Epoch: 0001 train_loss= 0.69844 train_acc= 0.52727 val_loss= 0.69613 val_acc= 0.55738 time= 0.09380
Epoch: 0002 train_loss= 0.69805 train_acc= 0.52121 val_loss= 0.69476 val_acc= 0.55738 time= 0.01562
Epoch: 0003 train_loss= 0.69733 train_acc= 0.52121 val_loss= 0.69380 val_acc= 0.55738 time= 0.01563
Epoch: 0004 train_loss= 0.69654 train_acc= 0.52121 val_loss= 0.69303 val_acc= 0.55738 time= 0.01563
Epoch: 0005 train_loss= 0.69668 train_acc= 0.52121 val_loss= 0.69245 val_acc= 0.55738 time= 0.00000
Epoch: 0006 train_loss= 0.69609 train_acc= 0.52121 val_loss= 0.69203 val_acc= 0.55738 time= 0.01563
Epoch: 0007 train_loss= 0.69575 train_acc= 0.52121 val_loss= 0.69171 val_acc= 0.55738 time= 0.01563
Epoch: 0008 train_loss= 0.69482 train_acc= 0.52121 val_loss= 0.69141 val_acc= 0.55738 time= 0.01563
Epoch: 0009 train_loss= 0.69512 train_acc= 0.52121 val_loss= 0.69122 val_acc= 0.55738 time= 0.00000
Epoch: 0010 train_loss= 0.69472 train_acc= 0.52121 val_loss= 0.69113 val_acc= 0.55738 time= 0.01563
Epoch: 0011 train_loss= 0.69483 train_acc= 0.52121 val_loss= 0.69112 val_acc= 0.55738 time= 0.01563
Epoch: 0012 train_loss= 0.69393 train_acc= 0.52121 val_loss= 0.69107 val_acc= 0.55738 time= 0.01978
Epoch: 0013 train_loss= 0.69412 train_acc= 0.52121 val_loss= 0.69100 val_acc= 0.55738 time= 0.01300
Epoch: 0014 train_loss= 0.69363 train_acc= 0.52121 val_loss= 0.69089 val_acc= 0.55738 time= 0.01200
Epoch: 0015 train_loss= 0.69322 train_acc= 0.52121 val_loss= 0.69071 val_acc= 0.55738 time= 0.01200
Epoch: 0016 train_loss= 0.69334 train_acc= 0.52121 val_loss= 0.69051 val_acc= 0.55738 time= 0.00121
Epoch: 0017 train_loss= 0.69335 train_acc= 0.52121 val_loss= 0.69030 val_acc= 0.55738 time= 0.01563
Epoch: 0018 train_loss= 0.69290 train_acc= 0.52121 val_loss= 0.69007 val_acc= 0.55738 time= 0.01563
Epoch: 0019 train_loss= 0.69304 train_acc= 0.52121 val_loss= 0.68986 val_acc= 0.55738 time= 0.01563
Epoch: 0020 train_loss= 0.69234 train_acc= 0.52121 val_loss= 0.68961 val_acc= 0.55738 time= 0.00000
Epoch: 0021 train_loss= 0.69244 train_acc= 0.52121 val_loss= 0.68938 val_acc= 0.55738 time= 0.01563
Epoch: 0022 train_loss= 0.69225 train_acc= 0.52121 val_loss= 0.68913 val_acc= 0.55738 time= 0.01563
Epoch: 0023 train_loss= 0.69312 train_acc= 0.52121 val_loss= 0.68901 val_acc= 0.55738 time= 0.01563
Epoch: 0024 train_loss= 0.69236 train_acc= 0.52121 val_loss= 0.68895 val_acc= 0.55738 time= 0.01563
Epoch: 0025 train_loss= 0.69216 train_acc= 0.52121 val_loss= 0.68890 val_acc= 0.55738 time= 0.00000
Epoch: 0026 train_loss= 0.69222 train_acc= 0.52121 val_loss= 0.68887 val_acc= 0.55738 time= 0.01563
Epoch: 0027 train_loss= 0.69219 train_acc= 0.52121 val_loss= 0.68889 val_acc= 0.55738 time= 0.01563
Epoch: 0028 train_loss= 0.69233 train_acc= 0.52121 val_loss= 0.68897 val_acc= 0.55738 time= 0.01563
Epoch: 0029 train_loss= 0.69216 train_acc= 0.52121 val_loss= 0.68905 val_acc= 0.55738 time= 0.00000
Epoch: 0030 train_loss= 0.69230 train_acc= 0.52121 val_loss= 0.68917 val_acc= 0.55738 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69093 accuracy= 0.53279 time= 0.01563 
