1144 lines
327 KiB
Plaintext
1144 lines
327 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6b54d34a-5e54-49d5-a13b-bf704cb900a4",
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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;\">网络优化实验</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11月20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": "0fad7a29-2cf7-423f-b9eb-b3a8096d8148",
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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 22.04.3 LTS (GNU/Linux 6.2.0-36-generic x86_64)\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: 12.3\n",
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"- conda: miniconda 23.9.0\n",
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"- python:3.10.13\n",
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"- torch:2.1.1"
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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": "415d3060-464f-40c5-8af5-598e3ad6018d",
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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.nn.functional import *\n",
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"from torch.utils.data import DataLoader, random_split\n",
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"from torch import nn\n",
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"from torchvision import datasets, transforms\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "026bc7da-f207-4713-acf6-10dc472237ad",
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"metadata": {},
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"source": [
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"引用必要的库。"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "8cd38046-6b87-4b05-82b8-ad5576baf100",
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"metadata": {},
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"source": [
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"# 任务一\n",
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"\n",
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"**在多分类任务实验中分别手动实现和用torch.nn实现dropout**\n",
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"\n",
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"- 探究不同丢弃率对实验结果的影响(可用loss曲线进行展示)\n"
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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": "b139b54d-89ce-4b05-93fc-de5fad7e2906",
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"metadata": {},
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"outputs": [],
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"source": [
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"class MNIST_CLS_Model(nn.Module):\n",
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" def __init__(self, num_classes, dropout_rate=0.5):\n",
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" super().__init__()\n",
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" self.flatten = nn.Flatten()\n",
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" self.fc1 = nn.Linear(in_features=28 * 28, out_features=2048)\n",
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" self.fc2 = nn.Linear(in_features=2048, out_features=4096)\n",
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" self.fc3 = nn.Linear(in_features=4096, out_features=1024)\n",
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" self.fc4 = nn.Linear(in_features=1024, out_features=256)\n",
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" self.fc5 = nn.Linear(in_features=256, out_features=num_classes)\n",
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" self.dropout = nn.Dropout(p=dropout_rate)\n",
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"\n",
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" def forward(self, x: torch.Tensor):\n",
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" x = self.flatten(x)\n",
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" x = torch.relu(self.fc1(x))\n",
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" x = torch.relu(self.fc2(x))\n",
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" x = torch.relu(self.fc3(x))\n",
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" x = torch.relu(self.fc4(x))\n",
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" x = self.dropout(x)\n",
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" x = self.fc5(x)\n",
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" return x\n",
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"\n",
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"\n",
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"def train_MNIST_CLS(model, optimizer, num_epochs):\n",
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" batch_size = 8192\n",
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" num_classes = 10\n",
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" device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
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"\n",
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" transform = transforms.Compose(\n",
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" [\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize((0.5,), (0.5,)),\n",
