Epoch: 0001 train_loss= 1.39079 train_acc= 0.31055 val_loss= 1.38832 val_acc= 0.23214 time= 0.25002
Epoch: 0002 train_loss= 1.38899 train_acc= 0.31055 val_loss= 1.38618 val_acc= 0.23214 time= 0.01563
Epoch: 0003 train_loss= 1.38776 train_acc= 0.31055 val_loss= 1.38480 val_acc= 0.23214 time= 0.01563
Epoch: 0004 train_loss= 1.38651 train_acc= 0.31055 val_loss= 1.38352 val_acc= 0.23214 time= 0.01563
Epoch: 0005 train_loss= 1.38585 train_acc= 0.31055 val_loss= 1.38239 val_acc= 0.23214 time= 0.01563
Epoch: 0006 train_loss= 1.38471 train_acc= 0.31055 val_loss= 1.38142 val_acc= 0.23214 time= 0.01563
Epoch: 0007 train_loss= 1.38389 train_acc= 0.31055 val_loss= 1.38065 val_acc= 0.23214 time= 0.01563
Epoch: 0008 train_loss= 1.38259 train_acc= 0.31055 val_loss= 1.38014 val_acc= 0.23214 time= 0.03125
Epoch: 0009 train_loss= 1.38211 train_acc= 0.31055 val_loss= 1.37993 val_acc= 0.23214 time= 0.01563
Epoch: 0010 train_loss= 1.38250 train_acc= 0.31055 val_loss= 1.38003 val_acc= 0.23214 time= 0.01563
Epoch: 0011 train_loss= 1.38178 train_acc= 0.31055 val_loss= 1.38043 val_acc= 0.23214 time= 0.01563
Epoch: 0012 train_loss= 1.38096 train_acc= 0.31055 val_loss= 1.38102 val_acc= 0.23214 time= 0.01563
Epoch: 0013 train_loss= 1.38098 train_acc= 0.31055 val_loss= 1.38174 val_acc= 0.23214 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38404 accuracy= 0.29204 time= 0.00000 
