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This article introduces the relevant knowledge of "what is the role of None in Numpy". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

That is, there is no None when you call the parameter, you must pass it the parameter, circle (this must be passed in the parameter to successfully call this parameter.

What does numpy-python [:, 2] [:, None] mean

Solution

Python supports multidimensional slicing syntax, but python itself does not use it. Usually, I see it used in numpy.

[:, 2] look at slicing principle:

[:, None]

None means that the dimension is not sliced, but is treated as an array element as a whole.

So, the effect of [:, None] is to split the two-dimensional array by each row, and finally form a three-dimensional array.

The function of None in Numpy Array

> importnumpyasnp

> a = [1, 2, 3, 4]

> > a=np.array (a)

> > a

Array ([1, 2, 3, 4])

> Bevera [:, None]

> > b

Array ([1])

[2],

[3],

[4]])

> Centra [:, None,None]

> > c

Array ([[1]])

[[2]]

[[3]]

[[4])

> a=np.ones ((2BL3))

> > a

Array ([[1. Rect. 1.]

[1. 1.]])

> Bevera [:, None,:]

> > b

Array ([1. Rect. 1.]]

[[1. Dint 1.])

> > Bevera [None,:,:]

> > b

Array ([1.rect.]

[1. Dint 1.])

In pytorch:

> importtorchast

> > a=t.from_numpy (a)

> > a

Tensor ([[1. Rect. 1.]

[1. Dtype=torch.float64 1.]],

> Bevera [:, None,:]

> > b

Tensor ([1. Rect. 1.]]

[[1. Dtype=torch.float64 1.],)

>

>

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