title: usage of tf.keras.layers.DenseFeatures
body: <p>Here is the official doc.</p>
  <pre><code>A layer that produces a dense Tensor based on given feature_columns.
  
  Inherits From: DenseFeatures
  
  tf.keras.layers.DenseFeatures(
      feature_columns, trainable=True, name=None, **kwargs
  )
  </code></pre>
  <p>This is used in TF example and usually put in keras.Sequential(...) model construction. Like below:</p>
  <pre><code>model = tf.keras.Sequential([
    feature_layer,
    layers.Dense(128, activation='relu'),
    layers.Dense(128, activation='relu'),
    layers.Dropout(.1),
    layers.Dense(1)
  ])
  </code></pre>
  <p>In my case, I want to use it to transfer my dictionary data type into Tensor format and pass it into model. So I used code like below:</p>
  <pre><code>feature_columns = []
  bins = [-125, -75, -50, -25, 0, 25, 50, 75, 125]
  temp_num = feature_column.numeric_column('temp')
  temp_buckets = feature_column.bucketized_column(temp_num, boundaries=bins)
  feature_columns.append(temp_buckets)
  feature_layer = layers.DenseFeatures(feature_columns) 
  input = feature_layer(dict(dataframe))
  </code></pre>
  <p>And input is the training data I would feed into model. The question is whether my usage of this DenseFeatures() layer is reasonable. Or this feature_layer has to be in keras.Model class?</p>