title: Difference between feature_column.embedding_column and keras.layers.Embedding in TensorFlow
body: <p>I have been using <strong>keras.layers.Embedding</strong> for almost all of my projects. But, recently I wanted to fiddle around with tf.data and found <strong>feature_column.embedding_column</strong>.</p>
  
  <p>From the documentation:</p>
  
  <p><strong>feature_column.embedding_column</strong> - 
  <code>DenseColumn</code> that converts from sparse, categorical input.
    Use this when your inputs are sparse, but you want to convert them to a dense
    representation (e.g., to feed to a DNN).</p>
  
  <p><strong>keras.layers.Embedding</strong> - Turns positive integers (indexes) into dense vectors of fixed size.
    e.g. <code>[[4], [20]] -&gt; [[0.25, 0.1], [0.6, -0.2]]</code>
    This layer can only be used as the first layer in a model.</p>
  
  <p>My question is, is both of the api doing similar thing on different type of input data(for ex. input - [0,1,2] for keras.layers.Embedding and its one-hot-encoded rep. [[1,0,0],[0,1,0],[0,0,1] for feature_column.embedding_column)?</p>