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" ]\n",
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" )\n",
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" train_mnist_dataset = datasets.MNIST(root=\"./dataset\", train=True, transform=transform, download=True)\n",
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" test_mnist_dataset = datasets.MNIST(root=\"./dataset\", train=False, transform=transform, download=True)\n",
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" train_loader = DataLoader(dataset=train_mnist_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
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" test_loader = DataLoader(dataset=test_mnist_dataset, batch_size=batch_size, num_workers=14, pin_memory=True)\n",
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"\n",
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" model = model.to(device)\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" \n",
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" train_loss = list()\n",
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" test_acc = list()\n",
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" for epoch in range(num_epochs):\n",
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" model.train()\n",
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" total_epoch_loss = 0\n",
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" for images, targets in train_loader:\n",
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" optimizer.zero_grad()\n",
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"\n",
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" images = images.to(device)\n",
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" targets = targets.to(device)\n",
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" one_hot_targets = one_hot(targets, num_classes=num_classes).to(dtype=torch.float)\n",
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"\n",
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" outputs = model(images)\n",
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" loss = criterion(outputs, one_hot_targets)\n",
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" total_epoch_loss += loss.item()\n",
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"\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" model.eval()\n",
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" with torch.no_grad():\n",
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" total_epoch_acc = 0\n",
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" for image, targets in test_loader:\n",
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" image = image.to(device)\n",
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" targets = targets.to(device)\n",
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" \n",
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" outputs = model(image)\n",
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" pred = softmax(outputs, dim=1)\n",
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" total_epoch_acc += (pred.argmax(1) == targets).sum().item()\n",
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" \n",
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" avg_epoch_acc = total_epoch_acc / len(test_mnist_dataset)\n",
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" if epoch % 40 == 0:\n",
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" print(\n",
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" f\"Epoch [{epoch + 1}/{num_epochs}],\",\n",
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" f\"Train Loss: {total_epoch_loss:.10f},\",\n",
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" f\"Test Acc: {avg_epoch_acc * 100:.3f}%\",\n",
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" )\n",
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" train_loss.append(total_epoch_loss)\n",
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" test_acc.append(avg_epoch_acc * 100)\n",
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" return train_loss, test_acc"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ff4b323-7f04-40b9-a164-07858ae02a12",
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"metadata": {},
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"source": [
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"首先编写训练模型的框架函数代码。"
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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": 3,
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"id": "d208439d-b749-4619-b991-4310bcbbed5b",
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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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"输入:\n",
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"tensor([[ 1., 2., 3., 4., 5.],\n",
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" [ 6., 7., 8., 9., 10.]])\n",
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"My_Dropout输出:\n",
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"tensor([[ 2., 0., 6., 8., 0.],\n",
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" [ 0., 14., 0., 18., 0.]])\n",
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"nn.Dropout输出:\n",
