Epoch: 0001 train_loss= 1.39069 train_acc= 0.23603 val_loss= 1.39133 val_acc= 0.12500 time= 0.93756
Epoch: 0002 train_loss= 1.39040 train_acc= 0.24721 val_loss= 1.38995 val_acc= 0.19643 time= 0.01562
Epoch: 0003 train_loss= 1.38920 train_acc= 0.24022 val_loss= 1.38864 val_acc= 0.25000 time= 0.00000
Epoch: 0004 train_loss= 1.38855 train_acc= 0.22765 val_loss= 1.38739 val_acc= 0.25000 time= 0.01563
Epoch: 0005 train_loss= 1.38758 train_acc= 0.25559 val_loss= 1.38618 val_acc= 0.30357 time= 0.00000
Epoch: 0006 train_loss= 1.38611 train_acc= 0.26257 val_loss= 1.38501 val_acc= 0.33929 time= 0.01563
Epoch: 0007 train_loss= 1.38530 train_acc= 0.30307 val_loss= 1.38389 val_acc= 0.33929 time= 0.00000
Epoch: 0008 train_loss= 1.38454 train_acc= 0.30587 val_loss= 1.38279 val_acc= 0.33929 time= 0.00000
Epoch: 0009 train_loss= 1.38476 train_acc= 0.30726 val_loss= 1.38172 val_acc= 0.33929 time= 0.01563
Epoch: 0010 train_loss= 1.38305 train_acc= 0.30726 val_loss= 1.38066 val_acc= 0.33929 time= 0.00000
Epoch: 0011 train_loss= 1.38410 train_acc= 0.30726 val_loss= 1.37962 val_acc= 0.33929 time= 0.01562
Epoch: 0012 train_loss= 1.38269 train_acc= 0.30587 val_loss= 1.37858 val_acc= 0.33929 time= 0.00000
Epoch: 0013 train_loss= 1.38211 train_acc= 0.30866 val_loss= 1.37756 val_acc= 0.33929 time= 0.01563
Epoch: 0014 train_loss= 1.38200 train_acc= 0.30726 val_loss= 1.37656 val_acc= 0.33929 time= 0.00000
Epoch: 0015 train_loss= 1.38059 train_acc= 0.30726 val_loss= 1.37557 val_acc= 0.33929 time= 0.01563
Epoch: 0016 train_loss= 1.38126 train_acc= 0.30726 val_loss= 1.37460 val_acc= 0.33929 time= 0.00000
Epoch: 0017 train_loss= 1.38086 train_acc= 0.30726 val_loss= 1.37366 val_acc= 0.33929 time= 0.00000
Epoch: 0018 train_loss= 1.37969 train_acc= 0.30726 val_loss= 1.37276 val_acc= 0.33929 time= 0.01563
Epoch: 0019 train_loss= 1.37918 train_acc= 0.30726 val_loss= 1.37192 val_acc= 0.33929 time= 0.00000
Epoch: 0020 train_loss= 1.37892 train_acc= 0.30726 val_loss= 1.37112 val_acc= 0.33929 time= 0.01562
Epoch: 0021 train_loss= 1.37954 train_acc= 0.30866 val_loss= 1.37040 val_acc= 0.33929 time= 0.00000
Epoch: 0022 train_loss= 1.37974 train_acc= 0.30726 val_loss= 1.36981 val_acc= 0.33929 time= 0.01563
Epoch: 0023 train_loss= 1.38026 train_acc= 0.30866 val_loss= 1.36929 val_acc= 0.33929 time= 0.00000
Epoch: 0024 train_loss= 1.37870 train_acc= 0.30726 val_loss= 1.36886 val_acc= 0.33929 time= 0.00000
Epoch: 0025 train_loss= 1.37895 train_acc= 0.30726 val_loss= 1.36850 val_acc= 0.33929 time= 0.01562
Epoch: 0026 train_loss= 1.37917 train_acc= 0.30726 val_loss= 1.36821 val_acc= 0.33929 time= 0.00000
Epoch: 0027 train_loss= 1.37908 train_acc= 0.30726 val_loss= 1.36799 val_acc= 0.33929 time= 0.01563
Epoch: 0028 train_loss= 1.37987 train_acc= 0.30726 val_loss= 1.36782 val_acc= 0.33929 time= 0.00000
Epoch: 0029 train_loss= 1.37928 train_acc= 0.30726 val_loss= 1.36772 val_acc= 0.33929 time= 0.01563
Epoch: 0030 train_loss= 1.37931 train_acc= 0.30726 val_loss= 1.36772 val_acc= 0.33929 time= 0.00000
Epoch: 0031 train_loss= 1.37955 train_acc= 0.30726 val_loss= 1.36778 val_acc= 0.33929 time= 0.01563
Epoch: 0032 train_loss= 1.37947 train_acc= 0.30726 val_loss= 1.36791 val_acc= 0.33929 time= 0.00000
Epoch: 0033 train_loss= 1.38005 train_acc= 0.30726 val_loss= 1.36809 val_acc= 0.33929 time= 0.01563
Epoch: 0034 train_loss= 1.38001 train_acc= 0.30726 val_loss= 1.36832 val_acc= 0.33929 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 1.37789 accuracy= 0.29204 time= 0.00000 
