message: Add example code snippets to tf.contrib.saved_model.(save|load)_keras_model
diff: @@ -73,6 +73,25 @@ def save_keras_model(
  share variables. To use the train graph with evaluation or prediction graphs,
  create a new checkpoint if variable values have been updated.
  + Example:
  +
  + ```python
  + import tensorflow as tf
  +
  + # Create a tf.keras model.
  + model = tf.keras.Sequential()
  + model.add(tf.keras.layers.Dense(1, input_shape=[10]))
  + model.summary()
  +
  + # Save the tf.keras model in the SavedModel format.
  + saved_to_path = tf.contrib.saved_model.save_keras_model(
  + model, '/tmp/my_simple_tf_keras_saved_model')
  +
  + # Load the saved keras model back.
  + model_prime = tf.contrib.saved_model.load_keras_model(saved_to_path)
  + model_prime.summary()
  + ```
  +
  Args:
  model: A `tf.keras.Model` to be saved.
  saved_model_path: a string specifying the path to the SavedModel directory.
  @@ -303,6 +322,25 @@ def load_keras_model(saved_model_path):
  from metagraph).
  2) loading model weights from checkpoint.
  + Example:
  +
  + ```python
  + import tensorflow as tf
  +
  + # Create a tf.keras model.
  + model = tf.keras.Sequential()
  + model.add(tf.keras.layers.Dense(1, input_shape=[10]))
  + model.summary()
  +
  + # Save the tf.keras model in the SavedModel format.
  + saved_to_path = tf.contrib.saved_model.save_keras_model(
  + model, '/tmp/my_simple_tf_keras_saved_model')
  +
  + # Load the saved keras model back.
  + model_prime = tf.contrib.saved_model.load_keras_model(saved_to_path)
  + model_prime.summary()
  + ```
  +
  Args:
  saved_model_path: a string specifying the path to an existing SavedModel.
  