参考：
https://blog.csdn.net/jasonzzj/article/details/60811035
https://blog.csdn.net/UESTC_V/article/details/80310487
TensorFlow中，想要维度增加一维，可以使用tf.expand_dims(input, dim, name=None)函数。
当然，我们常用tf.reshape(input, shape=[])也可以达到相同效果，
one_img2 = tf.reshape(one_img, shape=[1, one_img.get_shape()[0].value, one_img.get_shape()[1].value, 1])但是有些时候在构建图的过程中，placeholder没有被feed具体的值，这时就会包下面的错误：
TypeError: Expected binary or unicode string, got 1
因此我们使用tf.expand_dims的方法
one_img = tf.expand_dims(one_img, 0)
one_img = tf.expand_dims(one_img, -1) #-1表示最后一维下面有一些官方的例子和说明
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
Args:
input: A Tensor.
dim: A Tensor. Must be one of the following types: int32, int64. 0-D (scalar).
Specifies the dimension index at which to expand the shape of input.
name: A name for the operation (optional).
Returns:
A Tensor. Has the same type as input. Contains the same data as input,
but its shape has an additional dimension of size 1 added.
可以看到在调用函数后，tensor中加入了一个“1”的维度，我在网上查了一下，这个“1”只是代表增加了一个维度，
没有任何具体的含义。
说完了维度扩张的方法再来看一下降低维度的方法
tf.squeeze()
参数列表
tf.squeeze(input, axis=None, name=None, squeeze_dims=None)# 一些实例
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
无意之间看到了一个春招的帖子，给大家分享一下
https://blog.csdn.net/dQCFKyQDXYm3F8rB0/article/details/89391988