title: tensorflow: tensor to ndarray conversion doesn't work - python3.x
body: <p>I am working on Tensorflow CNN Model and changed according to my scenario. Previously same code worked well on MNIST dataset but now, after feeding my own dataset a placeholder <code>y_true</code> for labels is causing problem. Code snippet:</p>
  
  <pre><code>x = tf.placeholder("float",shape=[88, 128])
  y_true = tf.placeholder("float",shape=[None, 3])
  </code></pre>
  
  <p><code>y_true</code> in the session gets me error. Code snippet:</p>
  
  <pre><code>sess.run(train,feed_dict={x:batch_x, y_true:batch_y,hold_prob:0.5})
  </code></pre>
  
  <p>Error:</p>
  
  <pre><code>TypeError: The value of a feed cannot be a tf.Tensor object.
  Acceptable feed values include Python scalars, strings, lists, 
  numpy ndarrays, or TensorHandles.
  </code></pre>
  
  <p>I casted it to <code>numpy array</code> via <code>y = np.array(y_true)</code> and tweaked the statement according to <code>y</code> but again error:</p>
  
  <pre><code>TypeError: unhashable type: 'numpy.ndarray'
  </code></pre>
  
  <p>Tried <code>y_true.eval(session = my_sess)</code> as suggested here on stackoverflow but again error:</p>
  
  <pre><code>InvalidArgumentError: You must feed a value for placeholder tensor
  'Placeholder_4' with dtype float and shape [?,3]
  </code></pre>
  
  <p>Any idea? What's wrong here and how to cast/convert it to suitable format.
  Additional info:</p>
  
  <pre><code>batch_x = batch of 50 images (total 200K), 88x128 single channel
  batch_y = labels (string) same batch as above
  </code></pre>