Tensorflow中replace()函数的使用
初学tensorflow，如果不对请指正，谢谢
tenorflow中有一个函数叫reshape()函数,这个函数可以将输入的tensor变换成shape的格式。具体看函数说明：
def reshape(tensor, shape, name=None):
Reshapes a tensor.
Given `tensor`, this operation returns a tensor that has the same values
as `tensor` with shape `shape`.
If one component of `shape` is the special value -1, the size of that dimension
is computed so that the total size remains constant. In particular, a `shape`
of `[-1]` flattens into 1-D. At most one component of `shape` can be -1.
If `shape` is 1-D or higher, then the operation returns a tensor with shape
`shape` filled with the values of `tensor`. In this case, the number of elements
implied by `shape` must be the same as the number of elements in `tensor`.1.给定一个tensor，怎么确定它的shape。
2.给定一个tensor和shape，经过reshape(tensor,shape)变换后返回的tensor是什么格式的。
先看一看文档中给出的例子：
For example:
```prettyprint
# 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]首先，tensor的维数与shape中元素的个数是相等的；其次，shape中数字的乘积与tensor中元素的个数相等，因此在调用reshape()函数时应注意元素个数保持一致。要确定给定的tensor的shape，应该从外往里看。例如
# 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]]经过reshape变化的t，最外层包含两个元素，里面一层包含4个元素，相当于2维数组。变化之前的t,最外层包含两个元素（第一行末尾一个逗号隔开），中间层每层两个元素（第一行中间一个逗号隔开），最内层两个元素，相当于三维数组。
给定了tensor和shape后，将tensor按一维展开，再按照给定的shape重新组合。
如果shape中有-1,函数会自动计算维度，但只能有有个-1.