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问题所属任务编号（Task 01-05）
Task 03
运行环境（操作系统版本、Python 版本）/ 非程序问题
非程序问题
完整的报错信息（截图或复制均可）/ 问题详细描述
H（p,q）表示交叉熵，Dkl(p||q)表示kl散度，H(p,q)=H(p)+Dkl(p||q)，Kl散度当q=p的时候可以到0， 而交叉熵最小智能是h(p),这里去优化交叉熵或是kl散度是不是没有什么区别呀？而且能优化到0不是更好吗？请助教老师们帮忙解答一下为什么优化交叉熵损失函数是好的选择。
描述你期望看到的结果
详细信息可以参考 stackexchange 上的问题：https://stats.stackexchange.com/questions/357963/what-is-the-difference-between-cross-entropy-and-kl-divergence?newreg=f9c50acac96b4b019c10abfc4e0a6b95
分类模型学习的目标是希望学习P_{truth}(y|x) := P(truth)，但是真实分布是未知的，我们只能通过数据集$P(D)$来近似真实分布，因此我们有P(model)≈P(D)≈P(truth)。同时在更新梯度的时候我们并没有用到整个数据集，而是使用minibatch的方法，因此在工程中交叉熵会比KL散度更加稳定。（当然我好像看t-SNE或者saliency任务也会用KL散度作为损失函数。