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"tensor([[ 0., 4., 6., 0., 10.],\n",
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" [12., 14., 0., 0., 0.]])\n"
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]
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}
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],
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"source": [
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"class My_Dropout(nn.Module):\n",
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" def __init__(self, p, **kwargs):\n",
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" super().__init__()\n",
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" self.p = p\n",
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" self.mask = None\n",
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"\n",
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" def forward(self, x:torch.Tensor):\n",
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" if self.training:\n",
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" self.mask = (torch.rand(x.shape) > self.p).to(dtype=torch.float32, device=x.device)\n",
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" return x * self.mask / (1 - self.p)\n",
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" else:\n",
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" return x\n",
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"\n",
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"\n",
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"# 测试\n",
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"my_dropout = My_Dropout(p=0.5)\n",
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"nn_dropout = nn.Dropout(p=0.5)\n",
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"x = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0],\n",
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" [6.0, 7.0, 8.0, 9.0, 10.0]])\n",
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"print(f\"输入:\\n{x}\")\n",
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"output_my_dropout = my_dropout(x)\n",
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"output_nn_dropout = nn_dropout(x)\n",
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"print(f\"My_Dropout输出:\\n{output_my_dropout}\")\n",
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"print(f\"nn.Dropout输出:\\n{output_nn_dropout}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "00a034cd-ec36-42d3-b678-399d4fd5d4ab",
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"metadata": {},
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"source": [
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"手动实现Dropout。"
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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": 4,
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"id": "ba0b10ce-f2e8-4a3a-b1fe-7ab5fe2a2b71",
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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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"dropout_rate=0.0\n",
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"Epoch [1/161], Train Loss: 18.3612053394, Test Acc: 10.540%\n",
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"Epoch [41/161], Train Loss: 2.2243138552, Test Acc: 92.090%\n",
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"Epoch [81/161], Train Loss: 1.4333713949, Test Acc: 94.850%\n",
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"Epoch [121/161], Train Loss: 1.0422569066, Test Acc: 95.950%\n",
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"Epoch [161/161], Train Loss: 0.7250142395, Test Acc: 96.640%\n",
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"dropout_rate=0.25\n",
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"Epoch [1/161], Train Loss: 18.3689846992, Test Acc: 25.860%\n",
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"Epoch [41/161], Train Loss: 2.3589982688, Test Acc: 91.930%\n",
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"Epoch [81/161], Train Loss: 1.4811040908, Test Acc: 94.560%\n",
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"Epoch [121/161], Train Loss: 1.0563187748, Test Acc: 96.140%\n",
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"Epoch [161/161], Train Loss: 1.3523552120, Test Acc: 95.800%\n",
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"dropout_rate=0.5\n",
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"Epoch [1/161], Train Loss: 18.3639202118, Test Acc: 29.560%\n",
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"Epoch [41/161], Train Loss: 2.6411375999, Test Acc: 91.350%\n",
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"Epoch [81/161], Train Loss: 1.5525230914, Test Acc: 94.640%\n",
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"Epoch [121/161], Train Loss: 1.1174016893, Test Acc: 96.200%\n",
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"Epoch [161/161], Train Loss: 0.8520765156, Test Acc: 96.710%\n",
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"dropout_rate=0.75\n",
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"Epoch [1/161], Train Loss: 18.3790585995, Test Acc: 15.140%\n",
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"Epoch [41/161], Train Loss: 3.2145081460, Test Acc: 90.870%\n",
