message: Describe the `ModelCheckpoint` `monitor` argument.
diff: @@ -1143,15 +1143,22 @@ class ModelCheckpoint(Callback):
  the end of every epoch, or after a fixed number of training batches.
  - Whether only weights are saved, or the whole model is saved.
  + Note: If you get `WARNING:tensorflow:Can save best model only with <name>
  + available, skipping` see the description of the `monitor` argument for
  + details on how to get this right.
  +
  Example:
  ```python
  + model.compile(loss=..., optimizer=...,
  + metrics=['accuracy'])
  +
  EPOCHS = 10
  checkpoint_filepath = '/tmp/checkpoint'
  model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
  filepath=checkpoint_filepath,
  save_weights_only=True,
  - monitor='val_acc',
  + monitor='val_accuracy',
  mode='max',
  save_best_only=True)
  @@ -1170,7 +1177,20 @@ class ModelCheckpoint(Callback):
  `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then the model
  checkpoints will be saved with the epoch number and the validation loss
  in the filename.
  - monitor: quantity to monitor.
  + monitor: The metric name to monitor. Typically the metrics are set by the
  + `Model.compile` method. Note:
  +
  + * Prefix the name with `"val_`" to monitor validation metrics.
  + * Use `"loss"` or "`val_loss`" to monitor the model's total loss.
  + * If you specify metrics as strings, like `"accuracy"`, pass the same
  + string (with or without the `"val_"` prefix).
  + * If you pass `metrics.Metric` objects, `monitor` should be set to
  + `metric.name`
  + * If you're not sure about the metric names you can check the contents
  + of the `history.history` dictionary returned by
  + `history = model.fit()`
  + * Multi-output models set additional prefixes on the metric names.
  +
  verbose: verbosity mode, 0 or 1.
  save_best_only: if `save_best_only=True`, it only saves when the model
  is considered the "best" and the latest best model according to the
  