title: Why is does pad_sequences necessary when one_hot encoding is used?
body: <p>In Keras, I can have the following code:</p>
  
  <pre><code>docs                                                                                                                                                                                                                                                                                                                                                                
  Out[9]: 
  ['Well done!',
   'Good work',
   'Great effort',
   'nice work',
   'Excellent!',
   'Weak',
   'Poor effort!',
   'not good',
   'poor work',
   'Could have done better.']
  
  labels = array([1,1,1,1,1,0,0,0,0,0]) 
  voc_size = 50
  encoded = [one_hot(d, voc_size) for d in docs] 
  max_length = 4 
  padded_docs = pad_sequences(encoded, maxlen=max_length, padding='post')  
  </code></pre>
  
  <p>My understanding is that, the 'one_hot' encoding already creates an equal length of each doc based on the vocabulary size. So why does each doc need to be padded again?</p>
  
  <p>EDIT: another example for more clarification:</p>
  
  <p>A one-hot encoding is a representation of categorical variables (e.g. cat, dog, rat) as binary vectors (e.g. [1,0,0], [0,1,0], [0,0,1]).</p>
  
  <p>So in this case, cat, dog and rat are encoded as equal length of vector. How is this different from the example above?</p>