(values)
return activation.softmax(values)
Predict method for using softmax cross entropy
            Formula : probability(category_i) =
                exp(value(category_i)) / sum(exp(value(category_i))

        Parameters
        ----------
        values : ndarray, The input value of softmax function

        Returns
        -------
        probability : ndarray, the output of softmax function,
            the array shape is the sample as input values
Predict method for using softmax cross entropy
            Formula : probability(category_i) =
                exp(value(category_i)) / sum(exp(value(category_i))
Predict method for using softmax cross entropy Formula : probability ( category_i ) = exp ( value ( category_i )) / sum ( exp ( value ( category_i ))
def predict(values):
        """
        Predict method for using softmax cross entropy
            Formula : probability(category_i) =
                exp(value(category_i)) / sum(exp(value(category_i))

        Parameters
        ----------
        values : ndarray, The input value of softmax function

        Returns
        -------
        probability : ndarray, the output of softmax function,
            the array shape is the sample as input values

        """
        return activation.softmax(values)
def predict ( values ) : return activation . softmax ( values )