title: Passing tensorflow placeholders as function parameters
body: <p>I am passing two parameters to a my (loss) function in tensorflow which (I think) should be in form of placeholders because they change for different steps. I feed them on during training. 
  My program outline is as follows. 
  My question is do they take the values I feed them effectively?
  I would appreciate if you can take a look at the following snippet and tell that I am doing it right?
  I don't get any error or something by the way. </p>
  
  <pre><code>tetha1_placeholder, tetha2_placeholder = tf.placeholder(tf.float32, name='tetha1plh'), tf.placeholder(tf.float32, name='tetha2plh')
  hyperparams = {'tetha1': tetha1_placeholder,'tetha2':tetha2_placeholder} 
  [getting embeddings1,embeddings2, embeddings3 from my model]
  loss = loss_function (embeddings1,embeddings1,embeddings3, hyperparams) 
  
  with sess.as_default():
  
     while true:
          step = sess.run(global_step, feed_dict=None)
          t1, t2 = calculate_params(step)
          feed_dict = {tetha1_placeholder:t1, tetha2_placeholder:t2}            
          error=sess.run([loss], feed_dict=feed_dict)
  
  def loss_function (embeddings1,embeddings2,embeddings3, hyperparams):
         pos_dist =hyperparams['tetha1'] * tf.reduce_sum(tf.square(tf.subtract(embeddings1, embeddings2)), 1)
         neg_dist = hyperparams['tetha2'] *tf.reduce_sum(tf.square(tf.subtract(embeddings1, embeddings3)), 1)
         loss = tf.reduce_mean(tf.add(pos_dist,neg_dist))
         return loss
  </code></pre>