title: Tensorflow: How to feed a placeholder variable with a tensor?
body: <p>I have a placeholder variable that expects a batch of input images: </p>
  
  <pre><code>input_placeholder = tf.placeholder(tf.float32, [None] + image_shape, name='input_images')
  </code></pre>
  
  <p>Now I have 2 sources for the input data:<br>
  1) a tensor and<br>
  2) some numpy data.  </p>
  
  <p>For the numpy input data, I know how to feed data to the placeholder variable:</p>
  
  <pre><code>sess = tf.Session()
  mLoss, = sess.run([loss], feed_dict = {input_placeholder: myNumpyData})
  </code></pre>
  
  <p>How can I feed a tensor to that placeholder variable? </p>
  
  <pre><code>mLoss, = sess.run([loss], feed_dict = {input_placeholder: myInputTensor})
  </code></pre>
  
  <p>gives me an error:</p>
  
  <pre><code>TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.
  </code></pre>
  
  <p>I don't want to convert the tensor into a numpy array using <code>.eval()</code>, since that would slow my program down, is there any other way?</p>