'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]'

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())
