Epoch: 0001 train_loss= 2.08690 train_acc= 0.09704 val_loss= 2.08382 val_acc= 0.13793 time= 0.72877
Epoch: 0002 train_loss= 2.08478 train_acc= 0.14825 val_loss= 2.08113 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08248 train_acc= 0.11321 val_loss= 2.07867 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.08102 train_acc= 0.15364 val_loss= 2.07629 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07914 train_acc= 0.14286 val_loss= 2.07411 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.07875 train_acc= 0.15364 val_loss= 2.07192 val_acc= 0.13793 time= 0.00000
Epoch: 0007 train_loss= 2.07758 train_acc= 0.14286 val_loss= 2.06929 val_acc= 0.13793 time= 0.01563
Epoch: 0008 train_loss= 2.07648 train_acc= 0.15364 val_loss= 2.06658 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.07619 train_acc= 0.14555 val_loss= 2.06392 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.07482 train_acc= 0.15364 val_loss= 2.06101 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.07396 train_acc= 0.14825 val_loss= 2.05834 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.07397 train_acc= 0.14825 val_loss= 2.05586 val_acc= 0.13793 time= 0.01563
Epoch: 0013 train_loss= 2.07248 train_acc= 0.15094 val_loss= 2.05354 val_acc= 0.13793 time= 0.00000
Epoch: 0014 train_loss= 2.07298 train_acc= 0.15094 val_loss= 2.05127 val_acc= 0.13793 time= 0.01563
Epoch: 0015 train_loss= 2.07198 train_acc= 0.13747 val_loss= 2.04943 val_acc= 0.13793 time= 0.00000
Epoch: 0016 train_loss= 2.07077 train_acc= 0.14825 val_loss= 2.04794 val_acc= 0.13793 time= 0.01563
Epoch: 0017 train_loss= 2.07222 train_acc= 0.14555 val_loss= 2.04687 val_acc= 0.13793 time= 0.01563
Epoch: 0018 train_loss= 2.07189 train_acc= 0.15094 val_loss= 2.04630 val_acc= 0.13793 time= 0.00000
Epoch: 0019 train_loss= 2.07087 train_acc= 0.14825 val_loss= 2.04618 val_acc= 0.13793 time= 0.01887
Epoch: 0020 train_loss= 2.07151 train_acc= 0.14286 val_loss= 2.04616 val_acc= 0.13793 time= 0.00800
Epoch: 0021 train_loss= 2.06953 train_acc= 0.14555 val_loss= 2.04628 val_acc= 0.13793 time= 0.00900
Epoch: 0022 train_loss= 2.07102 train_acc= 0.14555 val_loss= 2.04657 val_acc= 0.13793 time= 0.00800
Epoch: 0023 train_loss= 2.07078 train_acc= 0.14825 val_loss= 2.04697 val_acc= 0.13793 time= 0.00707
Epoch: 0024 train_loss= 2.07171 train_acc= 0.14286 val_loss= 2.04751 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.04213 accuracy= 0.15254 time= 0.00000 
