1326 lines
49 KiB
Plaintext
1326 lines
49 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3b57686b-7ac8-4897-bf76-3d982b1ff8da",
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"metadata": {},
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"source": [
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"<p style=\"text-align: center;\"><img alt=\"school-logo\" src=\"../images/school_logo.png\" style=\"zoom: 50%;\" /></p>\n",
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"\n",
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"<h1 align=\"center\">本科生《深度学习》课程<br>实验报告</h1>\n",
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"<div style=\"text-align: center;\">\n",
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" <div><span style=\"display: inline-block; width: 65px; text-align: center;\">课程名称</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">深度学习</span></div>\n",
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" <div><span style=\"display: inline-block; width: 65px; text-align: center;\">实验题目</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">Pytorch基本操作</span></div>\n",
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" <div><span style=\"display: inline-block; width: 65px; text-align: center;\">学号</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">21281280</span></div>\n",
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" <div><span style=\"display: inline-block; width: 65px; text-align: center;\">姓名</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">柯劲帆</span></div>\n",
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" <div><span style=\"display: inline-block; width: 65px; text-align: center;\">班级</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">物联网2101班</span></div>\n",
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" <div><span style=\"display: inline-block; width: 65px; text-align: center;\">指导老师</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">张淳杰</span></div>\n",
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" <div><span style=\"display: inline-block; width: 65px; text-align: center;\">报告日期</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">2023年10月9日</span></div>\n",
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"</div>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e24aa17e-faf9-4d69-9eae-43159116b56f",
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"metadata": {},
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"source": [
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"实验环境：\n",
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"- OS：Ubuntu 22.04 (Kernel: 6.2.0-34-generic)\n",
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"- CPU：12th Gen Intel(R) Core(TM) i7-12700H\n",
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"- GPU：NVIDIA GeForce RTX 3070 Ti Laptop\n",
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"- cuda: 12.2\n",
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"- conda: miniconda 23.9.0\n",
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"- python：3.10.13\n",
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"- pytorch：2.1.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a4e12268-bad4-44c4-92d5-883624d93e25",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import torch\n",
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"from torch.autograd import Variable\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"from torch import nn\n",
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"from torchvision import datasets, transforms"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cc7f0ce5-d613-425b-807c-78115632cd80",
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"metadata": {},
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"source": [
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"引用相关库。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59a43d35-56ac-4ade-995d-1c6fcbcd1262",
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"metadata": {},
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"source": [
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"# 一、Pytorch基本操作考察\n",
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"## 题目2\n",
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"**使用 𝐓𝐞𝐧𝐬𝐨𝐫 初始化一个 𝟏×𝟑 的矩阵 𝑴 和一个 𝟐×𝟏 的矩阵 𝑵，对两矩阵进行减法操作（要求实现三种不同的形式），给出结果并分析三种方式的不同（如果出现报错，分析报错的原因），同时需要指出在计算过程中发生了什么。**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "79ea46db-cf49-436c-9b5b-c6562d0da9e2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"方法1的结果:\n",
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"tensor([[-3, -2, -1],\n",
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" [-4, -3, -2]])\n",
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"方法2的结果:\n",
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"tensor([[-3, -2, -1],\n",
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" [-4, -3, -2]])\n",
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"方法3的结果:\n",
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"tensor([[-3, -2, -1],\n",
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" [-4, -3, -2]])\n"
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]
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}
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],
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"source": [
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"A = torch.tensor([[1, 2, 3]])\n",
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"\n",
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"B = torch.tensor([[4],\n",
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" [5]])\n",
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"\n",
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"# 方法1: 使用PyTorch的减法操作符\n",
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"result1 = A - B\n",
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"\n",
