class_docstr: Estimator class for training and evaluation BigDL models.
  
  Estimator wraps a model, and provide an uniform training, evaluation or prediction operation on
  both local host and distributed spark environment.
class_docstr_tok: ["Estimator","class","for","training","and","evaluation","BigDL","models",".","Estimator","wraps","a","model",",","and","provide","an","uniform","training",",","evaluation","or","prediction","operation","on","both","local","host","and","distributed","spark","environment","."]
docstr: Get the scalar from model validation summary
  Return 2-D array like object which could be converted
  by np.array()
  
  Note: The metric and tag may not be consistent
  Please look up following form to pass tag parameter
  Left side is your metric during compile
  Right side is the tag you should pass
  'Accuracy'                  |   'Top1Accuracy'
  'BinaryAccuracy'            |   'Top1Accuracy'
  'CategoricalAccuracy'       |   'Top1Accuracy'
  'SparseCategoricalAccuracy' |   'Top1Accuracy'
  'AUC'                       |   'AucScore'
  'HitRatio'                  |   'HitRate@k' (k is Top-k)
  'Loss'                      |   'Loss'
  'MAE'                       |   'MAE'
  'NDCG'                      |   'NDCG'
  'TFValidationMethod'        |   '${name + " " + valMethod.toString()}'
  'Top5Accuracy'              |   'Top5Accuracy'
  'TreeNNAccuracy'            |   'TreeNNAccuracy()'
  'MeanAveragePrecision'      |   'MAP@k' (k is Top-k) (BigDL)
  'MeanAveragePrecision'      |   'PascalMeanAveragePrecision' (Zoo)
  'StatelessMetric'           |   '${name}'
  # Arguments
  tag: The string variable represents the scalar wanted
docstr_tok: ["Get","the","scalar","from","model","validation","summary","Return","2-D","array","like","object","which","could","be","converted","by","np.array","(",")","Note",":","The","metric","and","tag","may","not","be","consistent","Please","look","up","following","form","to","pass","tag","parameter","Left","side","is","your","metric","during","compile","Right","side","is","the","tag","you","should","pass","'Accuracy","'","|","'Top1Accuracy'","'BinaryAccuracy","'","|","'Top1Accuracy'","'CategoricalAccuracy","'","|","'Top1Accuracy'","'SparseCategoricalAccuracy","'","|","'Top1Accuracy'","'AUC","'","|","'AucScore'","'HitRatio","'","|","'HitRate","@","k","'","(","k","is","Top-k",")","'Loss","'","|","'Loss'","'MAE","'","|","'MAE'","'NDCG","'","|","'NDCG'","'TFValidationMethod","'","|","'","$","{","name","+","``","``","+","valMethod.toString","(",")","}","'","'Top5Accuracy","'","|","'Top5Accuracy'","'TreeNNAccuracy","'","|","'TreeNNAccuracy","(",")","'","'MeanAveragePrecision","'","|","'MAP","@","k","'","(","k","is","Top-k",")","(","BigDL",")","'MeanAveragePrecision","'","|","'PascalMeanAveragePrecision","'","(","Zoo",")","'StatelessMetric","'","|","'","$","{","name","}","'","#","Arguments","tag",":","The","string","variable","represents","the","scalar","wanted"]
code: def get_validation_summary(self, tag=None):
      """
      Get the scalar from model validation summary
      Return 2-D array like object which could be converted
      by np.array()
  
      Note: The metric and tag may not be consistent
      Please look up following form to pass tag parameter
      Left side is your metric during compile
      Right side is the tag you should pass
      'Accuracy'                  |   'Top1Accuracy'
      'BinaryAccuracy'            |   'Top1Accuracy'
      'CategoricalAccuracy'       |   'Top1Accuracy'
      'SparseCategoricalAccuracy' |   'Top1Accuracy'
      'AUC'                       |   'AucScore'
      'HitRatio'                  |   'HitRate@k' (k is Top-k)
      'Loss'                      |   'Loss'
      'MAE'                       |   'MAE'
      'NDCG'                      |   'NDCG'
      'TFValidationMethod'        |   '${name + " " + valMethod.toString()}'
      'Top5Accuracy'              |   'Top5Accuracy'
      'TreeNNAccuracy'            |   'TreeNNAccuracy()'
      'MeanAveragePrecision'      |   'MAP@k' (k is Top-k) (BigDL)
      'MeanAveragePrecision'      |   'PascalMeanAveragePrecision' (Zoo)
      'StatelessMetric'           |   '${name}'
      # Arguments
      tag: The string variable represents the scalar wanted
      """
      return callZooFunc(
          "float",
          "estimatorGetScalarFromSummary",
          self.value,
          tag,
          "Validation",
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/estimator/estimator.py
imports: from bigdl.util.common import JavaValue
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 7 for calling the Estimator obj's underlying member method code with example usage: obj.get_validation_summary(tag) and returns: callZooFunc