parameters: (self,
                 x=None,
                 y=None,
                 input_fn=None,
                 feed_fn=None,
                 batch_size=None,
                 steps=None,
                 metrics=None,
                 name=None,
                 checkpoint_path=None,
                 hooks=None)
docstring: Evaluates given model with provided evaluation data.
  
      Stop conditions - we evaluate on the given input data until one of the
      following:
      - If `steps` is provided, and `steps` batches of size `batch_size` are
      processed.
      - If `input_fn` is provided, and it raises an end-of-input
      exception (`OutOfRangeError` or `StopIteration`).
      - If `x` is provided, and all items in `x` have been processed.
  
      The return value is a dict containing the metrics specified in `metrics`, as
      well as an entry `global_step` which contains the value of the global step
      for which this evaluation was performed.
  
      Args:
        x: Matrix of shape [n_samples, n_features...] or dictionary of many matrices
          containing the input samples for fitting the model. Can be iterator that returns
          arrays of features or dictionary of array of features. If set, `input_fn` must
          be `None`.
        y: Vector or matrix [n_samples] or [n_samples, n_outputs] containing the
          label values (class labels in classification, real numbers in
          regression) or dictionary of multiple vectors/matrices. Can be iterator
          that returns array of targets or dictionary of array of targets. If set,
          `input_fn` must be `None`. Note: For classification, label values must
          be integers representing the class index (i.e. values from 0 to
          n_classes-1).
        input_fn: Input function returning a tuple of:
          features - Dictionary of string feature name to `Tensor` or `Tensor`.
          labels - `Tensor` or dictionary of `Tensor` with labels.
          If input_fn is set, `x`, `y`, and `batch_size` must be `None`. If
          `steps` is not provided, this should raise `OutOfRangeError` or
          `StopIteration` after the desired amount of data (e.g., one epoch) has
          been provided. See "Stop conditions" above for specifics.
        feed_fn: Function creating a feed dict every time it is called. Called
          once per iteration. Must be `None` if `input_fn` is provided.
        batch_size: minibatch size to use on the input, defaults to first
          dimension of `x`, if specified. Must be `None` if `input_fn` is
          provided.
        steps: Number of steps for which to evaluate model. If `None`, evaluate
          until `x` is consumed or `input_fn` raises an end-of-input exception.
          See "Stop conditions" above for specifics.
        metrics: Dict of metrics to run. If None, the default metric functions
          are used; if {}, no metrics are used. Otherwise, `metrics` should map
          friendly names for the metric to a `MetricSpec` object defining which
          model outputs to evaluate against which labels with which metric
          function.
          Metric ops should support streaming, e.g., returning `update_op` and
          `value` tensors. For example, see the options defined in
          `../../../metrics/python/ops/metrics_ops.py`.
        name: Name of the evaluation if user needs to run multiple evaluations on
          different data sets, such as on training data vs test data.
        checkpoint_path: Path of a specific checkpoint to evaluate. If `None`, the
          latest checkpoint in `model_dir` is used.
        hooks: List of `SessionRunHook` subclass instances. Used for callbacks
          inside the evaluation call.
  
      Returns:
        Returns `dict` with evaluation results.
docstring_summary: Evaluates given model with provided evaluation data.
docstring_tokens: Evaluates given model with provided evaluation data .
function: def evaluate(self,
                 x=None,
                 y=None,
                 input_fn=None,
                 feed_fn=None,
                 batch_size=None,
                 steps=None,
                 metrics=None,
                 name=None,
                 checkpoint_path=None,
                 hooks=None):
      """Evaluates given model with provided evaluation data.
  
      Stop conditions - we evaluate on the given input data until one of the
      following:
      - If `steps` is provided, and `steps` batches of size `batch_size` are
      processed.
      - If `input_fn` is provided, and it raises an end-of-input
      exception (`OutOfRangeError` or `StopIteration`).
      - If `x` is provided, and all items in `x` have been processed.
  
      The return value is a dict containing the metrics specified in `metrics`, as
      well as an entry `global_step` which contains the value of the global step
      for which this evaluation was performed.
  
      Args:
        x: Matrix of shape [n_samples, n_features...] or dictionary of many matrices
          containing the input samples for fitting the model. Can be iterator that returns
          arrays of features or dictionary of array of features. If set, `input_fn` must
          be `None`.
        y: Vector or matrix [n_samples] or [n_samples, n_outputs] containing the
          label values (class labels in classification, real numbers in
          regression) or dictionary of multiple vectors/matrices. Can be iterator
          that returns array of targets or dictionary of array of targets. If set,
          `input_fn` must be `None`. Note: For classification, label values must
          be integers representing the class index (i.e. values from 0 to
          n_classes-1).
        input_fn: Input function returning a tuple of:
          features - Dictionary of string feature name to `Tensor` or `Tensor`.
          labels - `Tensor` or dictionary of `Tensor` with labels.
          If input_fn is set, `x`, `y`, and `batch_size` must be `None`. If
          `steps` is not provided, this should raise `OutOfRangeError` or
          `StopIteration` after the desired amount of data (e.g., one epoch) has
          been provided. See "Stop conditions" above for specifics.
        feed_fn: Function creating a feed dict every time it is called. Called
          once per iteration. Must be `None` if `input_fn` is provided.
        batch_size: minibatch size to use on the input, defaults to first
          dimension of `x`, if specified. Must be `None` if `input_fn` is
          provided.
        steps: Number of steps for which to evaluate model. If `None`, evaluate
          until `x` is consumed or `input_fn` raises an end-of-input exception.
          See "Stop conditions" above for specifics.
        metrics: Dict of metrics to run. If None, the default metric functions
          are used; if {}, no metrics are used. Otherwise, `metrics` should map
          friendly names for the metric to a `MetricSpec` object defining which
          model outputs to evaluate against which labels with which metric
          function.
          Metric ops should support streaming, e.g., returning `update_op` and
          `value` tensors. For example, see the options defined in
          `../../../metrics/python/ops/metrics_ops.py`.
        name: Name of the evaluation if user needs to run multiple evaluations on
          different data sets, such as on training data vs test data.
        checkpoint_path: Path of a specific checkpoint to evaluate. If `None`, the
          latest checkpoint in `model_dir` is used.
        hooks: List of `SessionRunHook` subclass instances. Used for callbacks
          inside the evaluation call.
  
      Returns:
        Returns `dict` with evaluation results.
      """
      raise NotImplementedError
function_tokens: def evaluate ( self , x = None , y = None , input_fn = None , feed_fn = None , batch_size = None , steps = None , metrics = None , name = None , checkpoint_path = None , hooks = None ) : raise NotImplementedError