(y, y_prob)
return logloss_sum / sample_num
The cross-entropy loss class for binary classification
            Formula : -(sum(y * log(y_prob) + (1 - y) * log(1 - y_prob)) / N)

        Parameters
        ----------
        y : Table
            The input data's labels

        y_prob : Table
            The predict probability.

        Returns
        -------
        log_loss : float, the binary cross entropy loss
The cross-entropy loss class for binary classification
            Formula : -(sum(y * log(y_prob) + (1 - y) * log(1 - y_prob)) / N)
The cross - entropy loss class for binary classification Formula : - ( sum ( y * log ( y_prob ) + ( 1 - y ) * log ( 1 - y_prob )) / N )
def compute_loss(y, y_prob):
        """
        The cross-entropy loss class for binary classification
            Formula : -(sum(y * log(y_prob) + (1 - y) * log(1 - y_prob)) / N)

        Parameters
        ----------
        y : Table
            The input data's labels

        y_prob : Table
            The predict probability.

        Returns
        -------
        log_loss : float, the binary cross entropy loss

        """
        logloss = y.join(y_prob, lambda y, yp: (-np.nan_to_num(y * np.log(yp) + (1 - y) * np.log(1 - yp)), 1))
        logloss_sum, sample_num = logloss.reduce(lambda tuple1, tuple2: (tuple1[0] + tuple2[0], tuple1[1] + tuple2[1]))
        return logloss_sum / sample_num
def compute_loss ( y , y_prob ) : logloss = y . join ( y_prob , lambda y , yp : ( - np . nan_to_num ( y * np . log ( yp ) + ( 1 - y ) * np . log ( 1 - yp ) ) , 1 ) ) logloss_sum , sample_num = logloss . reduce ( lambda tuple1 , tuple2 : ( tuple1 [ 0 ] + tuple2 [ 0 ] , tuple1 [ 1 ] + tuple2 [ 1 ] ) ) return logloss_sum / sample_num