title: How to Switch from Keras Tensortype to numpy array for a custom layer?
body: <p>So I have a custom layer, that does not have any weights. 
  In a fist step, I tried to implement the functions manipulating the input tensors in Kers. But I did not succeed because of many reasons. My second approach was to implement the functions with numpy operations, since the custom layer I am implementing does not have any weights, from my understanding, I would say, that I could use numpy operarations, as I don't need backpropagation, since there are no weights, right? And then, I would just convert the output of my layer to a tensor with:</p>
  
  <pre><code> Keras.backend.variable(value = output) 
  </code></pre>
  
  <p>So the main idea is to implement a custom layer, that takes tensors, convert them to numpy arrays, operate on them with numpy operations, then convert the output to a tensor.
  The problem is that I seem not to be able to use .eval() in order to convert the input tensors of my layer into numpy arrays, so that they could be manipulated with numpy operations.  </p>
  
  <p>Can anybody tell, how I can get around this problem ? </p>