Accelerated Python learning - 2 days

The sky is full of stars_ 2020-11-13 00:42:07
accelerated python learning days

Pytorch The basic data structure of is tensor Tensor. A tensor is a multidimensional array .Pytorch The tensor and numpy Medium array Is very similar .

In this section, we mainly introduce the data types of tensors 、 The dimensions of tensors 、 The size of the tensor 、 Tensor sum numpy Basic concepts such as arrays .

One , The data type of the tensor

The data types of tensors and numpy.array Basically one-to-one correspondence , But not supported str type .

Include :










General neural network modeling uses torch.float32 type .

i = torch.IntTensor(1);print(i,i.dtype)
x = torch.Tensor(np.array(2.0));print(x,x.dtype) # Equivalent to torch.FloatTensor
b = torch.BoolTensor(np.array([1,0,2,0])); print(b,b.dtype)
# Different types are converted
i = torch.tensor(1); print(i,i.dtype)
x = i.float(); print(x,x.dtype) # call float Method to floating point type
y = i.type(torch.float); print(y,y.dtype) # Use type Function conversion to floating point type
z = i.type_as(x);print(z,z.dtype) # Use type_as Method is converted to some Tensor The same type 

Two , The dimensions of tensors

Different types of data can have different dimensions (dimension) To express .

The scalar is 0 D tensor , The vector is 1 D tensor , The matrix of the 2 D tensor .

Color images have rgb Three channels , It can be expressed as 3 D tensor .

Video and time dimension , It can be expressed as 4 D tensor .

It can be simply summed up as : There are several layers of brackets , It's just how many dimensional tensors .

tensor3 = torch.tensor([[[1.0,2.0],[3.0,4.0]],[[5.0,6.0],[7.0,8.0]]]) # 3 D tensor

3、 ... and , The size of the tensor

have access to shape Property or size() Method to see the length of the tensor in each dimension .

have access to view Methods change the size of the tensor .

If view Method change size failed , have access to reshape Method .

scalar = torch.tensor(True)
# Use view You can change the tensor size
vector = torch.arange(0,12)
matrix34 = vector.view(3,4)
matrix43 = vector.view(4,-1) #-1 Indicates that the length of the position is automatically inferred by the program
# Some operations distort the tensor storage structure , Use it directly view Will fail , It can be used reshape Method
matrix26 = torch.arange(0,12).view(2,6)
# Transpose operation distorts tensor storage structure
matrix62 = matrix26.t()
# Use it directly view The method will fail , have access to reshape Method
#matrix34 = matrix62.view(3,4) #error!
matrix34 = matrix62.reshape(3,4) # Equivalent to matrix34 = matrix62.contiguous().view(3,4)

Four , Tensor sum numpy Array

It can be used numpy Methods from Tensor obtain numpy Array , It can also be used. torch.from_numpy from numpy Array Tensor.

this The two methods are related Tensor and numpy Arrays are shared data memory .

If you change one of them , The value of the other one will also change .

If necessary , It can be used Tensor clone Method copy tensor , Break the link .

Besides , You can also use item Methods get the corresponding from scalar tensor Python The number .

Use tolist Methods get the corresponding from the tensor Python List of values .

import numpy as np
import torch
#torch.from_numpy Function from numpy Array Tensor
arr = np.zeros(3)
tensor = torch.from_numpy(arr)
print("before add 1:")
print("\nafter add 1:")
np.add(arr,1, out = arr) # to arr increase 1,tensor It also changes


# numpy Methods from Tensor obtain numpy Array
tensor = torch.zeros(3)
arr = tensor.numpy()
print("before add 1:")
print("\nafter add 1:")
# Use the underlined method to indicate that the calculation result is returned to the call tensor
tensor.add_(1) # to tensor increase 1,arr It also changes
# or : torch.add(tensor,1,out = tensor)


本文为[The sky is full of stars_]所创,转载请带上原文链接,感谢

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database