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原创一个月Pytorch笔记

一个月Pytorch从入门到实践学习时间：学习内容：3-2 张量，标量，向量，矩阵3-3 tensor创建理论3-4 tensor创建代码3-5 tensor创建代码学习总结：学习时间：1、周六上午 9 点-上午 12 点2、学习内容：3-2 张量，标量，向量，矩阵3-3 tensor创建理论tensor 类型 torch.float ; torch.bool ; torch.int tensor创建 3-4 tensor创建代码import torch

2020-10-28 08:54:49 329

原创波斯顿房价 (1)

#房价预测import torch#dataimport numpy as npimport reff = open(r"C:\Users\22383\Desktop\备份pytorch\000、课程配套资料\Pytorch_code-master\pytorch_code\04\cls_reg\housing.data").readlines()data = []for item in ff: out = re.sub(r"\s{2,}", " ", item).strip()

2020-11-01 17:36:38 179

原创 pytorch任课（第三章）

import torch#torch.wherea = torch.rand(4, 4)b = torch.rand(4, 4)print(a)print(b)tensor([[0.4295, 0.4639, 0.1673, 0.8253], [0.6463, 0.0885, 0.6070, 0.9644], [0.3394, 0.3260, 0.6519, 0.7562], [0.7720, 0.7656, 0.3653, 0.3863]]

2020-10-29 22:34:00 172

原创 3-11比较运算-排序

import torcha = torch.rand(2, 3)b = torch.rand(2, 3)print(a)print(b)tensor([[0.2165, 0.9520, 0.2256], [0.0793, 0.1522, 0.3120]])tensor([[0.8147, 0.2384, 0.1755], [0.6711, 0.9985, 0.4503]])#维度相同才可以比较print(torch.eq(a, b)) #返回一个te

2020-10-28 10:51:25 134

原创 3-10取整取余运算

import torcha = torch.rand(2, 2)a = a * 10print(a)print(torch.floor(a)) #向下取整print("-----\n",a)print(torch.ceil(a)) #向上取整print(torch.round(a)) #四舍五入print(torch.trunc(a)) #留整数部分print(torch.frac(a))

2020-10-28 10:05:30 1738

原创 3-9广播机制

import torcha = torch.rand(2, 2)b = torch.rand(1, 2)# a, 2*1# b, 1*2# c, 2*2# 2*4*2*3c = a + bprint(a)print(b)print(c)print(c.shape)tensor([[0.3246, 0.0065], [0.5726, 0.3850]])tensor([[0.3750, 0.1614]])tensor([[0.6996, 0.1680],

2020-10-28 09:41:21 73

原创 3-8tensor算术运算编程

import torch##adda = torch.rand(2, 3)b = torch.rand(2, 3)print(a)print(b)print(a + b)print(a.add(b))print(torch.add(a, b))print(a)print(a.add_(b))print(a)tensor([[0.8129, 0.6266, 0.2343], [0.4351, 0.9933, 0.0969]])tensor([[0.4357

2020-10-28 09:30:43 162 1

原创 3-4 tensor 创建

import torch# 1 直接定义a=torch.Tensor([[1,2],[3,4]])print(a)print(a.type())#type 有括号tensor([[1., 2.], [3., 4.]])torch.FloatTensor# 2 通过shape定义,值随机a=torch.Tensor(2,3) # 无括号print(a)print(a.shape) tensor([[ 0.0000e+00, -1.1152e-01, 5.2

2020-10-24 10:30:11 143

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