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"# 方法2: 使用PyTorch的sub函数\n",
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"result2 = torch.sub(A, B)\n",
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"\n",
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"# 方法3: 手动实现广播机制并作差\n",
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"def my_sub(a:torch.Tensor, b:torch.Tensor):\n",
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" if not ((a.size(0) == 1 and b.size(1) == 1) or (a.size(1) == 1 and b.size(0) == 1)):\n",
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" raise ValueError(\"输入的张量大小无法满足广播机制的条件。\")\n",
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" else:\n",
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" target_shape = torch.Size([max(A.size(0), B.size(0)), max(A.size(1), B.size(1))])\n",
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" A_broadcasted = A.expand(target_shape)\n",
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" B_broadcasted = B.expand(target_shape)\n",
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" result = torch.zeros(target_shape, dtype=torch.int64).to(device=A_broadcasted.device)\n",
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" for i in range(target_shape[0]):\n",
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" for j in range(target_shape[1]):\n",
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" result[i, j] = A_broadcasted[i, j] - B_broadcasted[i, j]\n",
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" return result\n",
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"\n",
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"result3 = my_sub(A, B)\n",
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"\n",
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"print(\"方法1的结果:\")\n",
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"print(result1)\n",
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"print(\"方法2的结果:\")\n",
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"print(result2)\n",
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"print(\"方法3的结果:\")\n",
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||
"print(result3)"
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||
]
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||
},
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||
{
|
||
"cell_type": "markdown",
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||
"id": "bd9bd5cc-b6da-4dd6-a599-76498bc5247d",
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||
"metadata": {},
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||
"source": [
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||
"第1、2、3种减法形式实质是一样的。\n",
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"\n",
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||
"步骤如下：\n",
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"1. 对A、B两个张量进行广播，将A、B向广播的方向复制，得到两个$\\max(A.size(0), B.size(0))\\times \\max(A.size(1), B.size(1))$的张量；\n",
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||
"2. 对广播后的两个张量作差，尺寸不变。\n",
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"\n",
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||
"第1种减法形式和第2种是等价的，前者是后者的符号化表示。\n",
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"\n",
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"第3种形式是手动实现的，将上述两个步骤分别手动实现了。但是torch.Tensor还内置了其他机制，这里仅模拟了广播和作差。"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"id": "2489a3ad-f6ff-4561-bb26-e02654090b98",
|
||
"metadata": {},
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||
"source": [
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||
"## 题目2\n",
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"1. **利用Tensor创建两个大小分别$3\\times 2$和$4\\times 2$的随机数矩阵$P$和$Q$，要求服从均值为$0$，标准差$0.01$为的正态分布；**\n",
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"2. **对第二步得到的矩阵$Q$进行形状变换得到$Q$的转置$Q^T$；**\n",
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"3. **对上述得到的矩阵$P$和矩阵$Q^T$求矩阵相乘。**"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
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||
"id": "41e4ee02-1d05-4101-b3f0-477bac0277fb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
"矩阵 P:\n",
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"tensor([[ 0.0069, 0.0082],\n",
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" [-0.0052, -0.0124],\n",
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" [ 0.0055, -0.0014]])\n",
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||
"矩阵 Q:\n",
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"tensor([[ 0.0050, 0.0075],\n",
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||
" [ 0.0161, 0.0070],\n",
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" [-0.0009, -0.0014],\n",
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" [-0.0003, 0.0024]])\n",
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||
"矩阵 QT:\n",
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||
"tensor([[ 0.0050, 0.0161, -0.0009, -0.0003],\n",
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" [ 0.0075, 0.0070, -0.0014, 0.0024]])\n",
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||
"矩阵相乘的结果:\n",
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||
"tensor([[ 9.6016e-05, 1.6860e-04, -1.7451e-05, 1.8011e-05],\n",
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||
" [-1.1894e-04, -1.7065e-04, 2.1900e-05, -2.8712e-05],\n",
|
||
" [ 1.6918e-05, 7.8455e-05, -2.7165e-06, -4.9904e-06]])\n"
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||
]
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||
}
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||
],
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||
"source": [
|
||
"mean = 0\n",
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||
"stddev = 0.01\n",
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||
"\n",
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||
"P = torch.normal(mean=mean, std=stddev, size=(3, 2))\n",
