title: TypeError: Output tensors to a Model must be Keras tensors
body: <p>I want to take an input image <code>img</code> (which also has negative values) and feed it into two activation layers. However, I want to make a simple transformation e.g. multiply the whole image with <code>-1.0</code>:</p>
  
  <pre class="lang-py prettyprint-override"><code>left = Activation('relu')(img)
  right = Activation('relu')(tf.mul(img, -1.0))
  </code></pre>
  
  <p>If I do it this way I am getting:</p>
  
  <pre><code>TypeError: Output tensors to a Model must be Keras tensors. Found: Tensor("add_1:0", shape=(?, 5, 1, 3), dtype=float32)
  </code></pre>
  
  <p>and I am not sure how I can fix that. Is there a <code>Keras</code> side <code>mul()</code> method that I can use for such a thing? Or can I wrap the result of <code>tf.mul(img, -1.0)</code> somehow such that I can pass it on to <code>Activation</code>?</p>
  
  <p><strong>Please note:</strong> The negative values may be important. Thus transforming the image s.t. the minimum is simply <code>0.0</code> is not a solution here.</p>
  
  <hr>
  
  <p>I am getting the same error for</p>
  
  <pre class="lang-py prettyprint-override"><code>left = Activation('relu')(conv)
  right = Activation('relu')(-conv)
  </code></pre>
  
  <hr>
  
  <p>The same error for:</p>
  
  <pre class="lang-py prettyprint-override"><code>import tensorflow as tf
  
  minus_one = tf.constant([-1.])
  
  # ...
  
      right = merge([conv, minus_one], mode='mul')
  </code></pre>