未验证 提交 f5d76b50 编写于 作者: Z Zhang Ting 提交者: GitHub

polish the dist doc, test=document_fix (#23982)

上级 62e647c3
......@@ -341,9 +341,32 @@ def norm(input, p='fro', axis=None, keepdim=False, out=None, name=None):
def dist(x, y, p=2):
"""
This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure
of distance. The shapes of x and y must be broadcastable.
of distance. The shapes of x and y must be broadcastable. The definition is as follows, for
details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:
Where, z = x - y,
- Each input has at least one dimension.
- Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.
Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be
obtained as follows:
1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the
tensor with fewer dimensions.
For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the
dimension of y.
x (4-D Tensor): 8 x 1 x 6 x 1
y (4-D Tensor): 1 x 7 x 1 x 5
2. Determine the size of each dimension of the output z: choose the maximum value from the
two input dimensions.
z (4-D Tensor): 8 x 7 x 6 x 5
If the number of dimensions of the two inputs are the same, the size of the output can be
directly determined in step 2. When p takes different values, the norm formula is as follows:
When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册