Class Imbalence Problem even after Balancing Data
The fact tha balancing the dataset Will prevent the overfitting and thus, good results in the test set, Works under the assumption that the model does not underfit. 

What it may be happening is that the model is too bad that it can't perform the classification task in either the un-balanced and the balanced dataset. 

It's difficult to analyse the problema without more information, you may have facing convergence issues (too large learning rate, vanishing gradients), you may also be computing the accuracy in a bad way...