class_docstr: Class for evaluation of minibatches against the specified evaluation function.
  
  Args:
     eval_function (:class:`~cntk.ops.functions.Function`): evaluation function.
     progress_writers (list): optionally, list of progress writers from :mod:`cntk.utils` to track
       training progress.
class_docstr_tok: ["Class","for","evaluation","of","minibatches","against","the","specified","evaluation","function",".","Args",":","eval_function","(",":","class",":","`","~cntk.ops.functions.Function","`",")",":","evaluation","function",".","progress_writers","(","list",")",":","optionally",",","list","of","progress","writers","from",":","mod",":","`","cntk.utils","`","to","track","training","progress","."]
docstr: Test the evaluation function on the specified batch of samples.
  
  Args:
      arguments: maps variables to their
       input data. The interpretation depends on the input type:
  
         * `dict`: keys are input variable or names, and values are the input data.
           See :meth:`~cntk.ops.functions.Function.forward` for details on passing input data.
  
         * any other type: if node has a unique input, ``arguments`` is mapped to this input.
           For nodes with more than one input, only `dict` is allowed.
  
       In both cases, every sample in the data will be interpreted
       as a new sequence. To mark samples as continuations of the
       previous sequence, specify ``arguments`` as `tuple`: the
       first element will be used as ``arguments``, and the second one will
       be used as a list of bools, denoting whether a sequence is a new
       one (`True`) or a continuation of the previous one (`False`).
       Data should be either NumPy arrays or a
       :class:`~cntk.io.MinibatchData` instance.
      device (:class:`~cntk.device.DeviceDescriptor`): the device descriptor that
       contains the type and id of the device on which the computation is
       to be performed.
      distributed (`bool`, optional): flag indicating if evaluation results should
       be aggregated across workers.
  
  Note:
       See :meth:`~cntk.ops.functions.Function.forward` for examples on
       passing input data.
  
  Returns:
      `float`: the average evaluation criterion value per sample for the
      tested minibatch.
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code: def test_minibatch(self, arguments, device=None, distributed=False):
      """
      Test the evaluation function on the specified batch of samples.
  
      Args:
          arguments: maps variables to their
           input data. The interpretation depends on the input type:
  
             * `dict`: keys are input variable or names, and values are the input data.
               See :meth:`~cntk.ops.functions.Function.forward` for details on passing input data.
  
             * any other type: if node has a unique input, ``arguments`` is mapped to this input.
               For nodes with more than one input, only `dict` is allowed.
  
           In both cases, every sample in the data will be interpreted
           as a new sequence. To mark samples as continuations of the
           previous sequence, specify ``arguments`` as `tuple`: the
           first element will be used as ``arguments``, and the second one will
           be used as a list of bools, denoting whether a sequence is a new
           one (`True`) or a continuation of the previous one (`False`).
           Data should be either NumPy arrays or a
           :class:`~cntk.io.MinibatchData` instance.
          device (:class:`~cntk.device.DeviceDescriptor`): the device descriptor that
           contains the type and id of the device on which the computation is
           to be performed.
          distributed (`bool`, optional): flag indicating if evaluation results should
           be aggregated across workers.
  
      Note:
           See :meth:`~cntk.ops.functions.Function.forward` for examples on
           passing input data.
  
      Returns:
          `float`: the average evaluation criterion value per sample for the
          tested minibatch.
      """
      if not device:
          device = use_default_device()
  
      arguments = sanitize_var_map(
          tuple(self.evaluation_function.arguments), arguments
      )
      return super(Evaluator, self).test_minibatch(
          arguments, device, distributed
      )
  
file_path: CNTK/bindings/python/cntk/eval/evaluator.py
imports: from ..None import cntk_py
  from ..device import use_default_device
  from cntk.internal import sanitize_var_map, sanitize_function, typemap
  from ..io import MinibatchData
label_desc: Use image node_id 2 for calling the Evaluator obj's underlying member method code with example usage: obj.test_minibatch(arguments, device, distributed) and returns: super