Epoch: 0001 train_loss= 0.69888 train_acc= 0.51558 val_loss= 0.69736 val_acc= 0.57377 time= 0.32293
Epoch: 0002 train_loss= 0.69837 train_acc= 0.51039 val_loss= 0.69600 val_acc= 0.57377 time= 0.01563
Epoch: 0003 train_loss= 0.69779 train_acc= 0.50390 val_loss= 0.69491 val_acc= 0.57377 time= 0.01563
Epoch: 0004 train_loss= 0.69739 train_acc= 0.50260 val_loss= 0.69417 val_acc= 0.57377 time= 0.00869
Epoch: 0005 train_loss= 0.69695 train_acc= 0.50649 val_loss= 0.69366 val_acc= 0.57377 time= 0.01563
Epoch: 0006 train_loss= 0.69649 train_acc= 0.50390 val_loss= 0.69332 val_acc= 0.57377 time= 0.01563
Epoch: 0007 train_loss= 0.69618 train_acc= 0.50390 val_loss= 0.69314 val_acc= 0.57377 time= 0.00000
Epoch: 0008 train_loss= 0.69572 train_acc= 0.50390 val_loss= 0.69303 val_acc= 0.57377 time= 0.01563
Epoch: 0009 train_loss= 0.69531 train_acc= 0.50390 val_loss= 0.69289 val_acc= 0.57377 time= 0.01563
Epoch: 0010 train_loss= 0.69514 train_acc= 0.50519 val_loss= 0.69281 val_acc= 0.57377 time= 0.01562
Epoch: 0011 train_loss= 0.69492 train_acc= 0.50390 val_loss= 0.69281 val_acc= 0.57377 time= 0.01563
Epoch: 0012 train_loss= 0.69456 train_acc= 0.50130 val_loss= 0.69267 val_acc= 0.57377 time= 0.01563
Epoch: 0013 train_loss= 0.69441 train_acc= 0.50390 val_loss= 0.69249 val_acc= 0.57377 time= 0.00000
Epoch: 0014 train_loss= 0.69422 train_acc= 0.50519 val_loss= 0.69227 val_acc= 0.57377 time= 0.01563
Epoch: 0015 train_loss= 0.69407 train_acc= 0.50390 val_loss= 0.69206 val_acc= 0.57377 time= 0.01563
Epoch: 0016 train_loss= 0.69389 train_acc= 0.50390 val_loss= 0.69179 val_acc= 0.57377 time= 0.01563
Epoch: 0017 train_loss= 0.69378 train_acc= 0.50390 val_loss= 0.69149 val_acc= 0.57377 time= 0.01563
Epoch: 0018 train_loss= 0.69370 train_acc= 0.50390 val_loss= 0.69125 val_acc= 0.57377 time= 0.01563
Epoch: 0019 train_loss= 0.69364 train_acc= 0.50390 val_loss= 0.69116 val_acc= 0.57377 time= 0.00000
Epoch: 0020 train_loss= 0.69359 train_acc= 0.50390 val_loss= 0.69122 val_acc= 0.57377 time= 0.01562
Epoch: 0021 train_loss= 0.69343 train_acc= 0.50390 val_loss= 0.69123 val_acc= 0.57377 time= 0.01563
Epoch: 0022 train_loss= 0.69338 train_acc= 0.50390 val_loss= 0.69121 val_acc= 0.57377 time= 0.01563
Epoch: 0023 train_loss= 0.69335 train_acc= 0.50390 val_loss= 0.69123 val_acc= 0.57377 time= 0.01563
Epoch: 0024 train_loss= 0.69335 train_acc= 0.50390 val_loss= 0.69131 val_acc= 0.57377 time= 0.01563
Epoch: 0025 train_loss= 0.69321 train_acc= 0.50390 val_loss= 0.69125 val_acc= 0.57377 time= 0.01563
Epoch: 0026 train_loss= 0.69322 train_acc= 0.50390 val_loss= 0.69119 val_acc= 0.57377 time= 0.00000
Epoch: 0027 train_loss= 0.69317 train_acc= 0.50390 val_loss= 0.69106 val_acc= 0.57377 time= 0.01563
Epoch: 0028 train_loss= 0.69316 train_acc= 0.50390 val_loss= 0.69086 val_acc= 0.57377 time= 0.01563
Epoch: 0029 train_loss= 0.69316 train_acc= 0.50390 val_loss= 0.69070 val_acc= 0.57377 time= 0.01563
Epoch: 0030 train_loss= 0.69320 train_acc= 0.50390 val_loss= 0.69070 val_acc= 0.57377 time= 0.01563
Epoch: 0031 train_loss= 0.69318 train_acc= 0.50390 val_loss= 0.69080 val_acc= 0.57377 time= 0.00000
Epoch: 0032 train_loss= 0.69315 train_acc= 0.50390 val_loss= 0.69102 val_acc= 0.57377 time= 0.01563
Epoch: 0033 train_loss= 0.69316 train_acc= 0.50519 val_loss= 0.69114 val_acc= 0.57377 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69144 accuracy= 0.55738 time= 0.01563 
