pyspark/bigdl/nn/layer.py
def parameters(self):
        """
        Get the model parameters which containing: weight, bias, gradBias, gradWeight

        :return: dict(layername -> dict(parametername -> ndarray))
        """
        name_to_params = callBigDlFunc(self.bigdl_type,
                                       "modelGetParameters",
                                       self.value)

        def to_ndarray(params):
            return dict((param_name,
                         np.array(values[0], dtype=self.get_dtype()).reshape(
                             values[1])) for param_name, values in
                        params.items())

        return dict((layer_name, to_ndarray(params)) for layer_name, params in
                name_to_params.items())
def parameters(self):
        """
        Get the model parameters which containing: weight, bias, gradBias, gradWeight

        :return: dict(layername -> dict(parametername -> ndarray))
        """
        name_to_params = callBigDlFunc(self.bigdl_type,
                                       "modelGetParameters",
                                       self.value)

        def to_ndarray(params):
            return dict((param_name,
                         np.array(values[0], dtype=self.get_dtype()).reshape(
                             values[1])) for param_name, values in
                        params.items())

        return dict((layer_name, to_ndarray(params)) for layer_name, params in
                name_to_params.items())
def parameters ( self ) : name_to_params = callBigDlFunc ( self . bigdl_type , "modelGetParameters" , self . value ) def to_ndarray ( params ) : return dict ( ( param_name , np . array ( values [ 0 ] , dtype = self . get_dtype ( ) ) . reshape ( values [ 1 ] ) ) for param_name , values in params . items ( ) ) return dict ( ( layer_name , to_ndarray ( params ) ) for layer_name , params in name_to_params . items ( ) )
Get the model parameters which containing: weight, bias, gradBias, gradWeight

        :return: dict(layername -> dict(parametername -> ndarray))
Get the model parameters which containing : weight bias gradBias gradWeight