message: Updates to the wide_deep tutorials to keep up with the code. Goes with
diff: @@ -215,26 +215,18 @@ Before we train the model, let's read in the Census dataset as we did in the
  After reading in the data, you can train and evaluate the model:
  ```python
  -# Set up input function generators for the train and test data files.
  -train_input_fn = input_fn(
  - data_file=FLAGS.train_data,
  - num_epochs=FLAGS.epochs_per_eval,
  - shuffle=True,
  - batch_size=FLAGS.batch_size)
  -eval_input_fn = input_fn(
  - data_file=FLAGS.test_data,
  - num_epochs=1,
  - shuffle=False,
  - batch_size=FLAGS.batch_size)
  -
  # Train and evaluate the model every `FLAGS.epochs_per_eval` epochs.
  for n in range(FLAGS.train_epochs // FLAGS.epochs_per_eval):
  - model.train(input_fn=train_input_fn)
  - results = model.evaluate(input_fn=eval_input_fn)
  + model.train(input_fn=lambda: input_fn(
  + FLAGS.train_data, FLAGS.epochs_per_eval, True, FLAGS.batch_size))
  +
  + results = model.evaluate(input_fn=lambda: input_fn(
  + FLAGS.test_data, 1, False, FLAGS.batch_size))
  # Display evaluation metrics
  print('Results at epoch', (n + 1) * FLAGS.epochs_per_eval)
  print('-' * 30)
  +
  for key in sorted(results):
  print('%s: %s' % (key, results[key]))
  ```
  