pyspark/bigdl/nn/layer.py
def evaluate(self, *args):
        """
        No argument passed in:
        Evaluate the model to set train = false, useful when doing test/forward
        :return: layer itself

        Three arguments passed in:
        A method to benchmark the model quality.

        :param dataset: the input data
        :param batch_size: batch size
        :param val_methods: a list of validation methods. i.e: Top1Accuracy,Top5Accuracy and Loss.
        :return: a list of the metrics result
        """
        if len(args) == 0:
            callBigDlFunc(self.bigdl_type,
                          "evaluate", self.value)
            return self
        elif len(args) == 3:
            dataset, batch_size, val_methods = args
            if (isinstance(dataset, ImageFrame)):
                return callBigDlFunc(self.bigdl_type,
                                    "modelEvaluateImageFrame",
                                    self.value,
                                    dataset, batch_size, val_methods)
            else:
                return callBigDlFunc(self.bigdl_type,
                                     "modelEvaluate",
                                     self.value,
                                     dataset, batch_size, val_methods)
        else:
            raise Exception("Error when calling evaluate(): it takes no argument or exactly three arguments only")
def evaluate(self, *args):
        """
        No argument passed in:
        Evaluate the model to set train = false, useful when doing test/forward
        :return: layer itself

        Three arguments passed in:
        A method to benchmark the model quality.

        :param dataset: the input data
        :param batch_size: batch size
        :param val_methods: a list of validation methods. i.e: Top1Accuracy,Top5Accuracy and Loss.
        :return: a list of the metrics result
        """
        if len(args) == 0:
            callBigDlFunc(self.bigdl_type,
                          "evaluate", self.value)
            return self
        elif len(args) == 3:
            dataset, batch_size, val_methods = args
            if (isinstance(dataset, ImageFrame)):
                return callBigDlFunc(self.bigdl_type,
                                    "modelEvaluateImageFrame",
                                    self.value,
                                    dataset, batch_size, val_methods)
            else:
                return callBigDlFunc(self.bigdl_type,
                                     "modelEvaluate",
                                     self.value,
                                     dataset, batch_size, val_methods)
        else:
            raise Exception("Error when calling evaluate(): it takes no argument or exactly three arguments only")
def evaluate ( self , * args ) : if len ( args ) == 0 : callBigDlFunc ( self . bigdl_type , "evaluate" , self . value ) return self elif len ( args ) == 3 : dataset , batch_size , val_methods = args if ( isinstance ( dataset , ImageFrame ) ) : return callBigDlFunc ( self . bigdl_type , "modelEvaluateImageFrame" , self . value , dataset , batch_size , val_methods ) else : return callBigDlFunc ( self . bigdl_type , "modelEvaluate" , self . value , dataset , batch_size , val_methods ) else : raise Exception ( "Error when calling evaluate(): it takes no argument or exactly three arguments only" )
No argument passed in:
        Evaluate the model to set train = false, useful when doing test/forward
        :return: layer itself

        Three arguments passed in:
        A method to benchmark the model quality.

        :param dataset: the input data
        :param batch_size: batch size
        :param val_methods: a list of validation methods. i.e: Top1Accuracy,Top5Accuracy and Loss.
        :return: a list of the metrics result
No argument passed in : Evaluate the model to set train = false useful when doing test / forward : return : layer itself