(self, y, t)
return (self.be.sum(-t * self.logscale * self.be.safelog(y), axis=0))
Returns the multiclass cross entropy cost

        Args:
            y (Tensor or OpTree): Output of previous layer or model
            t (Tensor or OpTree): True targets corresponding to y

        Returns:
            OpTree: Returns the multiclass cross entropy cost
Returns the multiclass cross entropy cost
Returns the multiclass cross entropy cost
def __call__(self, y, t):
        """
        Returns the multiclass cross entropy cost

        Args:
            y (Tensor or OpTree): Output of previous layer or model
            t (Tensor or OpTree): True targets corresponding to y

        Returns:
            OpTree: Returns the multiclass cross entropy cost
        """
        if y.shape != t.shape:
            raise ValueError((
                "CrossEntropy requires network output shape to match "
                "targets. Network output shape was {} and targets shape "
                "was {}"
            ).format(y.shape, t.shape))
        return (self.be.sum(-t * self.logscale * self.be.safelog(y), axis=0))
def __call__ ( self , y , t ) : if y . shape != t . shape : raise ValueError ( ( "CrossEntropy requires network output shape to match " "targets. Network output shape was {} and targets shape " "was {}" ) . format ( y . shape , t . shape ) ) return ( self . be . sum ( - t * self . logscale * self . be . safelog ( y ) , axis = 0 ) )