title: Tensorflow 1.9 DNNClassifier unequal output labels optimization
body: <p>I have a classification problem which requires some optimization, as my results are not quite adequate.
  I'm using the DNNClassifier for a huge dataset in order to classify items in 5 different classes (labels). I have over 2000 distinct items (in a hashbucket column with size 2000 and dims 6 - is this adequate?) and multiple numeric columns for said classification.
  My problem is the following: the amount of items belonging in each class is very variable. Class 1 is very common, class 2 is common but classes 3 4 and 5 are highly uncommon (under 2% of the dataset) but they are the most interesting ones in my test case. Even if I tweak the network size/number of neurons or the training epoch, I get close to no results in classes 3, 4 and 5, so class 1 and 2 are clearly overfitted.
  I saw the weight_column option in the documentation - would that be a good idea to change the learning weight of these three class to "normalize" the weight in each class ? Is there a more efficient way to get better results on rarer cases without losing the detection precision on the common classes?
  Many thanks!</p>