Epoch: 0001 train_loss= 1.39404 train_acc= 0.29316 val_loss= 1.39158 val_acc= 0.28571 time= 0.18601
Epoch: 0002 train_loss= 1.39089 train_acc= 0.29642 val_loss= 1.38979 val_acc= 0.28571 time= 0.02100
Epoch: 0003 train_loss= 1.38790 train_acc= 0.29642 val_loss= 1.38887 val_acc= 0.28571 time= 0.00938
Epoch: 0004 train_loss= 1.38580 train_acc= 0.30293 val_loss= 1.38873 val_acc= 0.28571 time= 0.00000
Epoch: 0005 train_loss= 1.38399 train_acc= 0.30293 val_loss= 1.38928 val_acc= 0.28571 time= 0.01563
Epoch: 0006 train_loss= 1.38271 train_acc= 0.29642 val_loss= 1.39037 val_acc= 0.28571 time= 0.02505
Epoch: 0007 train_loss= 1.38208 train_acc= 0.30293 val_loss= 1.39177 val_acc= 0.28571 time= 0.00781
Epoch: 0008 train_loss= 1.38158 train_acc= 0.29316 val_loss= 1.39321 val_acc= 0.28571 time= 0.01601
Epoch: 0009 train_loss= 1.38124 train_acc= 0.29642 val_loss= 1.39456 val_acc= 0.28571 time= 0.01300
Epoch: 0010 train_loss= 1.38126 train_acc= 0.29967 val_loss= 1.39561 val_acc= 0.28571 time= 0.00128
Epoch: 0011 train_loss= 1.38111 train_acc= 0.29967 val_loss= 1.39638 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.38121 train_acc= 0.30945 val_loss= 1.39682 val_acc= 0.28571 time= 0.01860
Early stopping...
Optimization Finished!
Test set results: cost= 1.38419 accuracy= 0.30973 time= 0.00202 
