问题描述：
Specifically, I want to do matmul(A,B) where
'A' has shape (m,n)
'B' has shape (k,n,p)
and the result should have shape (k,m,p)
参考网站：
https://groups.google.com/a/tensorflow.org/forum/#!topic/discuss/4tgsOSxwtkY
https://stackoverflow.com/questions/38235555/tensorflow-matmul-of-input-matrix-with-batch-data
解决办法：
1，我们知道TensorFlow的matmul已经支持了batch，即：
A = tf.Variable(tf.random_normal(shape=(a, b, n, m)))
B = tf.Variable(tf.random_normal(shape=(a, b, m, k)))
tf.matmul(A, B)会返回(a,b,n,k)，前面的N个维度会被保留。但是适用情景与题目不符。
2，所以我们自然想到reshape。
You can conflate the two dimensions not used in the multiplication using reshape, multiply the two matrices, and then call reshape again to get the desired shape. This is equivalent to doing batch multiplication.
简而言之呢，就是，你可以将乘法中用不到的维度reshape到后面去，比如
(k, m, p) => (m, p * k)
进行矩阵乘法得到：(n, p * k)
之后reshape成：(k, n, p)。
虽然有些麻烦，但这是唯一的解决办法了。
适用情景：A矩阵只有一个，但是B矩阵有batch_num个，需要逐个进行矩阵乘法的场合。