Epoch: 0001 train_loss= 0.69726 train_acc= 0.53636 val_loss= 0.70718 val_acc= 0.40984 time= 0.09376
Epoch: 0002 train_loss= 0.69564 train_acc= 0.53939 val_loss= 0.71154 val_acc= 0.40984 time= 0.01563
Epoch: 0003 train_loss= 0.69478 train_acc= 0.53939 val_loss= 0.71627 val_acc= 0.40984 time= 0.00000
Epoch: 0004 train_loss= 0.69348 train_acc= 0.53636 val_loss= 0.72097 val_acc= 0.40984 time= 0.01563
Epoch: 0005 train_loss= 0.69200 train_acc= 0.53939 val_loss= 0.72568 val_acc= 0.40984 time= 0.01563
Epoch: 0006 train_loss= 0.69226 train_acc= 0.53939 val_loss= 0.73007 val_acc= 0.40984 time= 0.00000
Epoch: 0007 train_loss= 0.69201 train_acc= 0.53939 val_loss= 0.73416 val_acc= 0.40984 time= 0.01563
Epoch: 0008 train_loss= 0.69136 train_acc= 0.53939 val_loss= 0.73747 val_acc= 0.40984 time= 0.01563
Epoch: 0009 train_loss= 0.69220 train_acc= 0.53939 val_loss= 0.73937 val_acc= 0.40984 time= 0.01563
Epoch: 0010 train_loss= 0.69161 train_acc= 0.53939 val_loss= 0.74004 val_acc= 0.40984 time= 0.01562
Epoch: 0011 train_loss= 0.69080 train_acc= 0.53939 val_loss= 0.73976 val_acc= 0.40984 time= 0.00000
Epoch: 0012 train_loss= 0.69197 train_acc= 0.53939 val_loss= 0.73841 val_acc= 0.40984 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69636 accuracy= 0.52459 time= 0.01563 
