title: Layer Normalization, with average power constraints
body: <p>I'm studying the paper "An Introduction to Deep Learning for the Physical Layer". While implementing the proposed network with python Keras, I should normalize some values, output of former layer.<br>
  One way is simple L2 Normalization, ||X||^2 = 1, where X is a tensor of former layer output.<br>
  as a 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 ||X||^2 ≤ 1.<br>
  Is there any way that constrains the value of layer outputs?</p>