parameters: (self,
                 x=None,
                 y=None,
                 input_fn=None,
                 feed_fn=None,
                 batch_size=None,
                 steps=None,
                 metrics=None,
                 name=None)
return_statement: return super(Classifier, self).evaluate(x=x,
                                              y=y,
                                              input_fn=input_fn,
                                              batch_size=batch_size,
                                              steps=steps,
                                              metrics=metrics,
                                              name=name)
docstring: Evaluates given model with provided evaluation data.
  
      See superclass Estimator for more details.
  
      Args:
        x: features.
        y: targets.
        input_fn: Input function.
        feed_fn: Function creating a feed dict every time it is called.
        batch_size: minibatch size to use on the input.
        steps: Number of steps for which to evaluate model.
        metrics: Dict of metric ops to run. If None, the default metrics are used.
        name: Name of the evaluation.
  
      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):
      """Evaluates given model with provided evaluation data.
  
      See superclass Estimator for more details.
  
      Args:
        x: features.
        y: targets.
        input_fn: Input function.
        feed_fn: Function creating a feed dict every time it is called.
        batch_size: minibatch size to use on the input.
        steps: Number of steps for which to evaluate model.
        metrics: Dict of metric ops to run. If None, the default metrics are used.
        name: Name of the evaluation.
  
      Returns:
        Returns `dict` with evaluation results.
      """
      metrics = metrics or _get_classifier_metrics(self._n_classes)
      return super(Classifier, self).evaluate(x=x,
                                              y=y,
                                              input_fn=input_fn,
                                              batch_size=batch_size,
                                              steps=steps,
                                              metrics=metrics,
                                              name=name)
function_tokens: def evaluate ( self , x = None , y = None , input_fn = None , feed_fn = None , batch_size = None , steps = None , metrics = None , name = None ) : metrics = metrics or _get_classifier_metrics ( self . _n_classes ) return super ( Classifier , self ) . evaluate ( x = x , y = y , input_fn = input_fn , batch_size = batch_size , steps = steps , metrics = metrics , name = name )