title: check if tensorflow placeholder is filled
body: <p>Suppose I have two placeholder quantities in tensorflow: placeholder_1 and placeholder_2. Essentially I would like the following computational functionality: "if placeholder_1 is defined (ie is given a value in the feed_dict of sess.run()), compute X as f(placeholder_1), otherwise, compute X as g(placeholder_2)." Think of X as being a hidden layer in a neural network that can optionally be computed in these two different ways. Eventually I would use X to produce an output, and I'd like to backpropagate error to the parameters of f or g depending on which placeholder I used. </p>
  
  <p>One could accomplish this using the tf.where(condition, x, y) function if there was a way to make the condition "placeholder_1 has a value", but after looking through the tensorflow documentation on booleans and asserts I couldn't find anything that looked applicable.</p>
  
  <p>Any ideas? I have a vague idea of how I could accomplish this basically by copying part of the network, sharing parameters and syncing the networks after updates, but I'm hoping for a cleaner way to do it.</p>