(self, y, t)
return self.be.sum(self.be.safelog(1 - y) * (t - 1) - self.be.safelog(y) * t, axis=0)
Returns the binary 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 binary cross entropy cost
Returns the binary cross entropy cost.
Returns the binary cross entropy cost .
def __call__(self, y, t):
        """
        Returns the binary 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 binary cross entropy cost
        """
        assert y.shape == t.shape, "CrossEntropy requires network output shape to match targets"
        return self.be.sum(self.be.safelog(1 - y) * (t - 1) - self.be.safelog(y) * t, axis=0)
def __call__ ( self , y , t ) : assert y . shape == t . shape , "CrossEntropy requires network output shape to match targets" return self . be . sum ( self . be . safelog ( 1 - y ) * ( t - 1 ) - self . be . safelog ( y ) * t , axis = 0 )