Epoch: 0001 train_loss= 2.08726 train_acc= 0.12399 val_loss= 2.08519 val_acc= 0.13793 time= 0.72401
Epoch: 0002 train_loss= 2.08474 train_acc= 0.16442 val_loss= 2.08412 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08240 train_acc= 0.18059 val_loss= 2.08344 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.07995 train_acc= 0.17790 val_loss= 2.08287 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07841 train_acc= 0.17790 val_loss= 2.08235 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.07678 train_acc= 0.17790 val_loss= 2.08179 val_acc= 0.13793 time= 0.00000
Epoch: 0007 train_loss= 2.07528 train_acc= 0.17790 val_loss= 2.08133 val_acc= 0.13793 time= 0.01563
Epoch: 0008 train_loss= 2.07380 train_acc= 0.18059 val_loss= 2.08087 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.07307 train_acc= 0.17790 val_loss= 2.08053 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.07289 train_acc= 0.17790 val_loss= 2.07989 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.07142 train_acc= 0.17790 val_loss= 2.07923 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.07152 train_acc= 0.17790 val_loss= 2.07833 val_acc= 0.13793 time= 0.00000
Epoch: 0013 train_loss= 2.07171 train_acc= 0.17790 val_loss= 2.07782 val_acc= 0.13793 time= 0.01563
Epoch: 0014 train_loss= 2.07032 train_acc= 0.17790 val_loss= 2.07744 val_acc= 0.13793 time= 0.00000
Epoch: 0015 train_loss= 2.07020 train_acc= 0.17790 val_loss= 2.07709 val_acc= 0.13793 time= 0.01563
Epoch: 0016 train_loss= 2.06963 train_acc= 0.17790 val_loss= 2.07658 val_acc= 0.13793 time= 0.00000
Epoch: 0017 train_loss= 2.06975 train_acc= 0.18059 val_loss= 2.07614 val_acc= 0.13793 time= 0.01563
Epoch: 0018 train_loss= 2.06836 train_acc= 0.17790 val_loss= 2.07584 val_acc= 0.13793 time= 0.00000
Epoch: 0019 train_loss= 2.06780 train_acc= 0.17790 val_loss= 2.07556 val_acc= 0.13793 time= 0.01563
Epoch: 0020 train_loss= 2.06775 train_acc= 0.17790 val_loss= 2.07543 val_acc= 0.13793 time= 0.00000
Epoch: 0021 train_loss= 2.06756 train_acc= 0.17790 val_loss= 2.07554 val_acc= 0.13793 time= 0.01563
Epoch: 0022 train_loss= 2.06729 train_acc= 0.17790 val_loss= 2.07558 val_acc= 0.13793 time= 0.00000
Epoch: 0023 train_loss= 2.06697 train_acc= 0.17790 val_loss= 2.07549 val_acc= 0.13793 time= 0.01563
Epoch: 0024 train_loss= 2.06552 train_acc= 0.18059 val_loss= 2.07537 val_acc= 0.13793 time= 0.00000
Epoch: 0025 train_loss= 2.06683 train_acc= 0.17790 val_loss= 2.07549 val_acc= 0.13793 time= 0.01563
Epoch: 0026 train_loss= 2.06626 train_acc= 0.17790 val_loss= 2.07541 val_acc= 0.13793 time= 0.00000
Epoch: 0027 train_loss= 2.06723 train_acc= 0.17790 val_loss= 2.07523 val_acc= 0.13793 time= 0.01563
Epoch: 0028 train_loss= 2.06550 train_acc= 0.18059 val_loss= 2.07524 val_acc= 0.13793 time= 0.00000
Epoch: 0029 train_loss= 2.06643 train_acc= 0.17790 val_loss= 2.07560 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.05827 accuracy= 0.11864 time= 0.00000 
