Epoch: 0001 train_loss= 2.08732 train_acc= 0.06918 val_loss= 2.08460 val_acc= 0.13793 time= 0.14063
Epoch: 0002 train_loss= 2.08472 train_acc= 0.16981 val_loss= 2.08206 val_acc= 0.13793 time= 0.01563
Epoch: 0003 train_loss= 2.08287 train_acc= 0.16981 val_loss= 2.07992 val_acc= 0.13793 time= 0.01562
Epoch: 0004 train_loss= 2.08086 train_acc= 0.16981 val_loss= 2.07814 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07946 train_acc= 0.16981 val_loss= 2.07659 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.07774 train_acc= 0.16981 val_loss= 2.07510 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.07638 train_acc= 0.16981 val_loss= 2.07358 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.07478 train_acc= 0.16981 val_loss= 2.07207 val_acc= 0.13793 time= 0.01562
Epoch: 0009 train_loss= 2.07436 train_acc= 0.16981 val_loss= 2.07063 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.07251 train_acc= 0.16981 val_loss= 2.06921 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.07221 train_acc= 0.16981 val_loss= 2.06793 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.07015 train_acc= 0.16981 val_loss= 2.06686 val_acc= 0.13793 time= 0.01563
Epoch: 0013 train_loss= 2.06914 train_acc= 0.16981 val_loss= 2.06583 val_acc= 0.13793 time= 0.00000
Epoch: 0014 train_loss= 2.06918 train_acc= 0.16981 val_loss= 2.06481 val_acc= 0.13793 time= 0.01563
Epoch: 0015 train_loss= 2.06564 train_acc= 0.16981 val_loss= 2.06384 val_acc= 0.13793 time= 0.01563
Epoch: 0016 train_loss= 2.06709 train_acc= 0.16981 val_loss= 2.06308 val_acc= 0.13793 time= 0.00000
Epoch: 0017 train_loss= 2.06525 train_acc= 0.16981 val_loss= 2.06239 val_acc= 0.13793 time= 0.01563
Epoch: 0018 train_loss= 2.06462 train_acc= 0.16981 val_loss= 2.06193 val_acc= 0.13793 time= 0.01562
Epoch: 0019 train_loss= 2.06466 train_acc= 0.16981 val_loss= 2.06150 val_acc= 0.13793 time= 0.00000
Epoch: 0020 train_loss= 2.06299 train_acc= 0.16981 val_loss= 2.06103 val_acc= 0.13793 time= 0.01563
Epoch: 0021 train_loss= 2.06296 train_acc= 0.16981 val_loss= 2.06068 val_acc= 0.13793 time= 0.00000
Epoch: 0022 train_loss= 2.06100 train_acc= 0.16981 val_loss= 2.06039 val_acc= 0.13793 time= 0.01562
Epoch: 0023 train_loss= 2.06174 train_acc= 0.16981 val_loss= 2.06015 val_acc= 0.13793 time= 0.01563
Epoch: 0024 train_loss= 2.06090 train_acc= 0.16981 val_loss= 2.05998 val_acc= 0.13793 time= 0.00000
Epoch: 0025 train_loss= 2.05803 train_acc= 0.16981 val_loss= 2.05985 val_acc= 0.13793 time= 0.01563
Epoch: 0026 train_loss= 2.06046 train_acc= 0.16981 val_loss= 2.05950 val_acc= 0.13793 time= 0.01563
Epoch: 0027 train_loss= 2.05791 train_acc= 0.16981 val_loss= 2.05917 val_acc= 0.13793 time= 0.00000
Epoch: 0028 train_loss= 2.05861 train_acc= 0.16981 val_loss= 2.05873 val_acc= 0.13793 time= 0.01563
Epoch: 0029 train_loss= 2.05820 train_acc= 0.16981 val_loss= 2.05817 val_acc= 0.13793 time= 0.01563
Epoch: 0030 train_loss= 2.05749 train_acc= 0.16981 val_loss= 2.05749 val_acc= 0.13793 time= 0.00000
Epoch: 0031 train_loss= 2.05793 train_acc= 0.16981 val_loss= 2.05665 val_acc= 0.13793 time= 0.01563
Epoch: 0032 train_loss= 2.05785 train_acc= 0.16981 val_loss= 2.05589 val_acc= 0.13793 time= 0.01563
Epoch: 0033 train_loss= 2.05667 train_acc= 0.16981 val_loss= 2.05515 val_acc= 0.13793 time= 0.00000
Epoch: 0034 train_loss= 2.05712 train_acc= 0.16981 val_loss= 2.05441 val_acc= 0.13793 time= 0.01563
Epoch: 0035 train_loss= 2.05554 train_acc= 0.16981 val_loss= 2.05363 val_acc= 0.13793 time= 0.01563
Epoch: 0036 train_loss= 2.05693 train_acc= 0.16981 val_loss= 2.05297 val_acc= 0.13793 time= 0.00000
Epoch: 0037 train_loss= 2.05452 train_acc= 0.16981 val_loss= 2.05244 val_acc= 0.13793 time= 0.01563
Epoch: 0038 train_loss= 2.05613 train_acc= 0.16981 val_loss= 2.05199 val_acc= 0.13793 time= 0.01563
Epoch: 0039 train_loss= 2.05598 train_acc= 0.16981 val_loss= 2.05160 val_acc= 0.13793 time= 0.00000
Epoch: 0040 train_loss= 2.05367 train_acc= 0.16981 val_loss= 2.05130 val_acc= 0.13793 time= 0.01563
Epoch: 0041 train_loss= 2.05602 train_acc= 0.16981 val_loss= 2.05106 val_acc= 0.13793 time= 0.00000
Epoch: 0042 train_loss= 2.05399 train_acc= 0.16981 val_loss= 2.05101 val_acc= 0.13793 time= 0.01563
Epoch: 0043 train_loss= 2.05463 train_acc= 0.16981 val_loss= 2.05105 val_acc= 0.13793 time= 0.01563
Epoch: 0044 train_loss= 2.05470 train_acc= 0.16981 val_loss= 2.05123 val_acc= 0.13793 time= 0.00000
Epoch: 0045 train_loss= 2.05426 train_acc= 0.16981 val_loss= 2.05159 val_acc= 0.13793 time= 0.01563
Epoch: 0046 train_loss= 2.05485 train_acc= 0.16981 val_loss= 2.05173 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07673 accuracy= 0.11864 time= 0.00000 
