(self, y, t, calcrange=slice(0, None))
return np.array(self.correctProbs.get()[:, calcrange].mean())
Args:
            y (Tensor or OpTree): Output of previous layer or model
            t (Tensor or OpTree): True targets corresponding to y

        Returns:
            numpy array : Returns the log loss  metric in numpy array,
                         [LogLoss]
Args:
            y (Tensor or OpTree): Output of previous layer or model
            t (Tensor or OpTree): True targets corresponding to y
Args : y ( Tensor or OpTree ) : Output of previous layer or model t ( Tensor or OpTree ) : True targets corresponding to y
def __call__(self, y, t, calcrange=slice(0, None)):
        """
        Args:
            y (Tensor or OpTree): Output of previous layer or model
            t (Tensor or OpTree): True targets corresponding to y

        Returns:
            numpy array : Returns the log loss  metric in numpy array,
                         [LogLoss]
        """
        self.correctProbs[:] = self.be.sum(y * t, axis=0)
        self.correctProbs[:] = -self.be.safelog(self.correctProbs)
        return np.array(self.correctProbs.get()[:, calcrange].mean())
def __call__ ( self , y , t , calcrange = slice ( 0 , None ) ) : self . correctProbs [ : ] = self . be . sum ( y * t , axis = 0 ) self . correctProbs [ : ] = - self . be . safelog ( self . correctProbs ) return np . array ( self . correctProbs . get ( ) [ : , calcrange ] . mean ( ) )