoch: 0018 train_loss= 0.69282 train_acc= 0.52909 val_loss= 0.68217 val_acc= 0.55738 time= 0.01563
Epoch: 0019 train_loss= 0.68982 train_acc= 0.53455 val_loss= 0.68216 val_acc= 0.55738 time= 0.00000
Epoch: 0020 train_loss= 0.68939 train_acc= 0.54182 val_loss= 0.68215 val_acc= 0.55738 time= 0.01563
Epoch: 0021 train_loss= 0.69254 train_acc= 0.53273 val_loss= 0.68228 val_acc= 0.55738 time= 0.00000
Epoch: 0022 train_loss= 0.69316 train_acc= 0.52909 val_loss= 0.68248 val_acc= 0.55738 time= 0.00000
Epoch: 0023 train_loss= 0.69337 train_acc= 0.53273 val_loss= 0.68277 val_acc= 0.55738 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69094 accuracy= 0.54918 time= 0.01563 
