title: Principle of setting 'hash_bucket_size' parameter?
body: <p><strong>Question 1</strong>:</p>
  
  <p>In <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py" rel="noreferrer"><code>wide_n_deep_tutorial.py</code></a>, there is a hyper-parameter named <code>hash_bucket_size</code> for both <code>tf.feature_column.categorical_column_with_hash_bucket</code> and <code>tf.feature_column.crossed_column</code> methods, and the value is <code>hash_bucket_size=1000</code>.</p>
  
  <p>But why 1000? How to set this parameter ?</p>
  
  <p><strong>Question 2</strong>:
  The second question about <code>crossed_columns</code>, that is,</p>
  
  <p><code>crossed_columns = [ 
  tf.feature_column.crossed_column( ["education", "occupation"], hash_bucket_size=1000), 
  tf.feature_column.crossed_column( [age_buckets, "education", "occupation"], hash_bucket_size=1000), 
  tf.feature_column.crossed_column( ["native_country", "occupation"], hash_bucket_size=1000) ]</code></p>
  
  <p>in <code>wide_n_deep_tutorial.py</code>,</p>
  
  <p>Why choose <code>["education", "occupation"]</code>, <code>[age_buckets, "education", "occupation"]</code> and <code>["native_country", "occupation"]</code> as <code>crossed_columns</code>, are there any rule of thumb ?</p>