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"Q = torch.normal(mean=mean, std=stddev, size=(4, 2))\n",
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"\n",
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||
"print(\"矩阵 P:\")\n",
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||
"print(P)\n",
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"print(\"矩阵 Q:\")\n",
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"print(Q)\n",
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"\n",
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||
"# 对矩阵Q进行转置操作，得到矩阵Q的转置Q^T\n",
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||
"QT = Q.T\n",
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||
"print(\"矩阵 QT:\")\n",
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||
"print(QT)\n",
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"\n",
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||
"# 计算矩阵P和矩阵Q^T的矩阵相乘\n",
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||
"result = torch.matmul(P, QT)\n",
|
||
"print(\"矩阵相乘的结果:\")\n",
|
||
"print(result)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cea9cb6d-adde-4e08-b9f2-8c417abf4231",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 题目2\n",
|
||
"**给定公式$ y_3=y_1+y_2=𝑥^2+𝑥^3$，且$x=1$。利用学习所得到的Tensor的相关知识，求$y_3$对$x$的梯度，即$\\frac{dy_3}{dx}$。**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "951512cd-d915-4d04-959f-eb99d1971e2d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
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||
"text": [
|
||
"仅通过y_1传递的梯度: 2.0\n",
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||
"仅通过y_2传递的梯度: 3.0\n",
|
||
"dy_3/dx: 5.0\n"
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||
]
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||
}
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||
],
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||
"source": [
|
||
"x = torch.tensor(1.0, requires_grad=True)\n",
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"\n",
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"y_1 = x ** 2\n",
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||
"with torch.no_grad():\n",
|
||
" y_2 = x ** 3\n",
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"y_3 = y_1 + y_2\n",
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"y_3.backward()\n",
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||
"print(\"仅通过y_1传递的梯度: \", x.grad.item())\n",
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"\n",
|
||
"x.grad.data.zero_()\n",
|
||
"with torch.no_grad():\n",
|
||
" y_1 = x ** 2\n",
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||
"y_2 = x ** 3\n",
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"y_3 = y_1 + y_2\n",
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"y_3.backward()\n",
|
||
"print(\"仅通过y_2传递的梯度: \", x.grad.item())\n",
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"\n",
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"x.grad.data.zero_()\n",
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"y_1 = x ** 2\n",
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"y_2 = x ** 3\n",
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"y_3 = y_1 + y_2\n",
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"y_3.backward()\n",
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"\n",
|
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"print(\"dy_3/dx: \", x.grad.item())"
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||
]
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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3269dbf6-889a-49eb-8094-1e588e1a6c30",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 二、动手实现logistic回归\n",
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||
"## 题目1\n",
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||
"**要求动手从0实现 logistic 回归（只借助Tensor和Numpy相关的库）在人工构造的数据集上进行训练和测试，并从loss以及训练集上的准确率等多个角度对结果进行分析（可借助nn.BCELoss或nn.BCEWithLogitsLoss作为损失函数，从零实现二元交叉熵为选作）**"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bcd12aa9-f187-4d88-8c59-af6d16107edb",
|
||
"metadata": {},
|
||
"source": [
|
||
"给定预测概率$ \\left( \\hat{y} \\right) $和目标标签$ \\left( y \\right)$（通常是0或1），BCELoss的计算公式如下：\n",
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"$$\n",
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||
" \\text{BCELoss}(\\hat{y}, y) = -\\frac{1}{N} \\sum_{i=1}^{N} \\left(y_i \\cdot \\log(\\hat{y}_i) + (1 - y_i) \\cdot \\log(1 - \\hat{y}_i)\\right) \n",
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"$$\n",
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"其中，$\\left( N \\right) $是样本数量，$\\left( \\hat{y}_i \\right) $表示模型的预测概率向量中的第$ \\left( i \\right) $个元素，$\\left( y_i \\right) $表示实际的目标标签中的第$ \\left( i \\right) $个元素。在二分类问题中，$\\left( y_i \\right) $通常是0或1。这个公式表示对所有样本的二分类交叉熵损失进行了求和并取平均。\n",
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"\n",
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||
"因此BCELoss的手动实现如下。"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "e31b86ec-4114-48dd-8d73-fe4e0686419a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"输入：\n",
|
||
"tensor([0.6900])\n",
|
||
"标签：\n",
|
||
"tensor([1.])\n",
|
||
"My_BCELoss损失值: 0.37110066413879395\n",
|
||
"nn.BCELoss损失值: 0.37110066413879395\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_BCELoss:\n",
|
||
" def __call__(self, prediction: torch.Tensor, target: torch.Tensor):\n",
|
||
" loss = -torch.mean(target * torch.log(prediction) + (1 - target) * torch.log(1 - prediction))\n",
|
||
" return loss\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试\n",
|
||
"prediction = torch.sigmoid(torch.tensor([0.8]))\n",
|
||
"target = torch.tensor([1.0])\n",
|
||
"print(f\"输入：\\n{prediction}\")\n",
|
||
"print(f\"标签：\\n{target}\")\n",
|
||
"\n",
|
||
"my_bce_loss = My_BCELoss()\n",
|
||
"my_loss = my_bce_loss(prediction, target)\n",
|
||
"print(\"My_BCELoss损失值:\", my_loss.item())\n",
|
||
"\n",
|
||
"nn_bce_loss = nn.BCELoss()\n",
|
||
