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，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 
在这种情况下，我们就可以考虑使用expand_dims来将维度加1。比如我自己代码中遇到的情况，在对图像维度降到二维做特定操作后，要还原成四维[batch, height, width, channels]，前后各增加一维。如果用reshape，则因为上述原因报错

one_img2 = tf.reshape(one_img, shape=[1, one_img.get_shape()[0].value, one_img.get_shape()[1].value, 1])

用下面的方法可以实现：

one_img = tf.expand_dims(one_img, 0)
one_img = tf.expand_dims(one_img, -1) #-1表示最后一维

在最后，给出官方的例子和说明

# '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]

举例：

import tensorflow as tf

a = tf.constant([[1, 2], [3, 4], [5, 6]], dtype=tf.float32)
a0 = tf.expand_dims(a, 0)
a1 = tf.expand_dims(a, 1)
a2 = tf.expand_dims(a, 2)
with tf.Session() as sess:
    print(sess.run(a))
    print('------------')
    print(sess.run(a0))
    print('------------')
    print(sess.run(a1))
    print('------------')
    print(sess.run(a2))

结果：

[[1. 2.]
 [3. 4.]
 [5. 6.]]
------------
[[[1. 2.]
  [3. 4.]
  [5. 6.]]]
------------
[[[1. 2.]]

 [[3. 4.]]

 [[5. 6.]]]
------------
[[[1.]
  [2.]]

 [[3.]
  [4.]]

 [[5.]
  [6.]]]

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.