当前位置:网站首页>[pytorch 07] hands on deep learning chapter_ Preliminaries/ndarray exercises hands-on version
[pytorch 07] hands on deep learning chapter_ Preliminaries/ndarray exercises hands-on version
2022-07-07 10:44:00 【ECCUSXR】
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3. Through tensor shape Property to access the tensor x( Length along each axis ) The shape of the
4. Just want to know the total number of elements in the tensor , You can check its size (size)
5. Put tensor x From shape to (12,) The row vector of is transformed into a shape of (3,4) Matrix .
6. Create a shape as (2,3,4) Tensor , All elements are set to 0.
7. Create a shape as (2,3,4) Tensor , All elements are set to 1.
9. torch.tensor Create a (3,4) Two dimensional array of .
10. Realize the addition, subtraction, multiplication and division of the following two arrays
11. Exponentiate the following array
12. The following two arrays , Press the line / Splicing by columns
13. Judge whether each element of the following two homogeneous arrays is equal
14. Calculation X The sum of all elements in the array
15. Use arange Create a 3*1 Array of a and 1*2 Array of b
16. Try adding the above two arrays directly , View the situation .
19、 Modify two-dimensional array X The first 0 Xing He 1 All elements of the row are 12.
20、 take X Turn into numpy Assign to A, then A Turn into tensor In the form of B
21、 Output a Original number of 、 Character 、 floating-point
1. Import torch
2. Use arange
Create a row vector x
, This row vector contains the following elements 0 Before we start 12 It's an integer .
3. Through tensor shape
Property to access the tensor x
( Length along each axis ) Of shape
4. Just want to know the total number of elements in the tensor , You can check its size (size)
5. Put tensor x
From shape to (12,) The row vector of is transformed into a shape of (3,4) Matrix .
6. Create a shape as (2,3,4) Tensor , All elements are set to 0.
7. Create a shape as (2,3,4)
Tensor , All elements are set to 1.
8. Create a shape as (3,4) Tensor . Each of these elements has a mean value of 0、 The standard deviation is 1 The standard Gaussian distribution of ( Normal distribution ) Medium random sampling .
9. torch.tensor Create a (3,4) Two dimensional array of .
【 Operator 】
10. Realize the addition, subtraction, multiplication and division of the following two arrays
x = torch.tensor([1.0, 2, 4, 8])
y = torch.tensor([2, 2, 2, 2])
11. Exponentiate the following array
x = torch.tensor([1.0, 2, 4, 8])
12. The following two arrays , Press the line / Splicing by columns
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
Y = torch.tensor([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
13. Judge whether each element of the following two homogeneous arrays is equal
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
Y = torch.tensor([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
14. Calculation X The sum of all elements in the array
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
【 Broadcast mechanism 】
15. Use arange
Create a 3*1 Array of a and 1*2 Array of b
16. Try adding the above two arrays directly , View the situation .
【 Index and slice 】
17. Take out the two-dimensional array X in choice The last line of elements 、 The second to third lines of elements
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
18、 Modify two-dimensional array X The first 1 Xing di 2 The value of the column is 9 and Write matrix
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
19、 Modify two-dimensional array X The first 0 Xing He 1 All elements of the row are 12.
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
【 Convert objects 】
20、 take X Turn into numpy Assign to A, then A Turn into tensor In the form of B
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
21、 Output a Original number of 、 Character 、 floating-point
a = torch.tensor([3.5])
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