adagan.py 文件源码

def _compute_data_weights_topk(self, opts, density_ratios):
        """Put a uniform distribution on K points with largest prob real data.

        This is a naiive heuristic which makes next GAN concentrate on those
        points of the training set, which were classified correctly with
        largest margins. I.e., out current mixture model is not capable of
        generating points looking similar to these ones.
        """
        threshold = np.percentile(density_ratios,
                                  opts["topk_constant"]*100.0)
        # Note that largest prob_real_data corresponds to smallest density
        # ratios.
        mask = density_ratios <= threshold
        data_weights = np.zeros(self._data_num)
        data_weights[mask] = 1.0 / np.sum(mask)
        return data_weights