Reduction operations

最新推荐文章于 2022-09-24 17:25:31 发布

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最新推荐文章于 2022-09-24 17:25:31 发布

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原文链接：http://www.cnblogs.com/xxxxxxxxx/p/11068461.html

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博客主要介绍了张量的缩减操作，即减少张量内元素数量的操作。它能对单个张量内的元素进行运算，还提及常见的张量缩减操作，如按轴缩减、Argmax缩减操作，Argmax可返回张量内最大值的索引位置，常用于网络输出预测张量。

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Reuction operations

Reduction operations

A reduction operations on a tensor is an operation that reduces the number of elements contained within the tensor.

Tensors give us the ability to manage out data. Reduction operations allow us to perform operations on elements within a single tensor.

Suppose we have the following 3$\times$3 rank-2 tensor.

> t = torch.tensor([
    [0, 1, 0],
    [2, 0, 2],
    [0, 3, 0]
], dtype=torch.float32)

Common tensor reduction operations

> t.sum()
tensor(8.)

> t.numel()
9

> t.prod()
tensor(0.)

> t.mean()
tensor(.8889)

> t.std()
tensor(1.1667)

Reducing tensors by axes

Suppose we have the following tensor:

> t = torch.tensor([
    [1,1,1,1],
    [2,2,2,2],
    [3,3,3,3]
], dtype=torch.float32)

This time , we will specify a dimension to reduce.

> t.sum(dim=0)
tensor([6., 6., 6., 6.])

> t.sum(dim=1)
tensor([4., 8., 12.])

Argmax tensor reduction operation

Argmax returns the index location of the maximum value inside a tensor.

t = torch.tensor([
    [1,0,0,2],
    [0,3,3,0],
    [4,0,0,5]
], dtype=torch.float32)

If we don't specific an axis to the argmax() method, it returns the index location of the max value from the flattened tensor, which in the case is indeed 11.

> t.max()
tensor(5.)

> t.argmax()
tensor(11)

> t.flatten()
tensor([1., 0., 0., 2., 0., 3., 3., 0., 4., 0., 0., 5.])

Work with specific axis now:

> t.max(dim=0)
(tensor([4., 3., 3., 5.]), tensor([2, 1, 1, 2]))

> t.argmax(dim=0)
tensor([2, 1, 1, 2])

> t.max(dim=1)
(tensor([2., 3., 5.]), tensor([3, 1, 3]))

> t.argmax(dim=1)
tensor([3, 1, 3])

In practice, we often use the argmax() function on a network's output prediction tensor, to determine which category has the highest prediction value.

转载于:https://www.cnblogs.com/xxxxxxxxx/p/11068461.html