learning_ss.py 文件源码

def sigmoid_kl_with_logits(logits, targets):
        """ Sigmoid cross entropy with smooth labels
        Args:
            logits: logits
            targets: smooth targets

        Returns:
            cross entropy loss
        """

        assert isinstance(targets, float)
        if targets in [0., 1.]:
            entropy = 0.
        else:
            entropy = - targets * np.log(targets) - \
                (1. - targets) * np.log(1. - targets)
        return tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits) * targets, logits=logits) - entropy