title: Trying to use LinearRegressor
body: <p>I'm trying to use the tf.estimator.LinearRegressor in a simple example. The input points are on the line y=2x, but the estimator predicts wrong values. Here's my code:</p>
  
  <pre><code># Create feature column and estimator
  column = tf.feature_column.numeric_column("x", shape=[1])
  lin_reg = tf.estimator.LinearRegressor([column])
  
  # Train the estimator
  train_input = tf.estimator.inputs.numpy_input_fn(
      x={"x": np.array([1.0, 2.0, 3.0, 4.0, 5.0])},
      y=np.array([2.0, 4.0, 6.0, 8.0, 10.0]), shuffle=False)
  lin_reg.train(train_input)
  
   # Make two predictions
   predict_input = tf.estimator.inputs.numpy_input_fn(
       x={"x": np.array([1.9, 1.4], dtype=np.float32)},
       num_epochs=1, shuffle=False)
   results = lin_reg.predict(predict_input)
  
   # Print result
   for value in results:
       print(value['predictions'])
  </code></pre>
  
  <p>The proper output should be 3.8 and 2.8, but the estimator predicts 0.58 and 0.48. Any thoughts?</p>