title: Layer Normalization with Python Keras
body: <p>I'm studying the paper "An Introduction to Deep Learning for the Physical Layer".<br/> While implementing the proposed network with <code>python keras</code>, I should normalize output of some layer.</p>
  
  <p>One way is simple L2 Normalization (<code>||X||^2 = 1</code>), where X is a tensor of former layer output. I can implement simple L2 Normalization by the following code:</p>
  
  <pre><code>from keras import backend as K
  Lambda(lambda x: K.l2_normalize(x,axis=1))
  </code></pre>
  
  <p>The other way, what I want to know, is <code>||X||^2 ≤ 1</code>.
  Is there any way that constrains the value of layer outputs?</p>