"nn_loss = nn_bce_loss(prediction, target)\n",
|
||
"print(\"nn.BCELoss损失值:\", nn_loss.item())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "345b0300-8808-4c43-9bf9-05a7e6e1f5af",
|
||
"metadata": {},
|
||
"source": [
|
||
"Optimizer的实现较为简单。\n",
|
||
"\n",
|
||
"主要实现：\n",
|
||
"- 传入参数：`__init__()`\n",
|
||
"- 对传入的参数进行更新：`step()`\n",
|
||
"- 清空传入参数存储的梯度：`zero_grad()`\n",
|
||
"\n",
|
||
"但是有一点需要注意，就是需要将传进来的`params`参数转化为`list`类型。因为`nn.Module`的`parameters()`方法会以`<class 'generator'>`的类型返回模型的参数，但是该类型变量无法像`list`一样使用`for`循环遍历。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "0297066c-9fc1-448d-bdcb-29a6f1519117",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"x的初始值: 1.0\n",
|
||
"学习率: 0.1\n",
|
||
"y.backward()之后，x的梯度: 2.0\n",
|
||
"optimizer_test.step()之后，x的值: 0.800000011920929\n",
|
||
"optimizer_test.zero_grad()之后，x的梯度: 0.0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_Optimizer:\n",
|
||
" def __init__(self, params: list[torch.Tensor], lr: float):\n",
|
||
" self.params = list(params)\n",
|
||
" self.lr = lr\n",
|
||
"\n",
|
||
" def step(self):\n",
|
||
" for param in self.params:\n",
|
||
" param.data = param.data - self.lr * param.grad.data\n",
|
||
"\n",
|
||
" def zero_grad(self):\n",
|
||
" for param in self.params:\n",
|
||
" if param.grad is not None:\n",
|
||
" param.grad.data.zero_()\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试\n",
|
||
"x = torch.tensor(1.0, requires_grad=True)\n",
|
||
"print(\"x的初始值: \", x.item())\n",
|
||
"\n",
|
||
"optimizer_test = My_Optimizer([x], lr=0.1)\n",
|
||
"print(\"学习率: \", optimizer_test.lr)\n",
|
||
"\n",
|
||
"y = x ** 2\n",
|
||
"y.backward()\n",
|
||
"print(\"y.backward()之后，x的梯度: \", x.grad.item())\n",
|
||
"\n",
|
||
"optimizer_test.step()\n",
|
||
"print(\"optimizer_test.step()之后，x的值: \", x.item())\n",
|
||
"\n",
|
||
"optimizer_test.zero_grad()\n",
|
||
"print(\"optimizer_test.zero_grad()之后，x的梯度: \", x.grad.item())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6ab83528-a88b-4d66-b0c9-b1315cf75c22",
|
||
"metadata": {},
|
||
"source": [
|
||
"线性层主要有一个权重（weight）和一个偏置（bias）。\n",
|
||
"线性层的数学公式如下：\n",
|
||
"$$\n",
|
||
"x:=x \\times weight^T+bias\n",
|
||
"$$\n",
|
||
"因此代码实现如下："
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "8e18695a-d8c5-4f77-8b5c-de40d9240fb9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"输入：\n",
|
||
"tensor([[1.],\n",
|
||
" [2.]], requires_grad=True)\n",
|
||
"权重：\n",
|
||
"tensor([[ 0.4240],\n",
|
||
" [-0.2577],\n",
|
||
" [ 0.4972]])\n",
|
||
"偏置：\n",
|
||
"tensor([0.6298, 0.6243, 0.8217])\n",
|
||
"My_Linear输出：\n",
|
||
"tensor([[1.0539, 0.3666, 1.3189],\n",
|
||
" [1.4779, 0.1089, 1.8161]], grad_fn=<AddBackward0>)\n",
|
||
"nn.Linear输出：\n",
|
||
"tensor([[1.0539, 0.3666, 1.3189],\n",
|
||
" [1.4779, 0.1089, 1.8161]], grad_fn=<AddmmBackward0>)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_Linear:\n",
|
||
" def __init__(self, input_feature: int, output_feature: int):\n",
|
||
" self.weight = torch.randn((output_feature, input_feature), requires_grad=True, dtype=torch.float32)\n",
|
||
" self.bias = torch.randn(1, requires_grad=True, dtype=torch.float32)\n",
|
||
" self.params = [self.weight, self.bias]\n",
|
||
"\n",
|
||
" def __call__(self, x: torch.Tensor):\n",
|
||
" return self.forward(x)\n",
|
||
"\n",
|
||
" def forward(self, x: torch.Tensor):\n",
|
||
" x = torch.matmul(x, self.weight.T) + self.bias\n",
|
||
" return x\n",
|
||
"\n",
|
||
" def to(self, device: str):\n",
|
||
" for param in self.params:\n",
|
||
" param.data = param.data.to(device=device)\n",
|
||
" return self\n",
|
||
"\n",
|
||
" def parameters(self):\n",
|
||
" return self.params\n",
|
||
"\n",
|
||
" \n",
|
||
"# 测试\n",
|
||
"my_linear = My_Linear(1, 3)\n",
|
||
"nn_linear = nn.Linear(1, 3)\n",
|
||
"my_linear.weight = nn_linear.weight.clone().requires_grad_()\n",
|
||
"my_linear.bias = nn_linear.bias.clone().requires_grad_()\n",
|
||
"x = torch.tensor([[1.], [2.]], requires_grad=True)\n",
|
||
"print(f\"输入：\\n{x}\")\n",
|
||
"print(f\"权重：\\n{my_linear.weight.data}\")\n",
|
||
"print(f\"偏置：\\n{my_linear.bias.data}\")\n",
|
||
"y_my_linear = my_linear(x)\n",
|
||
"print(f\"My_Linear输出：\\n{y_my_linear}\")\n",
|
||
"y_nn_linear = nn_linear(x)\n",
|
||
"print(f\"nn.Linear输出：\\n{y_nn_linear}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5ff813cc-c1f0-4c73-a3e8-d6796ef5d366",
|
||
"metadata": {},
|
||
"source": [
|
||
"手动实现logistic回归模型。\n",
|
||
"\n",
|
||
"模型很简单，主要由一个线性层和一个sigmoid层组成。\n",
|
||
"\n",
|
||
"Sigmoid函数（又称为 Logistic函数）是一种常用的激活函数，通常用于神经网络的输出层或隐藏层，其作用是将输入的实数值压缩到一个范围在0和1之间的数值。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "e7de7e4b-a084-4793-812e-46e8550ecd8d",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Model_2_1():\n",
|
||
" def __init__(self):\n",
|
||
" self.linear = My_Linear(1, 1)\n",
|
||
" self.params = self.linear.params\n",
|
||
"\n",
|
||
" def __call__(self, x):\n",
|
||
" return self.forward(x)\n",
|
||
"\n",
|
||
" def forward(self, x):\n",
|
||
" x = self.linear(x)\n",
|
||
" x = torch.sigmoid(x)\n",
|
||
" return x\n",
|
||
"\n",
|
||
" def to(self, device: str):\n",
|
||
" for param in self.params:\n",
|
||
" param.data = param.data.to(device=device)\n",
|
||
" return self\n",
|
||
"\n",
|
||
" def parameters(self):\n",
|
||
" return self.params"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e14acea9-e5ef-4c24-aea9-329647224ce1",
|
||
"metadata": {},
|
||
"source": [
|
||
"人工随机构造数据集。\n",
|
||
"\n",
|
||
"这里我遇到了比较大的问题。因为数据构建不合适，会导致后面的训练出现梯度爆炸。\n",
|
||
"\n",
|
||
"我采用随机产生数据后归一化的方法，即\n",
|
||
"$$\n",
|
||
"\\hat{x} = \\frac{x - \\text{min}_x}{\\text{max}_x - \\text{min}_x} \n",
|
||
"$$\n",
|
||
"将数据控制在合适的区间。\n",
|
||
"\n",
|
||
"我的y设置为$4-3\\times x + noise$，noise为随机噪声。\n",
|
||
"\n",
|
||
"生成完x和y后进行归一化处理，并写好DataLoader访问数据集的接口`__getitem__()`。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "c39fbafb-62e4-4b8c-9d65-6718d25f2970",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"测试数据集大小：1000000\n",
|
||
"测试数据集第0对数据：\n",
|
||
"x_0 = 0.5488133381316141\n",
