# How to use real data for neural nets without clear input boundaries

I'm trying to use a neural net for classification and I am getting stumped on how to use it for noisy data. My problem seems simple; I have 1000's of experiments that have features and these features either hit a threshold or don't. I know if the experiments are success' or not, but since the data is noisy I can have two inputs map to different outputs, e.g.,

[1,1,0,0,0,1] -> 1
[1,1,0,0,0,1] -> 0


What I don't understand is how to use neural nets when I have thousands of runs to classify. Most of the examples online deal with XOR functions where there is a 1-1 mapping between input and output like here. How can I use neural nets to classify these inputs where the maps are not 1-1?

If I wanted to use the code in the link how can I do it? The weights would correspond to the same input, no? For example, how would I construct a neural net that could be written as this:

   nn = NeuralNetwork([6,2,1])
X = np.array([[1,1,0,0,0,1],
[1,1,0,0,0,1],
[1,1,0,0,0,1],
[1,1,0,0,0,1],
[1,1,0,0,0,1],
...])
y = np.array([1, 0, 0, 1, 0,...])
nn.fit(X, y)
for e in X:
print(e,nn.predict(e))


where the ... means more data and of course, I have other inputs and outputs, [1,1,1,1,1,1] -> 1, etc.

• Neural networks don't have to be only for perfectly separable data. Commented Oct 24, 2016 at 18:11
• @gung is correct. There is no specific requirement that training data be purely-separable. If the data are noisy, this will manifest as more uncertainty in particular decision regions, but this is no different from any other classifier. Commented Oct 24, 2016 at 18:16

• thanks for the reply. My training set is in the thousands and contains all variations of 1's and 0's, I was simply showing the basic code. Let me ask it this way: Let's say my input is [0,1],[0,0],[1,1],[1,0],[1,1] and my corresponding output is [1,0,0,0,1]. The output from the neural net will be the weights. What is the interpretation for the two weights that correspond to the two [1,1] instances? Commented Oct 24, 2016 at 18:41