message: I would like to clarify checkpoint is not a file. I also changed some
  wording which may lead readers to use a physical file name in their code.
diff: @@ -33,7 +33,7 @@ roughly speaking, map variable names to tensor values.
  Create a `Saver` with `tf.train.Saver()` to manage all variables in the
  model. For example, the following snippet demonstrates how to call the
  -`tf.train.Saver.save` method to save variables to a checkpoint file:
  +`tf.train.Saver.save` method to save variables to checkpoint files:
  ```python
  # Create some variables.
  @@ -58,7 +58,7 @@ with tf.Session() as sess:
  dec_v2.op.run()
  # Save the variables to disk.
  save_path = saver.save(sess, "/tmp/model.ckpt")
  - print("Model saved in file: %s" % save_path)
  + print("Model saved in path: %s" % save_path)
  ```
  @@ -66,10 +66,10 @@ with tf.Session() as sess:
  ### Restoring variables
  The `tf.train.Saver` object not only saves variables to checkpoint files, it
  -also restores variables. Note that when you restore variables from a file you
  -do not have to initialize them beforehand. For example, the following snippet
  -demonstrates how to call the `tf.train.Saver.restore` method to restore
  -variables from a checkpoint file:
  +also restores variables. Note that when you restore variables you do not have
  +to initialize them beforehand. For example, the following snippet demonstrates
  +how to call the `tf.train.Saver.restore` method to restore variables from the
  +checkpoint files:
  ```python
  tf.reset_default_graph()
  @@ -92,6 +92,12 @@ with tf.Session() as sess:
  print("v2 : %s" % v2.eval())
  ```
  +Notes:
  +
  +* There is not a physical file called "/tmp/model.ckpt". It is the **prefix**
  + of filenames created for the checkpoint. Users only interact with the
  + prefix instead of physical checkpoint files.
  +
  ### Choosing which variables to save and restore
  