|
||
"y_0 = 0.45217091576438073\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_Dataset(Dataset):\n",
|
||
" def __init__(self, data_size=1000000):\n",
|
||
" np.random.seed(0)\n",
|
||
" x = 2 * np.random.rand(data_size, 1)\n",
|
||
" noise = 0.2 * np.random.randn(data_size, 1)\n",
|
||
" y = 4 - 3 * x + noise\n",
|
||
" self.min_x, self.max_x = np.min(x), np.max(x)\n",
|
||
" min_y, max_y = np.min(y), np.max(y)\n",
|
||
" x = (x - self.min_x) / (self.max_x - self.min_x)\n",
|
||
" y = (y - min_y) / (max_y - min_y)\n",
|
||
" self.data = [[x[i][0], y[i][0]] for i in range(x.shape[0])]\n",
|
||
"\n",
|
||
" def __len__(self):\n",
|
||
" return len(self.data)\n",
|
||
"\n",
|
||
" def __getitem__(self, index):\n",
|
||
" x, y = self.data[index]\n",
|
||
" return x, y\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试，并后面的训练创建变量\n",
|
||
"dataset = My_Dataset()\n",
|
||
"dataset_size = len(dataset)\n",
|
||
"print(f\"测试数据集大小：{dataset_size}\")\n",
|
||
"x0, y0 = dataset[0]\n",
|
||
"print(f\"测试数据集第0对数据：\")\n",
|
||
"print(f\"x_0 = {x0}\")\n",
|
||
"print(f\"y_0 = {y0}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "957a76a2-b306-47a8-912e-8fbf00cdfd42",
|
||
"metadata": {},
|
||
"source": [
|
||
"训练Logistic回归模型。\n",
|
||
"进行如下步骤：\n",
|
||
"1. 初始化超参数\n",
|
||
"2. 获取数据集\n",
|
||
"3. 初始化模型\n",
|
||
"4. 定义损失函数和优化器\n",
|
||
"5. 训练\n",
|
||
" 1. 从训练dataloader中获取批量数据\n",
|
||
" 2. 传入模型\n",
|
||
" 3. 使用损失函数计算与ground_truth的损失\n",
|
||
" 4. 使用优化器进行反向传播\n",
|
||
" 5. 循环以上步骤\n",
|
||
"6. 测试\n",
|
||
" 1. 设置测试数据\n",
|
||
" 2. 传入模型\n",
|
||
" 3. 得到预测值"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "5612661e-2809-4d46-96c2-33ee9f44116d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/10, Loss: 688.6783249974251, Acc: 0.9766838179955138\n",
|
||
"Epoch 2/10, Loss: 679.506599009037, Acc: 0.992039453911494\n",
|
||
"Epoch 3/10, Loss: 677.644762635231, Acc: 0.9961844975781526\n",
|
||
"Epoch 4/10, Loss: 677.2690716981888, Acc: 0.998395304269398\n",
|
||
"Epoch 5/10, Loss: 677.1928514242172, Acc: 0.9993592246184307\n",
|
||
"Epoch 6/10, Loss: 677.1781670451164, Acc: 0.9996570376204033\n",
|
||
"Epoch 7/10, Loss: 677.1744618415833, Acc: 0.9998465339227576\n",
|
||
"Epoch 8/10, Loss: 677.1738814711571, Acc: 0.9998001679325041\n",
|
||
"Epoch 9/10, Loss: 677.1742851734161, Acc: 0.9998804348705138\n",
|
||
"Epoch 10/10, Loss: 677.1740592718124, Acc: 0.9999446971149187\n",
|
||
"Model weights: -0.0037125118542462587, bias: 0.017451055347919464\n",
|
||
"Prediction for test data: 0.5034345984458923\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 5e-2\n",
|
||
"num_epochs = 10\n",
|
||
"batch_size = 1024\n",
|
||
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
|
||
"\n",
|
||
"dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"\n",
|
||
"model = Model_2_1().to(device)\n",
|
||
"criterion = My_BCELoss()\n",
|
||
"optimizer = My_Optimizer(model.parameters(), lr=learning_rate)\n",
|
||
"\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" total_epoch_loss = 0\n",
|
||
" total_epoch_pred = 0\n",
|
||
" total_epoch_target = 0\n",
|
||
" for x, targets in dataloader:\n",
|
||
" optimizer.zero_grad()\n",
|
||
" \n",
|
||
" x = x.to(device).to(dtype=torch.float32)\n",
|
||
" targets = targets.to(device).to(dtype=torch.float32)\n",
|
||
" \n",
|
||
" x = x.unsqueeze(1)\n",
|
||
" y_pred = model(x)\n",
|
||
" loss = criterion(y_pred, targets)\n",
|
||
" total_epoch_loss += loss.item()\n",
|
||
" total_epoch_target += targets.sum().item()\n",
|
||
" total_epoch_pred += y_pred.sum().item()\n",
|
||
"\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
"\n",
|
||
" print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {total_epoch_loss}, \", end=\"\")\n",
|
||
" print(f\"Acc: {1 - abs(total_epoch_pred - total_epoch_target) / total_epoch_target}\")\n",
|
||
"\n",
|
||
"with torch.no_grad():\n",
|
||
" test_data = (np.array([[2]]) - dataset.min_x) / (dataset.max_x - dataset.min_x)\n",
|
||
" test_data = Variable(torch.tensor(test_data, dtype=torch.float32), requires_grad=False).to(device)\n",
|
||
" predicted = model(test_data).to(\"cpu\")\n",
|
||
" print(f\"Model weights: {model.linear.weight.item()}, bias: {model.linear.bias.item()}\")\n",
|
||
" print(f\"Prediction for test data: {predicted.item()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9e416582-a30d-4084-acc6-6e05f80a6aff",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 题目2\n",
|
||
"**利用 torch.nn 实现 logistic 回归在人工构造的数据集上进行训练和测试，并对结果进行分析，并从loss以及训练集上的准确率等多个角度对结果进行分析**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0460d125-7d03-44fe-845c-c4d13792e241",
|
||
"metadata": {},
|
||
"source": [
|
||
"使用torch.nn实现模型。\n",
|
||
"\n",
|
||
"将之前的Model_2_1中的手动实现函数改为torch.nn内置函数即可，再加上继承nn.Module以使用torch.nn内置模型模板特性。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "fa121afd-a1af-4193-9b54-68041e0ed068",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Model_2_2(nn.Module):\n",
|
||
" def __init__(self):\n",
|
||
" super(Model_2_2, self).__init__()\n",
|
||
" self.linear = nn.Linear(1, 1, dtype=torch.float64)\n",
|
||
"\n",
|
||
" def forward(self, x):\n",
|
||
" x = self.linear(x)\n",
|
||
" x = torch.sigmoid(x)\n",
|
||
" return x"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "176eee7e-4e3d-470e-8af2-8761bca039f8",
|
||
"metadata": {},
|
||
"source": [
|
||
"训练与测试过程与之前手动实现的几乎一致。仅有少量涉及数据类型（dtype）的代码需要更改以适应torch.nn的内置函数要求。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "93b0fdb6-be8b-4663-b59e-05ed19a9ea09",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/10, Loss: 660.2008021697803, Acc: 0.9355364605682331\n",
|
||
"Epoch 2/10, Loss: 589.2025169091534, Acc: 0.9769773185253259\n",
|
||
"Epoch 3/10, Loss: 572.7106042209589, Acc: 0.9881629137259633\n",
|
||
"Epoch 4/10, Loss: 568.0903503441508, Acc: 0.9935173218188225\n",
|
||
"Epoch 5/10, Loss: 566.6528526848851, Acc: 0.9962586560919562\n",
|
||
"Epoch 6/10, Loss: 566.1778871576632, Acc: 0.9978209774304773\n",
|
||
