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","."]
code: def __init__(self, eval_function, progress_writers=None):
      if eval_function is not None:
          eval_function = sanitize_function(eval_function)
  
      if progress_writers is None:
          progress_writers = []
      elif not isinstance(progress_writers, list):
          progress_writers = [progress_writers]
  
      evaluator = cntk_py.create_evaluator(
          eval_function, progress_writers
      )
      # transplant into this class instance
      self.__dict__ = evaluator.__dict__
  
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 1 to create a new Evaluator object from inherited base classes: cntk_py with example: obj = Evaluator(eval_function, progress_writers)