Epoch: 0001 train_loss= 1.41565 train_acc= 0.28320 val_loss= 1.38721 val_acc= 0.23214 time= 0.45790
Epoch: 0002 train_loss= 1.40400 train_acc= 0.22461 val_loss= 1.39106 val_acc= 0.21429 time= 0.01562
Epoch: 0003 train_loss= 1.39332 train_acc= 0.30469 val_loss= 1.39667 val_acc= 0.28571 time= 0.03308
Epoch: 0004 train_loss= 1.39389 train_acc= 0.25586 val_loss= 1.40264 val_acc= 0.23214 time= 0.01700
Epoch: 0005 train_loss= 1.38234 train_acc= 0.31055 val_loss= 1.41047 val_acc= 0.28571 time= 0.01711
Epoch: 0006 train_loss= 1.38198 train_acc= 0.32031 val_loss= 1.41881 val_acc= 0.26786 time= 0.01613
Epoch: 0007 train_loss= 1.37933 train_acc= 0.28516 val_loss= 1.42873 val_acc= 0.25000 time= 0.01400
Epoch: 0008 train_loss= 1.39223 train_acc= 0.25781 val_loss= 1.43644 val_acc= 0.28571 time= 0.01800
Epoch: 0009 train_loss= 1.38184 train_acc= 0.30469 val_loss= 1.44432 val_acc= 0.32143 time= 0.01500
Epoch: 0010 train_loss= 1.38425 train_acc= 0.30273 val_loss= 1.44838 val_acc= 0.32143 time= 0.01500
Epoch: 0011 train_loss= 1.38581 train_acc= 0.30273 val_loss= 1.44895 val_acc= 0.33929 time= 0.01492
Epoch: 0012 train_loss= 1.38701 train_acc= 0.29883 val_loss= 1.44895 val_acc= 0.33929 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.40121 accuracy= 0.31858 time= 0.00000 
