# Torch sum keepdim

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**torch**.

**sum**(input, dim,

**keepdim**=False, dtype=None) → Tensor Returns the

**sum**of each row of the input tensor in the given dimension dim.If dim is a list of dimensions, reduce over all of them. If

**keepdim**is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1..

**torch**.

**sum**(input, dim, keepdim=False, *, dtype=None) → Tensor Returns the

**sum**of each row of the input tensor in the given dimension dim. If dim is a list of dimensions, reduce over all of them. If

**keepdim**is True, the output tensor is of the same size as input except in the dimension (s) dim where it is of size 1.

**torch**.sum()对输入的tensor数据的某一维度求和，一共两种用法 1．torch.sum(input, dtype=None) input:输入一个tensor; dim:要求和的维度，可以是一个列表; keepdim:求和之后这个dim的元素个数为1，所以要被去掉，如果要保留这个维度，则应当keepdim=True; 举例说明: import

**torch**a.

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**torch**.max() or

**torch**.

**sum**() don’t have

**keepdim**argument thought it is available in the document. For example, if I run the following code: import numpy as np import

**torch**a =

**torch**.Tensor(np.arange(6).reshape(2, 3)) print

**torch**.max(a,

**keepdim**=True) I will get the error:. 16. · v =

**torch**.randn (100, 20, 10)

**torch**.

**sum**(v, list (range (2))) Then it works. But this seems inefficient for large n. In general Tensors with a very large number of dimensions But this seems inefficient for large n.

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**torch**.max中keepdim的作用. 1、 input 是输入的tensor。. 2、 dim 是索引的维度，dim=0寻找每一列的最大值，dim=1寻找每一行的最大值。. 3、

**keepdim**表示是否需要保持输出的维度与输入一样，keepdim=True表示输出和输入的维度一样，keepdim=False表示输出的维度被压缩了，也就是.