def Poisson_loss(y_true, y_pred): if not isinstance(y_true, R.Tensor): y_true = R.Tensor(y_true) if not isinstance(y_pred, R.Tensor): y_pred = R.Tensor(y_pred) y_pred = R.clip(y_pred, R.epsilon(), R.Saclar(1) - R.epsilon()) return R.sub(y_pred, R.elemul(y_true, R.natlog(y_pred)))
def KL_div_loss(y_true, y_pred, d): if not isinstance(y_true, R.Tensor): y_true = R.Tensor(y_true) if not isinstance(y_pred, R.Tensor): y_pred = R.Tensor(y_pred) y_pred = R.clip(y_pred, R.epsilon(), R.Saclar(1) - R.epsilon()) return R.elemul(y_true, R.natlog(R.div(y_true, y_pred)))