class_docstr: Evaluator.
  
  A class used for performing predictions and evaluate a model performance.
  
  Arguments:
      tensors: list of `Tensor`. A list of tensors to perform predictions.
      model: `str`. The model weights path (Optional).
      session: `Session`. The session to run the prediction (Optional).
class_docstr_tok: ["Evaluator",".","A","class","used","for","performing","predictions","and","evaluate","a","model","performance",".","Arguments",":","tensors",":","list","of","`","Tensor","`",".","A","list","of","tensors","to","perform","predictions",".","model",":","`","str","`",".","The","model","weights","path","(","Optional",")",".","session",":","`","Session","`",".","The","session","to","run","the","prediction","(","Optional",")","."]
docstr: Evaluate.
  
  Evaluate a list of tensors over a whole dataset. Generally,
  'ops' argument are average performance metrics (such as average mean,
  top-3, etc...)
  
  Arguments:
      feed_dict: `dict`. The feed dictionary of data.
      ops: list of `Tensors`. The tensors to evaluate.
      batch_size: `int`. A batch size.
  
  Returns:
      The mean average result per tensor over all batches.
docstr_tok: ["Evaluate",".","Evaluate","a","list","of","tensors","over","a","whole","dataset",".","Generally",",","'ops","'","argument","are","average","performance","metrics","(","such","as","average","mean",",","top-3",",","etc","...",")","Arguments",":","feed_dict",":","`","dict","`",".","The","feed","dictionary","of","data",".","ops",":","list","of","`","Tensors","`",".","The","tensors","to","evaluate",".","batch_size",":","`","int","`",".","A","batch","size",".","Returns",":","The","mean","average","result","per","tensor","over","all","batches","."]
code: def evaluate(self, feed_dict, ops, batch_size=128):
      """Evaluate.
  
      Evaluate a list of tensors over a whole dataset. Generally,
      'ops' argument are average performance metrics (such as average mean,
      top-3, etc...)
  
      Arguments:
          feed_dict: `dict`. The feed dictionary of data.
          ops: list of `Tensors`. The tensors to evaluate.
          batch_size: `int`. A batch size.
  
      Returns:
          The mean average result per tensor over all batches.
  
      """
      tflearn.is_training(False, self.session)
      coord = tf.train.Coordinator()
      inputs = tf.get_collection(tf.GraphKeys.INPUTS)
      # Data Preprocessing
      dprep_dict = {}
      dprep_collection = tf.get_collection(tf.GraphKeys.DATA_PREP)
      for i in range(len(inputs)):
          # Support for custom inputs not using dprep/daug
          if len(dprep_collection) > i:
              if dprep_collection[i] is not None:
                  dprep_dict[inputs[i]] = dprep_collection[i]
      # Data Flow
      df = data_flow.FeedDictFlow(
          feed_dict,
          coord,
          batch_size=batch_size,
          dprep_dict=dprep_dict,
          daug_dict=None,
          index_array=None,
          num_threads=1,
      )
  
      return evaluate_flow(self.session, ops, df)
  
file_path: tflearn/tflearn/helpers/evaluator.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  import tflearn
  from ..utils import to_list
  from ..None import data_flow
  from ..None import metrics
  from .trainer import evaluate_flow
label_desc: Use image node_id 3 for calling the Evaluator obj's underlying member method code with example usage: obj.evaluate(feed_dict, ops, batch_size) and returns: evaluate_flow