parameters.py 文件源码

def get_value(self, *args, **kwargs):
        if len(args) == 2:
            gradient = args[0]
            nat_gradient = args[1]
            tmp = np.asscalar(gradient.dot(nat_gradient))
            lambda_v = np.sqrt(tmp / (4. * self._eps))
            # For numerical stability
            lambda_v = max(lambda_v, 1e-8)
            step_length = 1. / (2. * lambda_v)

            return step_length
        elif len(args) == 1:
            return self.get_value(args[0], args[0], **kwargs)
        else:
            raise ValueError('Adaptive parameters needs gradient or gradient'
                             'and natural gradient')