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.
