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The ready-to-use DNNClassifier in tf.estimator seems not able to fit these data:

X = [[1,2], [1,12], [1,17], [9,33], [48,49], [48,50]]
Y = [ 1,     1,      1,      1,      2,       3     ]

I've tried with 4 layers but it's fitting to 83% (=5/6 sampes) only:

hidden_units = [2000,1000,500,100]
n_classes    = 4

The sample data above are supposed to be separated by 2 lines (right-click image to open in new tab):

enter image description here

It seems stuck be cause of Y=2 and Y=3 are too close. How to change the DNNClassifier to fit to 100%?

Dan D
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1 Answers1

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Normalise your inputs.

Neural networks work poorly outside of relatively small numerical ranges on input. An ideal range is for each feature to be drawn from $\mathcal{N}(0,1)$ i.e. a Normal distribution with mean $0$ and standard deviation $1$. In your case, divide both parts of $\mathbf{x}$ by $25$ and subtract $1$ would probably suffice.

Your neural network architecture is completely overblown for the problem at hand. That may be because you were trying to force it to fit this data (and failing because of lack of normalisation). Try something more like: hidden_units = [20,10]

Neil Slater
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