Answers.Body: <p>I think the answer to your question would be "yes". Though inference would always be best if you provide representative training data. For example you can train your net with <code>(c, b)</code> pairs, <code>(blank, b)</code> pairs, and <code>(c, blank)</code> pairs. That would make the net more robust and likely to support your use case. Training separate nets for each case would be more efficient and accurate - but I'm not sure what your goals and constraints are.</p>
  
  <p>The point is a "blank" input can be trained for as well in the same net. In your case it's a new kind of <code>body_height</code> or <code>color_of_clothing</code>. I also suspect you might have issues with how you encode <code>blank</code> because e.g. <code>rgb(0, 0, 0)</code> would be black, and perhaps you intended to model <code>blank</code> as zero which would mean a blank is black.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-01T07:12:27.210Z
Answers.LastActivityDate: 2018-08-01T07:12:27.210Z
Answers.UserDisplayName: ubershmekel
Body: <p>Say I'm training a neural net to compute the following function:</p>
  
  <pre><code>(color_of_clothing, body_height) -&gt; gender
  </code></pre>
  
  <p>When using this network for prediction, I can obviously plug in a pair <code>(c, b)</code> to receive a predicted <code>g</code>, but say I want to get a prediction only based on <code>c</code> or only based on <code>b</code>, can I use the same neural net somehow? Or would I need to train two separate neural nets <code>c -&gt; g</code> and <code>b -&gt; g</code> previously?</p>
  
  <p>Or more generally, can I use a neural net that was trained to predict <code>A -&gt; B</code> to make predictions on values from a subset of <code>A</code>, or should I train separate neural nets on all subsets of <code>A</code> that I'm interested in?</p>
  
Tags: <neural-networks>
Title: Dealing with "blank" inputs in prediction of a neural network?
UserDisplayName: Oliver Friedmann
ThreadText: # Dealing with "blank" inputs in prediction of a neural network?
  
  (Asked by: username_0 on 2018-07-31)
  
  Say I'm training a neural net to compute the following function:
  
  ```
  (color_of_clothing, body_height) -> gender
  
  ```
  
  When using this network for prediction, I can obviously plug in a pair `(c, b)` to receive a predicted `g`, but say I want to get a prediction only based on `c` or only based on `b`, can I use the same neural net somehow? Or would I need to train two separate neural nets `c -> g` and `b -> g` previously?
  
  Or more generally, can I use a neural net that was trained to predict `A -> B` to make predictions on values from a subset of `A`, or should I train separate neural nets on all subsets of `A` that I'm interested in?
  
  **Tags:** neural-networks
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-08-01. Score: 0 | Accepted answer
  
  I think the answer to your question would be "yes". Though inference would always be best if you provide representative training data. For example you can train your net with `(c, b)` pairs, `(blank, b)` pairs, and `(c, blank)` pairs. That would make the net more robust and likely to support your use case. Training separate nets for each case would be more efficient and accurate - but I'm not sure what your goals and constraints are.
  
  The point is a "blank" input can be trained for as well in the same net. In your case it's a new kind of `body_height` or `color_of_clothing`. I also suspect you might have issues with how you encode `blank` because e.g. `rgb(0, 0, 0)` would be black, and perhaps you intended to model `blank` as zero which would mean a blank is black.
  
  
  ---
  
  