Epoch: 0001 train_loss= 1.15645 train_acc= 0.50909 val_loss= 0.82794 val_acc= 0.53226 time= 0.09376
Epoch: 0002 train_loss= 1.23039 train_acc= 0.48485 val_loss= 0.70205 val_acc= 0.54839 time= 0.01562
Epoch: 0003 train_loss= 1.21506 train_acc= 0.50303 val_loss= 0.76172 val_acc= 0.54839 time= 0.01563
Epoch: 0004 train_loss= 0.97874 train_acc= 0.53333 val_loss= 0.84967 val_acc= 0.56452 time= 0.00000
Epoch: 0005 train_loss= 1.02427 train_acc= 0.49091 val_loss= 0.87954 val_acc= 0.56452 time= 0.01563
Epoch: 0006 train_loss= 0.80147 train_acc= 0.51212 val_loss= 0.89312 val_acc= 0.56452 time= 0.01563
Epoch: 0007 train_loss= 1.16621 train_acc= 0.51818 val_loss= 0.86909 val_acc= 0.56452 time= 0.01563
Epoch: 0008 train_loss= 1.03302 train_acc= 0.50909 val_loss= 0.82895 val_acc= 0.56452 time= 0.01563
Epoch: 0009 train_loss= 0.78508 train_acc= 0.50000 val_loss= 0.79696 val_acc= 0.56452 time= 0.00000
Epoch: 0010 train_loss= 1.12206 train_acc= 0.52121 val_loss= 0.75673 val_acc= 0.56452 time= 0.01563
Epoch: 0011 train_loss= 0.84310 train_acc= 0.50909 val_loss= 0.72778 val_acc= 0.56452 time= 0.01563
Epoch: 0012 train_loss= 0.85321 train_acc= 0.51515 val_loss= 0.71045 val_acc= 0.56452 time= 0.01563
Epoch: 0013 train_loss= 0.80693 train_acc= 0.49394 val_loss= 0.70689 val_acc= 0.53226 time= 0.00000
Epoch: 0014 train_loss= 0.80520 train_acc= 0.51818 val_loss= 0.70951 val_acc= 0.46774 time= 0.01563
Epoch: 0015 train_loss= 0.79043 train_acc= 0.51818 val_loss= 0.71578 val_acc= 0.43548 time= 0.01563
Epoch: 0016 train_loss= 0.73349 train_acc= 0.53030 val_loss= 0.72109 val_acc= 0.46774 time= 0.01563
Epoch: 0017 train_loss= 0.93269 train_acc= 0.51515 val_loss= 0.72214 val_acc= 0.45161 time= 0.01563
Epoch: 0018 train_loss= 0.73522 train_acc= 0.50303 val_loss= 0.72236 val_acc= 0.43548 time= 0.00000
Epoch: 0019 train_loss= 0.85457 train_acc= 0.51212 val_loss= 0.71933 val_acc= 0.41935 time= 0.01563
Epoch: 0020 train_loss= 0.69958 train_acc= 0.55152 val_loss= 0.71751 val_acc= 0.46774 time= 0.01563
Epoch: 0021 train_loss= 0.79969 train_acc= 0.50606 val_loss= 0.71607 val_acc= 0.46774 time= 0.01563
Epoch: 0022 train_loss= 0.78420 train_acc= 0.51515 val_loss= 0.71690 val_acc= 0.54839 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.70346 accuracy= 0.57258 time= 0.01563 
