check if tensorflow placeholder is filled
You can create a third placeholder variable of type boolean to select which branch to use and feed that in at run time.
The logic behind it is that since you are feeding in the placholders at runtime anyways you can determine outside of tensorflow which placeholders will be fed.
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. 
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.
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.