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函数：tf.reshape(tensor, shape, name=None) 
作用：将tensor变换为参数shape的形式。 
参数：其中shape为一个列表形式，特殊的一点是列表中可以存在-1。-1代表的含义是不用我们自己指定这一维的大小，函数会自动计算，但列表中只能存在一个-1。（当然如果存在多个-1，就是一个存在多解的方程了）

好了我想说的重点还有一个就是根据shape如何变换矩阵。其实简单的想就是，

reshape（t, shape） => reshape(t, [-1]) => reshape(t, shape)

首先将矩阵t变为一维矩阵，然后再对矩阵的形式更改就可以了。

官方的例子：

# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]# tensor 't' has shape [9]reshape(t, [3, 3]) ==> [[1, 2, 3],                        [4, 5, 6],                        [7, 8, 9]]# tensor 't' is [[[1, 1], [2, 2]],#                [[3, 3], [4, 4]]]# tensor 't' has shape [2, 2, 2]reshape(t, [2, 4]) ==> [[1, 1, 2, 2],                        [3, 3, 4, 4]]# tensor 't' is [[[1, 1, 1],#                 [2, 2, 2]],#                [[3, 3, 3],#                 [4, 4, 4]],#                [[5, 5, 5],#                 [6, 6, 6]]]# tensor 't' has shape [3, 2, 3]# pass '[-1]' to flatten 't'reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]# -1 can also be used to infer the shape# -1 is inferred to be 9:reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]# -1 is inferred to be 2:reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]# -1 is inferred to be 3:reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],                              [2, 2, 2],                              [3, 3, 3]],                             [[4, 4, 4],                              [5, 5, 5],                              [6, 6, 6]]]# tensor 't' is [7]# shape `[]` reshapes to a scalarreshape(t, []) ==> 7

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