Epoch: 0001 train_loss= 1.39420 train_acc= 0.22476 val_loss= 1.39027 val_acc= 0.41071 time= 0.14063
Epoch: 0002 train_loss= 1.39108 train_acc= 0.32899 val_loss= 1.38683 val_acc= 0.41071 time= 0.01563
Epoch: 0003 train_loss= 1.38845 train_acc= 0.32899 val_loss= 1.38351 val_acc= 0.41071 time= 0.01563
Epoch: 0004 train_loss= 1.38641 train_acc= 0.32899 val_loss= 1.38045 val_acc= 0.41071 time= 0.00000
Epoch: 0005 train_loss= 1.38458 train_acc= 0.32899 val_loss= 1.37769 val_acc= 0.41071 time= 0.01563
Epoch: 0006 train_loss= 1.38336 train_acc= 0.32899 val_loss= 1.37521 val_acc= 0.41071 time= 0.01563
Epoch: 0007 train_loss= 1.38225 train_acc= 0.32899 val_loss= 1.37299 val_acc= 0.41071 time= 0.01563
Epoch: 0008 train_loss= 1.38153 train_acc= 0.32899 val_loss= 1.37100 val_acc= 0.41071 time= 0.01563
Epoch: 0009 train_loss= 1.38152 train_acc= 0.32899 val_loss= 1.36925 val_acc= 0.41071 time= 0.01563
Epoch: 0010 train_loss= 1.38080 train_acc= 0.32899 val_loss= 1.36775 val_acc= 0.41071 time= 0.01563
Epoch: 0011 train_loss= 1.38109 train_acc= 0.32899 val_loss= 1.36651 val_acc= 0.41071 time= 0.01563
Epoch: 0012 train_loss= 1.38020 train_acc= 0.32899 val_loss= 1.36544 val_acc= 0.41071 time= 0.01563
Epoch: 0013 train_loss= 1.38059 train_acc= 0.32899 val_loss= 1.36457 val_acc= 0.41071 time= 0.01562
Epoch: 0014 train_loss= 1.37996 train_acc= 0.32899 val_loss= 1.36383 val_acc= 0.41071 time= 0.01563
Epoch: 0015 train_loss= 1.38019 train_acc= 0.32899 val_loss= 1.36326 val_acc= 0.41071 time= 0.01563
Epoch: 0016 train_loss= 1.38028 train_acc= 0.32899 val_loss= 1.36289 val_acc= 0.41071 time= 0.01563
Epoch: 0017 train_loss= 1.37914 train_acc= 0.32899 val_loss= 1.36264 val_acc= 0.41071 time= 0.01563
Epoch: 0018 train_loss= 1.37876 train_acc= 0.32899 val_loss= 1.36254 val_acc= 0.41071 time= 0.01563
Epoch: 0019 train_loss= 1.37847 train_acc= 0.32899 val_loss= 1.36254 val_acc= 0.41071 time= 0.01563
Epoch: 0020 train_loss= 1.37842 train_acc= 0.32899 val_loss= 1.36256 val_acc= 0.41071 time= 0.01563
Epoch: 0021 train_loss= 1.37784 train_acc= 0.32899 val_loss= 1.36250 val_acc= 0.41071 time= 0.01563
Epoch: 0022 train_loss= 1.37776 train_acc= 0.32899 val_loss= 1.36232 val_acc= 0.41071 time= 0.01563
Epoch: 0023 train_loss= 1.37749 train_acc= 0.32899 val_loss= 1.36203 val_acc= 0.41071 time= 0.01563
Epoch: 0024 train_loss= 1.37668 train_acc= 0.32899 val_loss= 1.36152 val_acc= 0.41071 time= 0.01563
Epoch: 0025 train_loss= 1.37766 train_acc= 0.32899 val_loss= 1.36104 val_acc= 0.41071 time= 0.01563
Epoch: 0026 train_loss= 1.37699 train_acc= 0.32899 val_loss= 1.36054 val_acc= 0.41071 time= 0.01563
Epoch: 0027 train_loss= 1.37710 train_acc= 0.32899 val_loss= 1.35996 val_acc= 0.41071 time= 0.01563
Epoch: 0028 train_loss= 1.37682 train_acc= 0.32899 val_loss= 1.35943 val_acc= 0.41071 time= 0.01563
Epoch: 0029 train_loss= 1.37704 train_acc= 0.32899 val_loss= 1.35900 val_acc= 0.41071 time= 0.01563
Epoch: 0030 train_loss= 1.37623 train_acc= 0.32899 val_loss= 1.35870 val_acc= 0.41071 time= 0.01563
Epoch: 0031 train_loss= 1.37568 train_acc= 0.32899 val_loss= 1.35859 val_acc= 0.41071 time= 0.01563
Epoch: 0032 train_loss= 1.37610 train_acc= 0.32899 val_loss= 1.35855 val_acc= 0.41071 time= 0.03125
Epoch: 0033 train_loss= 1.37649 train_acc= 0.32899 val_loss= 1.35871 val_acc= 0.41071 time= 0.01563
Epoch: 0034 train_loss= 1.37496 train_acc= 0.32899 val_loss= 1.35885 val_acc= 0.41071 time= 0.01562
Epoch: 0035 train_loss= 1.37600 train_acc= 0.32899 val_loss= 1.35888 val_acc= 0.41071 time= 0.01563
Epoch: 0036 train_loss= 1.37625 train_acc= 0.32899 val_loss= 1.35899 val_acc= 0.41071 time= 0.01563
Epoch: 0037 train_loss= 1.37530 train_acc= 0.32899 val_loss= 1.35906 val_acc= 0.41071 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.39009 accuracy= 0.29204 time= 0.00000 
