(self, y, t, calcrange=slice(0, None))
return self.outputs.get()[:, calcrange].mean()
Returns the misclassification error metric

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

        Returns:
            float: Returns the metric
Returns the misclassification error metric
Returns the misclassification error metric
def __call__(self, y, t, calcrange=slice(0, None)):
        """
        Returns the misclassification error metric

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

        Returns:
            float: Returns the metric
        """
        # convert back from onehot and compare
        self.preds[:] = self.be.argmax(y, axis=0)
        self.hyps[:] = self.be.argmax(t, axis=0)
        self.outputs[:] = self.be.not_equal(self.preds, self.hyps)

        return self.outputs.get()[:, calcrange].mean()
def __call__ ( self , y , t , calcrange = slice ( 0 , None ) ) : # convert back from onehot and compare self . preds [ : ] = self . be . argmax ( y , axis = 0 ) self . hyps [ : ] = self . be . argmax ( t , axis = 0 ) self . outputs [ : ] = self . be . not_equal ( self . preds , self . hyps ) return self . outputs . get ( ) [ : , calcrange ] . mean ( )