First of all, you probably want two classes. Solving a problem with one class is impossible, meaning that you always have at least two classes. In your cases: an element has a class (class 1), or it hasn’t (class 2). So num classes should be two. Make sure that your labels are constructed correctly.
Also, loss flattening means that the network is learning, which is a good thing. However, because you are not running on a test set, it is very likely that your model just overfits. You can Google ‘prevent overfitting’ and you’ll find solutions such as dropout.