"Epoch 7/10, Loss: 566.0143385848835, Acc: 0.9987369762885633\n",
|
||
"Epoch 8/10, Loss: 565.9605239629793, Acc: 0.9992563563084009\n",
|
||
"Epoch 9/10, Loss: 565.9402079010808, Acc: 0.9995321069396558\n",
|
||
"Epoch 10/10, Loss: 565.9281422200424, Acc: 0.9997496312356398\n",
|
||
"Model weights: -3.6833968323036084, bias: 1.8628376037952126\n",
|
||
"Prediction for test data: 0.13936666014014443\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 5e-2\n",
|
||
"num_epochs = 10\n",
|
||
"batch_size = 1024\n",
|
||
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
|
||
"\n",
|
||
"dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"\n",
|
||
"model = Model_2_2().to(device)\n",
|
||
"criterion = nn.BCELoss()\n",
|
||
"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
|
||
"\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" total_epoch_loss = 0\n",
|
||
" total_epoch_pred = 0\n",
|
||
" total_epoch_target = 0\n",
|
||
" for x, targets in dataloader:\n",
|
||
" optimizer.zero_grad()\n",
|
||
"\n",
|
||
" x = x.to(device)\n",
|
||
" targets = targets.to(device)\n",
|
||
"\n",
|
||
" x = x.unsqueeze(1)\n",
|
||
" targets = targets.unsqueeze(1)\n",
|
||
" y_pred = model(x)\n",
|
||
" loss = criterion(y_pred, targets)\n",
|
||
" total_epoch_loss += loss.item()\n",
|
||
" total_epoch_target += targets.sum().item()\n",
|
||
" total_epoch_pred += y_pred.sum().item()\n",
|
||
"\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
"\n",
|
||
" print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {total_epoch_loss}, \", end=\"\")\n",
|
||
" print(f\"Acc: {1 - abs(total_epoch_pred - total_epoch_target) / total_epoch_target}\")\n",
|
||
"\n",
|
||
"with torch.no_grad():\n",
|
||
" test_data = (np.array([[2]]) - dataset.min_x) / (dataset.max_x - dataset.min_x)\n",
|
||
" test_data = Variable(torch.tensor(test_data, dtype=torch.float64), requires_grad=False).to(device)\n",
|
||
" predicted = model(test_data).to(\"cpu\")\n",
|
||
" print(f\"Model weights: {model.linear.weight.item()}, bias: {model.linear.bias.item()}\")\n",
|
||
" print(f\"Prediction for test data: {predicted.item()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e6bff679-f8d2-46cc-bdcb-82af7dab38b3",
|
||
"metadata": {},
|
||
"source": [
|
||
"对比发现，手动实现的损失函数和优化器与torch.nn的内置损失函数和优化器相比，表现差不多。\n",
|
||
"\n",
|
||
"但是为什么相同分布的数据集训练出的权重和偏置，以及预测结果存在较大差别，这个问题的原因还有待我探究。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ef41d7fa-c2bf-4024-833b-60af0a87043a",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 三、动手实现softmax回归\n",
|
||
"\n",
|
||
"## 问题1\n",
|
||
"\n",
|
||
"**要求动手从0实现softmax回归（只借助Tensor和Numpy相关的库）在Fashion-MNIST数据集上进行训练和测试，并从loss、训练集以及测试集上的准确率等多个角度对结果进行分析（要求从零实现交叉熵损失函数）**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3c356760-75a8-4814-ba69-73b270396a4e",
|
||
"metadata": {},
|
||
"source": [
|
||
"手动实现nn.one_hot()。\n",
|
||
"\n",
|
||
"one-hot向量用于消除线性标签值所映射的类别的非线性。\n",
|
||
"\n",
|
||
"one-hot向量是使用一个长度为分类数量的数组表示标签值，其中有且仅有1个值为为1，该值的下标为标签值；其余为0。\n",
|
||
"\n",
|
||
"原理很简单，步骤如下：\n",
|
||
"1. 初始化全零的张量，大小为（标签数量，分类数量）；\n",
|
||
"2. 将标签值映射到全零张量的\\[下标，标签值\\]中，将该位置为1；\n",
|
||
"3. 返回修改后的张量，即是ont-hot向量。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "e605f1b0-1d32-410f-bddf-402a85ccc9ff",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"输入：\n",
|
||
"tensor([2, 1, 0])\n",
|
||
"my_one_hot输出：\n",
|
||
"tensor([[0, 0, 1, 0, 0],\n",
|
||
" [0, 1, 0, 0, 0],\n",
|
||
" [1, 0, 0, 0, 0]])\n",
|
||
"nn.functional.one_hot输出：\n",
|
||
"tensor([[0, 0, 1, 0, 0],\n",
|
||
" [0, 1, 0, 0, 0],\n",
|
||
" [1, 0, 0, 0, 0]])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def my_one_hot(indices: torch.Tensor, num_classes: int):\n",
|
||
" one_hot_tensor = torch.zeros(len(indices), num_classes).to(indices.device).to(dtype=torch.int64)\n",
|
||
" one_hot_tensor.scatter_(1, indices.view(-1, 1), 1)\n",
|
||
" return one_hot_tensor\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试\n",
|
||
"x = torch.tensor([2, 1, 0], dtype=torch.int64)\n",
|
||
"print(f\"输入：\\n{x}\")\n",
|
||
"\n",
|
||
"x_my_onehot = my_one_hot(x, 5)\n",
|
||
"print(f\"my_one_hot输出：\\n{x_my_onehot}\")\n",
|
||
"\n",
|
||
"x_nn_F_onehot = nn.functional.one_hot(x, 5)\n",
|
||
"print(f\"nn.functional.one_hot输出：\\n{x_nn_F_onehot}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "902603a6-bfb9-4ce3-bd0d-b00cebb1d3cb",
|
||
"metadata": {},
|
||
"source": [
|
||
"手动实现CrossEntropyLoss。\n",
|
||
"\n",
|
||
"CrossEntropyLoss由一个log_softmax和一个nll_loss组成。\n",
|
||
"\n",
|
||
"softmax的数学表达式如下：\n",
|
||
"$$\n",
|
||
"\\text{softmax}(y_i) = \\frac{e^{y_i - \\text{max}(y)}}{\\sum_{j=1}^{N} e^{y_j - \\text{max}(y)}} \n",
|
||
"$$\n",
|
||
"log_softmax即为$\\log\\left(softmax\\left(y\\right)\\right)$。\n",
|
||
"\n",
|
||
"CrossEntropyLoss的数学表达式如下：\n",
|
||
"$$\n",
|
||
"\\text{CrossEntropyLoss}(y, \\hat{y}) = -\\frac{1}{N} \\sum_{i=1}^{N} \\hat{y}_i \\cdot \\log(\\text{softmax}(y_i)) \n",
|
||
"$$\n",
|
||
"\n",
|
||
"故代码如下："
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "759a3bb2-b5f4-4ea5-a2d7-15f0c4cdd14b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"输入：\n",
|
||
"tensor([[ 0.7600, 0.4269, 0.7948, -0.6086, 1.2527],\n",
|
||
" [-0.4749, 0.5720, -0.0164, -0.2126, -0.0410],\n",
|
||
" [ 1.3269, 1.8524, -0.9815, 0.0156, 1.6971]], requires_grad=True)\n",
|
||
"标签：\n",
|
||
"tensor([[0., 1., 0., 0., 0.],\n",
|
||
" [0., 0., 0., 1., 0.],\n",
|
||
" [1., 0., 0., 0., 0.]])\n",
|
||
"My_CrossEntropyLoss损失值: 1.7417106628417969\n",
|
||
"nn.CrossEntropyLoss损失值: 1.7417105436325073\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_CrossEntropyLoss:\n",
|
||
" def __call__(self, predictions: torch.Tensor, targets: torch.Tensor):\n",
|
||
" max_values = torch.max(predictions, dim=1, keepdim=True).values\n",
|
||
" exp_values = torch.exp(predictions - max_values)\n",
|
||
" softmax_output = exp_values / torch.sum(exp_values, dim=1, keepdim=True)\n",
|
||
" log_probs = torch.log(softmax_output)\n",
|
||
" \n",
|
||
" nll_loss = -torch.sum(targets * log_probs, dim=1)\n",
|
||
