title: TypeError: Signature mismatch. Keys must be dtype <dtype: 'string'>, got <dtype:'int64'>
body: <p>While running the <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py" rel="nofollow noreferrer">wide_n_deep_tutorial</a> program from TensorFlow on my dataset, the following error is displayed.</p>
  
  <pre><code>"TypeError: Signature mismatch. Keys must be dtype &lt;dtype: 'string'&gt;, got &lt;dtype:'int64'&gt;"
  </code></pre>
  
  <p><img src="https://i.stack.imgur.com/aqs3x.png" alt="enter image description here"></p>
  
  <p>Following is the code snippet:</p>
  
  <pre><code> def input_fn(df):
    """Input builder function."""
    # Creates a dictionary mapping from each continuous feature column name (k) to
    # the values of that column stored in a constant Tensor.
    continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS}
    # Creates a dictionary mapping from each categorical feature column name (k)
    # to the values of that column stored in a tf.SparseTensor.
    categorical_cols = {k: tf.SparseTensor(
        indices=[[i, 0] for i in range(df[k].size)],
        values=df[k].values,
        shape=[df[k].size, 1])
                        for k in CATEGORICAL_COLUMNS}
  
    # Merges the two dictionaries into one.
    feature_cols = dict(continuous_cols)
    feature_cols.update(categorical_cols)
    # Converts the label column into a constant Tensor.
    label = tf.constant(df[LABEL_COLUMN].values)
    # Returns the feature columns and the label.
  
    return feature_cols, label
  
  
  
  def train_and_eval():
    """Train and evaluate the model."""
    train_file_name, test_file_name = maybe_download()
  
    df_train=train_file_name
    df_test=test_file_name
  
    df_train[LABEL_COLUMN] = (
        df_train["impression_flag"].apply(lambda x: "generated" in x)).astype(str)
  
    df_test[LABEL_COLUMN] = (
        df_test["impression_flag"].apply(lambda x: "generated" in x)).astype(str)
  
    model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir
    print("model directory = %s" % model_dir)
  
    m = build_estimator(model_dir)
    print('model succesfully build!')
    m.fit(input_fn=lambda: input_fn(df_train), steps=FLAGS.train_steps)
    print('model fitted!!')
    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>Any help is appreciated.</p>