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
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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：
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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
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# '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.
以上这篇TensorFlow用expand_dim()来增加维度的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/jasonzzj/article/details/60811035