" average_loss = torch.mean(nll_loss)\n",
|
||
" return average_loss\n",
|
||
"\n",
|
||
" \n",
|
||
"# 测试\n",
|
||
"input = torch.randn(3, 5, requires_grad=True)\n",
|
||
"target = torch.randn(3, 5).softmax(dim=1).argmax(1)\n",
|
||
"target = torch.nn.functional.one_hot(target, num_classes=5).to(dtype=torch.float32)\n",
|
||
"print(f\"输入：\\n{input}\")\n",
|
||
"print(f\"标签：\\n{target}\")\n",
|
||
"\n",
|
||
"my_crossentropyloss = My_CrossEntropyLoss()\n",
|
||
"my_loss = my_crossentropyloss(input, target)\n",
|
||
"print(\"My_CrossEntropyLoss损失值:\", my_loss.item())\n",
|
||
"\n",
|
||
"nn_crossentropyloss = nn.CrossEntropyLoss()\n",
|
||
"nn_loss = nn_crossentropyloss(input, target)\n",
|
||
"print(\"nn.CrossEntropyLoss损失值:\", nn_loss.item())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dbf78501-f5be-4008-986c-d331d531491f",
|
||
"metadata": {},
|
||
"source": [
|
||
"手动实现Flatten。\n",
|
||
"\n",
|
||
"原理很简单，就是把多维的张量拉直成一个向量。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "74322629-8325-4823-b80f-f28182d577c1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Flatten之前的x：\n",
|
||
"tensor([[[1., 2.],\n",
|
||
" [3., 4.]],\n",
|
||
"\n",
|
||
" [[5., 6.],\n",
|
||
" [7., 8.]]])\n",
|
||
"My_Flatten之后的x：\n",
|
||
"tensor([[1., 2., 3., 4.],\n",
|
||
" [5., 6., 7., 8.]])\n",
|
||
"nn.Flatten之后的x：\n",
|
||
"tensor([[1., 2., 3., 4.],\n",
|
||
" [5., 6., 7., 8.]])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_Flatten:\n",
|
||
" def __call__(self, x: torch.Tensor):\n",
|
||
" return self.forward(x)\n",
|
||
"\n",
|
||
" def forward(self, x: torch.Tensor):\n",
|
||
" x = x.view(x.shape[0], -1)\n",
|
||
" return x\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试\n",
|
||
"my_flatten = My_Flatten()\n",
|
||
"nn_flatten = nn.Flatten()\n",
|
||
"x = torch.tensor([[[1., 2.],\n",
|
||
" [3., 4.]],\n",
|
||
" [[5., 6.],\n",
|
||
" [7., 8.]]])\n",
|
||
"print(f\"Flatten之前的x：\\n{x}\")\n",
|
||
"x_my_flatten = my_flatten(x)\n",
|
||
"print(f\"My_Flatten之后的x：\\n{x_my_flatten}\")\n",
|
||
"x_nn_flatten = nn_flatten(x)\n",
|
||
"print(f\"nn.Flatten之后的x：\\n{x_nn_flatten}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "35aee905-ae37-4faa-a7f1-a04cd8579f78",
|
||
"metadata": {},
|
||
"source": [
|
||
"手动实现softmax回归模型。\n",
|
||
"\n",
|
||
"模型很简单，主要由一个Flatten层和一个线性层组成。\n",
|
||
"\n",
|
||
"Flatten层主要用于将2维的图像展开，直接作为1维的特征量输入网络。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "bb31a75e-464c-4b94-b927-b219a765e35d",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Model_3_1:\n",
|
||
" def __init__(self, num_classes):\n",
|
||
" self.flatten = My_Flatten()\n",
|
||
" self.linear = My_Linear(28 * 28, num_classes)\n",
|
||
" self.params = self.linear.params\n",
|
||
"\n",
|
||
" def __call__(self, x: torch.Tensor):\n",
|
||
" return self.forward(x)\n",
|
||
"\n",
|
||
" def forward(self, x: torch.Tensor):\n",
|
||
" x = self.flatten(x)\n",
|
||
" x = self.linear(x)\n",
|
||
" return x\n",
|
||
"\n",
|
||
" def to(self, device: str):\n",
|
||
" for param in self.params:\n",
|
||
" param.data = param.data.to(device=device)\n",
|
||
" return self\n",
|
||
"\n",
|
||
" def parameters(self):\n",
|
||
" return self.params"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "17e686d1-9c9a-4727-8fdc-9990d348c523",
|
||
"metadata": {},
|
||
"source": [
|
||
"训练与测试过程与之前手动实现的几乎一致。由于数据集的变化，对应超参数也进行了调整。\n",
|
||
"\n",
|
||
"数据集也使用了现成的FashionMNIST数据集，且划分了训练集和测试集。\n",
|
||
"\n",
|
||
"FashionMNIST数据集直接调用API获取。数据集的image为28*28的单通道灰白图片，label为单个数值标签。\n",
|
||
"\n",
|
||
"训练softmax回归模型。\n",
|
||
"进行如下步骤：\n",
|
||
"1. 初始化超参数\n",
|
||
"2. 获取数据集\n",
|
||
"3. 初始化模型\n",
|
||
"4. 定义损失函数和优化器\n",
|
||
"5. 训练\n",
|
||
" 1. 从训练dataloader中获取批量数据\n",
|
||
" 2. 传入模型\n",
|
||
" 3. 使用损失函数计算与ground_truth的损失\n",
|
||
" 4. 使用优化器进行反向传播\n",
|
||
" 5. 循环以上步骤\n",
|
||
"6. 测试\n",
|
||
" 1. 从测试dataloader中获取批量数据\n",
|
||
" 2. 传入模型\n",
|
||
" 3. 将预测值与ground_truth进行比较，得出正确率\n",
|
||
" 4. 对整个训练集统计正确率，从而分析训练效果"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "d816dae1-5fbe-4c29-9597-19d66b5eb6b4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/10, Loss: 87.64246368408203, Acc: 0.45329999923706055\n",
|
||
"Epoch 2/10, Loss: 42.025726318359375, Acc: 0.5523999929428101\n",
|
||
"Epoch 3/10, Loss: 34.06425094604492, Acc: 0.5947999954223633\n",
|
||
"Epoch 4/10, Loss: 30.135021209716797, Acc: 0.620199978351593\n",
|
||
"Epoch 5/10, Loss: 27.43822479248047, Acc: 0.6401000022888184\n",
|
||
"Epoch 6/10, Loss: 25.72039031982422, Acc: 0.6525999903678894\n",
|
||
"Epoch 7/10, Loss: 24.28335952758789, Acc: 0.6638999581336975\n",
|
||
"Epoch 8/10, Loss: 23.18214988708496, Acc: 0.671999990940094\n",
|
||
"Epoch 9/10, Loss: 22.18520164489746, Acc: 0.680899977684021\n",
|
||
"Epoch 10/10, Loss: 21.393451690673828, Acc: 0.6875999569892883\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 5e-1\n",
|
||
"num_epochs = 10\n",
|
||
"batch_size = 4096\n",
|
||
"num_classes = 10\n",
|
||
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
|
||
"\n",
|
||
"transform = transforms.Compose(\n",
|
||
" [\n",
|
||
" transforms.ToTensor(),\n",
|
||
" transforms.Normalize((0.5,), (1.0,)),\n",
|
||
" ]\n",
|
||
")\n",
|
||
"train_dataset = datasets.FashionMNIST(root=\"./dataset\", train=True, transform=transform, download=True)\n",
|
||
"test_dataset = datasets.FashionMNIST(root=\"./dataset\", train=False, transform=transform, download=True)\n",
|
||
"train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size,shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size,shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"\n",
|
||
"model = Model_3_1(num_classes).to(device)\n",
|
||
"criterion = My_CrossEntropyLoss()\n",
|
||
"optimizer = My_Optimizer(model.parameters(), lr=learning_rate)\n",
|
||
"\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" total_epoch_loss = 0\n",
|
||
" for images, targets in train_loader:\n",
|
||
" optimizer.zero_grad()\n",
|
||
"\n",
|
||
" images = images.to(device)\n",
|
||
" targets = targets.to(device).to(dtype=torch.long)\n",
