title: Is it possible using Tensorflow to create a neural network for input/output mapping?
body: <p>I am currently using tensorflow to create a neural network, that replicates the function of creating a certain output given an input. </p>
  
  <p>The input in this case is a sampled audio, and the audio is generating MFCC features. Know for each file what the corresponding MFCC feature, is, but aren't sure how i should setup the neural network. </p>
  
  <p>I am following this guide/tutorial <a href="http://www.kdnuggets.com/2016/09/urban-sound-classification-neural-networks-tensorflow.html/2" rel="nofollow">http://www.kdnuggets.com/2016/09/urban-sound-classification-neural-networks-tensorflow.html/2</a></p>
  
  <p>It which it says that the neural network is setup as such</p>
  
  <pre><code>training_epochs = 5000
  n_dim = tr_features.shape[1]
  n_classes = 10
  n_hidden_units_one = 280 
  n_hidden_units_two = 300
  sd = 1 / np.sqrt(n_dim)
  learning_rate = 0.01
  </code></pre>
  
  <p>My question here is how i define the number of classes? I mean, the real values I've computed aren't divided into classes, but is a decimal number, so should I just create multiple networks with different number of classes, and choose the one which has the smallest error compared to the original value, or is there a tensorflow command that can do that, as I am doing supervised learning..</p>