parameters: (self,
                 model,
                 data,
                 checkpoint_dir,
                 steps=None,
                 max_evaluations=None,
                 callbacks=None)
docstring: Initializes an `SidecarEvaluator` object.
  
      Args:
        model: Model to use for evaluation. The model object used here should be a
          `tf.keras.Model`, and should be the same as the one that is used in
          training, where `tf.keras.Model`s are checkpointed. The model should
          have one or more metrics compiled before using `SidecarEvaluator`.
        data: The input data for evaluation. `SidecarEvaluator` supports all data
          types that Keras `model.evaluate` supports as the input data `x`, such
          as a `tf.data.Dataset`.
        checkpoint_dir: Directory where checkpoint files are saved.
        steps: Number of steps to perform evaluation for, when evaluating a single
          checkpoint file. If `None`, evaluation continues until the dataset is
          exhausted. For repeated evaluation dataset, user must specify `steps` to
          avoid infinite evaluation loop.
        max_evaluations: Maximum number of the checkpoint file to be evaluated,
          for `SidecarEvaluator` to know when to stop. The evaluator will stop
          after it evaluates a checkpoint filepath ending with
          '<ckpt_name>-<max_evaluations>'. If using
          `tf.train.CheckpointManager.save` for saving checkpoints, the kth saved
          checkpoint has the filepath suffix '<ckpt_name>-<k>' (k=1 for the first
          saved), and if checkpoints are saved every epoch after training, the
          filepath saved at the kth epoch would end with '<ckpt_name>-<k>. Thus,
          if training runs for n epochs, and the evaluator should end after the
          training finishes, use n for this parameter. Note that this is not
          necessarily equal to the number of total evaluations, since some
          checkpoints may be skipped if evaluation is slower than checkpoint
          creation. If `None`, `SidecarEvaluator` will evaluate indefinitely, and
          the user must terminate evaluator program themselves.
        callbacks: List of `keras.callbacks.Callback` instances to apply during
          evaluation. See [callbacks](/api_docs/python/tf/keras/callbacks).
docstring_summary: Initializes an `SidecarEvaluator` object.
docstring_tokens: Initializes an SidecarEvaluator object .
function: def __init__(self,
                 model,
                 data,
                 checkpoint_dir,
                 steps=None,
                 max_evaluations=None,
                 callbacks=None):
      """Initializes an `SidecarEvaluator` object.
  
      Args:
        model: Model to use for evaluation. The model object used here should be a
          `tf.keras.Model`, and should be the same as the one that is used in
          training, where `tf.keras.Model`s are checkpointed. The model should
          have one or more metrics compiled before using `SidecarEvaluator`.
        data: The input data for evaluation. `SidecarEvaluator` supports all data
          types that Keras `model.evaluate` supports as the input data `x`, such
          as a `tf.data.Dataset`.
        checkpoint_dir: Directory where checkpoint files are saved.
        steps: Number of steps to perform evaluation for, when evaluating a single
          checkpoint file. If `None`, evaluation continues until the dataset is
          exhausted. For repeated evaluation dataset, user must specify `steps` to
          avoid infinite evaluation loop.
        max_evaluations: Maximum number of the checkpoint file to be evaluated,
          for `SidecarEvaluator` to know when to stop. The evaluator will stop
          after it evaluates a checkpoint filepath ending with
          '<ckpt_name>-<max_evaluations>'. If using
          `tf.train.CheckpointManager.save` for saving checkpoints, the kth saved
          checkpoint has the filepath suffix '<ckpt_name>-<k>' (k=1 for the first
          saved), and if checkpoints are saved every epoch after training, the
          filepath saved at the kth epoch would end with '<ckpt_name>-<k>. Thus,
          if training runs for n epochs, and the evaluator should end after the
          training finishes, use n for this parameter. Note that this is not
          necessarily equal to the number of total evaluations, since some
          checkpoints may be skipped if evaluation is slower than checkpoint
          creation. If `None`, `SidecarEvaluator` will evaluate indefinitely, and
          the user must terminate evaluator program themselves.
        callbacks: List of `keras.callbacks.Callback` instances to apply during
          evaluation. See [callbacks](/api_docs/python/tf/keras/callbacks).
      """
      self.model = model
      self.data = data
      self.checkpoint_dir = checkpoint_dir
      self._iterations = variables.Variable(
          name='iterations',
          initial_value=_ITERATIONS_UNINITIALIZED,
          dtype=dtypes.int64)
      self.max_evaluations = max_evaluations
      self.steps = steps
      self.callbacks = callbacks or []
function_tokens: def __init__ ( self , model , data , checkpoint_dir , steps = None , max_evaluations = None , callbacks = None ) : self . model = model self . data = data self . checkpoint_dir = checkpoint_dir self . _iterations = variables . Variable ( name = 'iterations' , initial_value = _ITERATIONS_UNINITIALIZED , dtype = dtypes . int64 ) self . max_evaluations = max_evaluations self . steps = steps self . callbacks = callbacks or [ ]