|
||
"\n",
|
||
" one_hot_targets = my_one_hot(targets, num_classes=num_classes).to(device).to(dtype=torch.long)\n",
|
||
"\n",
|
||
" outputs = model(images)\n",
|
||
" loss = criterion(outputs, one_hot_targets)\n",
|
||
" total_epoch_loss += loss\n",
|
||
"\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
"\n",
|
||
" total_acc = 0\n",
|
||
" with torch.no_grad():\n",
|
||
" for image, targets in test_loader:\n",
|
||
" image = image.to(device)\n",
|
||
" targets = targets.to(device)\n",
|
||
" outputs = model(image)\n",
|
||
" total_acc += (outputs.argmax(1) == targets).sum()\n",
|
||
" print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {total_epoch_loss}, Acc: {total_acc / len(test_dataset)}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a49d0165-aeb7-48c0-9b67-956bb08cb356",
|
||
"metadata": {},
|
||
"source": [
|
||
"在这里我遇到了梯度爆炸的问题。\n",
|
||
"\n",
|
||
"原来我在数据预处理中使用`transforms.Normalize((0.5,), (0.5,))`进行归一化，但是这样导致了梯度爆炸。\n",
|
||
"\n",
|
||
"将第二个参数方差改为1.0后，成功解决了梯度爆炸的问题。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3ef5240f-8a11-4678-bfce-f1cbc7e71b77",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 问题2\n",
|
||
"\n",
|
||
"**利用torch.nn实现softmax回归在Fashion-MNIST数据集上进行训练和测试，并从loss，训练集以及测试集上的准确率等多个角度对结果进行分析**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5c4a88c6-637e-4af5-bed5-f644685dcabc",
|
||
"metadata": {},
|
||
"source": [
|
||
"使用torch.nn实现模型。\n",
|
||
"\n",
|
||
"将之前的Model_3_1中的手动实现函数改为torch.nn内置函数即可，再加上继承nn.Module以使用torch.nn内置模型模板特性。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "0163b9f7-1019-429c-8c29-06436d0a4c98",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Model_3_2(nn.Module):\n",
|
||
" def __init__(self, num_classes):\n",
|
||
" super(Model_3_2, self).__init__()\n",
|
||
" self.flatten = nn.Flatten()\n",
|
||
" self.linear = nn.Linear(28 * 28, num_classes)\n",
|
||
"\n",
|
||
" def forward(self, x: torch.Tensor):\n",
|
||
" x = self.flatten(x)\n",
|
||
" x = self.linear(x)\n",
|
||
" return x"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e765ad7-c1c6-4166-bd7f-361666bd4016",
|
||
"metadata": {},
|
||
"source": [
|
||
"训练与测试过程与之前手动实现的几乎一致。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "6d241c05-b153-4f56-a845-0f2362f6459b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/10, Loss: 19.15451431274414, Acc: 0.7202000021934509\n",
|
||
"Epoch 2/10, Loss: 12.260371208190918, Acc: 0.7486000061035156\n",
|
||
"Epoch 3/10, Loss: 10.835549354553223, Acc: 0.7615999579429626\n",
|
||
"Epoch 4/10, Loss: 10.09542179107666, Acc: 0.7701999545097351\n",
|
||
"Epoch 5/10, Loss: 9.626176834106445, Acc: 0.777899980545044\n",
|
||
"Epoch 6/10, Loss: 9.264442443847656, Acc: 0.7854999899864197\n",
|
||
"Epoch 7/10, Loss: 9.017412185668945, Acc: 0.7879999876022339\n",
|
||
"Epoch 8/10, Loss: 8.786051750183105, Acc: 0.7915999889373779\n",
|
||
"Epoch 9/10, Loss: 8.613431930541992, Acc: 0.79749995470047\n",
|
||
"Epoch 10/10, Loss: 8.462657928466797, Acc: 0.7996999621391296\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 5e-2\n",
|
||
"num_epochs = 10\n",
|
||
"batch_size = 4096\n",
|
||
"num_classes = 10\n",
|
||
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
|
||
"\n",
|
||
"transform = transforms.Compose(\n",
|
||
" [\n",
|
||
" transforms.ToTensor(),\n",
|
||
" transforms.Normalize((0.5,), (0.5,)),\n",
|
||
" ]\n",
|
||
")\n",
|
||
"train_dataset = datasets.FashionMNIST(root=\"./dataset\", train=True, transform=transform, download=True)\n",
|
||
"test_dataset = datasets.FashionMNIST(root=\"./dataset\", train=False, transform=transform, download=True)\n",
|
||
"train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"\n",
|
||
"model = Model_3_2(num_classes).to(device)\n",
|
||
"criterion = nn.CrossEntropyLoss()\n",
|
||
"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
|
||
"\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" total_epoch_loss = 0\n",
|
||
" model.train()\n",
|
||
" for images, targets in train_loader:\n",
|
||
" optimizer.zero_grad()\n",
|
||
"\n",
|
||
" images = images.to(device)\n",
|
||
" targets = targets.to(device)\n",
|
||
"\n",
|
||
" one_hot_targets = nn.functional.one_hot(targets, num_classes=num_classes).to(device).to(dtype=torch.float32)\n",
|
||
"\n",
|
||
" outputs = model(images)\n",
|
||
" loss = criterion(outputs, one_hot_targets)\n",
|
||
" total_epoch_loss += loss\n",
|
||
"\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
"\n",
|
||
" model.eval()\n",
|
||
" total_acc = 0\n",
|
||
" with torch.no_grad():\n",
|
||
" for image, targets in test_loader:\n",
|
||
" image = image.to(device)\n",
|
||
" targets = targets.to(device)\n",
|
||
" outputs = model(image)\n",
|
||
" total_acc += (outputs.argmax(1) == targets).sum()\n",
|
||
" print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {total_epoch_loss}, Acc: {total_acc / len(test_dataset)}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "59555b67-1650-4e1a-a98e-7906878bf3d0",
|
||
"metadata": {},
|
||
"source": [
|
||
"与手动实现的softmax回归相比较，nn.CrossEntropyLoss比手动实现的My_CrossEntropyLoss更加稳定，对输入数据的兼容性更强，没有出现梯度爆炸的情况。\n",
|
||
"\n",
|
||
"总体表现上，torch.nn的内置功能相对手动实现的功能，正确率提升更快，最终正确率更高。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f40431f2-e77b-4ead-81a3-ff6451a8e452",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 实验心得体会\n",
|
||
"\n",
|
||
"通过完成本次Pytorch基本操作实验，让我对Pytorch框架有了更加深入的理解。我接触深度学习主要是在大语言模型领域，比较熟悉微调大模型，但是涉及到底层的深度学习知识，我还有很多短板和不足。这次实验对我这方面的锻炼让我收获良多。\n",
|
||
"\n",
|
||
"首先是数据集的设置。如果数据没有合理进行归一化，很容易出现梯度爆炸。这是在我以前直接使用图片数据集的经历中没有遇到过的问题。\n",
|
||
"\n",
|
||
"在实现logistic回归模型时，通过手动实现各个组件如优化器、线性层等，让我对这些模块的工作原理有了更清晰的认识。尤其是在实现广播机制时，需要充分理解张量操作的维度变换规律。而使用Pytorch内置模块进行实现时，通过继承nn.Module可以自动获得许多功能，使代码更加简洁。\n",
|
||
"\n",
|
||
"在实现softmax回归时，则遇到了更大的困难。手动实现的模型很容易出现梯度爆炸的问题，而使用Pytorch内置的损失函数和优化器则可以稳定训练。这让我意识到了选择合适的优化方法的重要性。另外，Pytorch强大的自动微分机制也是构建深度神经网络的重要基础。\n",
|
||
"\n",
|
||
"通过这个实验，让我对Pytorch框架有了更加直观的感受，也让我看到了仅靠基础模块搭建复杂模型的难点所在。这些经验对我后续使用Pytorch构建数据集模型会很有帮助。"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|