title: TypeError: argument of type 'float' is not iterable-Tensorflow wide_n_deep_tutorial
body: <p>I am facing an issue while running the wide_n_deep_tutorial program of TensorFlow <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py" rel="nofollow">https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py</a> on my personal data set with variation is the parameters. I am loading my data from S3.</p>
  
  <p>My target variable is "impression_flag" which the takes the value of either "TRUE" or "FALSE". Below is the code snippet of the train_and_eval method:</p>
  
  <pre><code>    def train_and_eval():
    """Train and evaluate the model."""
    train_file_name, test_file_name = maybe_download()
    df_train = pd.read_csv(
        tf.gfile.Open(train_file_name),
        names=COLUMNS,
        skipinitialspace=True)
    df_test = pd.read_csv(
        tf.gfile.Open(test_file_name),
        names=COLUMNS,
        skipinitialspace=True,
        skiprows=1)
    df_train[LABEL_COLUMN] = (
        df_train["impression_flag"].apply(lambda x: "TRUE" in x)).astype(int)
    df_test[LABEL_COLUMN] = (
        df_test["impression_flag"].apply(lambda x: "TRUE" in x)).astype(int)
  
    model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir
    print("model directory = %s" % model_dir)
  
    m = build_estimator(model_dir)
    m.fit(input_fn=lambda: input_fn(df_train), steps=FLAGS.train_steps)
    results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)
    for key in sorted(results):
      print("%s: %s" % (key, results[key]))
  </code></pre>
  
  <p>While running the code, an error "Type Error: argument of type 'float' is not iterable" is displayed. The following is the screenshot of the error.
  <a href="http://i.stack.imgur.com/OMfZL.png" rel="nofollow">enter image description here</a></p>
  
  <p>Any help would be appreciated!</p>