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学深度学习怎么可能学不到BN呢？今天就记录一下，学BN学到的一些知识。个人笔记，欢迎指点。
原论文：《Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift》
Batch Normalization的目的是使我们的feature map满足均值为0，方差为1的分布规律。
这里所说满足某一分布规律并不是指某一个feature map的数据要满足分布规律，理论上是指整个训练样本集所对应feature map的数据要满足分布规律。
对于一个拥有d维的输入x，我们将对它的每一个维度进行标准化处理
下图为论文中给出的公式：
我们刚刚有说让feature map满足某一分布规律，理论上是指整个训练样本集所对应feature
map的数据要满足分布规律，也就是说要计算出整个训练集的feature
map然后在进行标准化处理，对于一个大型的数据集明显是不可能的，所以论文中说的是Batch
Normalization，也就是我们计算一个Batch数据的feature
map然后在进行标准化（batch越大越接近整个数据集的分布，效果越好）。我们根据上图的公式可以知道代表着我们计算的feature
map每个维度（channel）的均值，注意是一个向量不是一个值，向量的每一个元素代表着一个维度（channel）的均值。代表着我们计算的feature
map每个维度（channel）的方差，注意是一个向量不是一个值，向量的每一个元素代表着一个维度（channel）的方差，然后根据和计算标准化处理后得到的值。
在原论文公式中还有，两个参数吗？是的，是用来调整数值分布的方差大小，是用来调节数值均值的位置。这两个参数是在反向传播过程中学习得到的，的默认值是1，
的默认值是0。