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"Epoch [81/161], Train Loss: 1.9012856632, Test Acc: 94.130%\n",
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"Epoch [121/161], Train Loss: 1.3508325368, Test Acc: 95.860%\n",
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"Epoch [161/161], Train Loss: 1.0570104122, Test Acc: 96.360%\n"
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]
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},
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{
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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 700x350 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 8e-2\n",
|
||
"num_epochs = 161\n",
|
||
"plt.figure(figsize=(7, 3.5))\n",
|
||
"color = [\"blue\", \"green\", \"orange\", \"purple\"]\n",
|
||
"for i in np.arange(4):\n",
|
||
" dropout_rate = i / 4\n",
|
||
" model = MNIST_CLS_Model(num_classes=10, dropout_rate=dropout_rate)\n",
|
||
" optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
|
||
" print(f\"dropout_rate={dropout_rate}\")\n",
|
||
" train_loss, test_acc = train_MNIST_CLS(model, optimizer, num_epochs=num_epochs)\n",
|
||
" plt.subplot(1, 2, 1)\n",
|
||
" plt.plot(range(1, num_epochs + 1), train_loss, label=f'dropout_rate={dropout_rate}', color=color[i])\n",
|
||
" plt.subplot(1, 2, 2)\n",
|
||
" plt.plot(range(1, num_epochs + 1), test_acc, label=f'dropout_rate={dropout_rate}', color=color[i])\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Train Loss')\n",
|
||
"plt.legend()\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Test Accuracy')\n",
|
||
"plt.legend()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a9f22410-1c48-46e2-a873-0b91ef824032",
|
||
"metadata": {},
|
||
"source": [
|
||
"可以看出,丢弃率越高,loss就会越高,因为丢弃部分神经元会导致网络在训练时失去了一些有用的信息,因为每个神经元都对模型的表达能力有贡献。如果丢弃率很高,网络可能无法充分利用所有的特征,导致信息的损失。\n",
|
||
"\n",
|
||
"但是,丢弃率越高,测试集的正确率提升相对更稳定。因为高丢弃率使得模型更多地依赖于共享的特征而不是过分依赖于个别神经元,有助于防止过拟合。在丢弃率为$0.5$以下时,容易发生过拟合。但是当丢弃率为$0.5$以上时,就很少发生过拟合的现象。"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"id": "cedbacde-15ba-4ffb-b734-b0f31b231775",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 任务二\n",
|
||
"\n",
|
||
"**在多分类任务实验中分别手动实现和用torch.nn实现$L_2$正则化**\n",
|
||
"\n",
|
||
"- 探究惩罚项的权重对实验结果的影响(可用loss曲线进行展示)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fc1d2c74-e75c-4de6-98c8-477634e44417",
|
||
"metadata": {},
|
||
"source": [
|
||
"对于$L_2$正则化,pytorch的的实现是将$L_2$正则化的系数作为优化器的`weight_decay`参数传入,在`step()`的过程中计算完成。具体原理如下:\n",
|
||
"\n",
|
||
"$L_2$正则化的公式是\n",
|
||
"\n",
|
||
"$$\n",
|
||
"L = L_0 + \\frac{\\lambda }{2n} \\sum_{w}w^2\n",
|
||
"$$\n",
|
||
"\n",
|
||
"其中$L$是进行$L_2$正则化后的损失,$L_0$是损失函数计算出来的原损失,$\\lambda$是$L_2$正则化系数(即optimizer的`weight_decay`参数),$n$是样本大小。\n",
|
||
"\n",
|
||
"反向传播:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{align}\n",
|
||
"\\frac{\\partial L}{\\partial w} & = \\frac{\\partial L_0}{\\partial w}+\\frac{\\lambda }{n} w \\\\\n",
|
||
"\\frac{\\partial L}{\\partial b} & = \\frac{\\partial L_0}{\\partial b}\n",
|
||
"\\end{align}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"所以,参数更新为:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{align}\n",
|
||
"w: & = w-\\frac{\\eta \\lambda }{n}w- \\frac{\\eta}{n}\\sum\\frac{\\partial L_0}{\\partial w} \\\\\n",
|
||
"b: & = b-\\frac{\\eta}{n}\\sum\\frac{\\partial L_0}{\\partial b}\n",
|
||
"\\end{align}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"其中$\\eta$是学习率。\n",
|
||
"\n",
|
||
"所以,手动在优化器中实现$L_2$正则化如下:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "5ddffd42-eeab-43f3-b3b8-1e3d343cf4ae",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"params1的梯度为:\n",
|
||
" tensor([[2., 2.]])\n",
|
||
"params2的梯度为:\n",
|
||
" tensor([[2., 2.]])\n",
|
||
"经过L_2正则化后的My_SGD反向传播结果:\n",
|
||
" tensor([[-0.0500, 0.9000]])\n",
|
||
"经过L_2正则化后的torch.optim.SGD反向传播结果:\n",
|
||
" tensor([[-0.0500, 0.9000]])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_SGD:\n",
|
||
" def __init__(self, params: list[torch.Tensor], lr: float, weight_decay=0.0):\n",
|
||
" self.params = params\n",
|
||
" self.lr = lr\n",
|
||
" self.weight_decay = weight_decay\n",
|
||
"\n",
|
||
" def step(self):\n",
|
||
" with torch.no_grad():\n",
|
||
" for param in self.params:\n",
|
||
" if param.grad is not None:\n",
|
||
" if len(param.data.shape) > 1:\n",
|
||
" param.data = param.data - self.lr * (param.grad + self.weight_decay * param.data)\n",
|
||
" else:\n",
|
||
" param.data = param.data - self.lr * param.grad\n",
|
||
"\n",
|
||
" def zero_grad(self):\n",
|
||
" for param in self.params:\n",
|
||
" if param.grad is not None:\n",
|
||
" param.grad.data = torch.zeros_like(param.grad.data)\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试\n",
|
||
"params1 = torch.tensor([[1., 2, ]], requires_grad=True)\n",
|
||
"params2 = torch.tensor([[1., 2, ]], requires_grad=True)\n",
|
||
"\n",
|
||
"my_sgd = My_SGD(params=[params1], lr=0.5, weight_decay=0.1)\n",
|
||
"optim_sgd = torch.optim.SGD(params=[params2], lr=0.5, weight_decay=0.1)\n",
|
||
"my_sgd.zero_grad()\n",
|
||
"optim_sgd.zero_grad()\n",
|
||
"\n",
|
||
"loss1 = 2 * params1.sum()\n",
|
||
"loss2 = 2 * params2.sum()\n",
|
||
" # 偏导为2\n",
|
||
"loss1.backward()\n",
|
||
"loss2.backward()\n",
|
||
"print(\"params1的梯度为:\\n\", params1.grad.data)\n",
|
||
"print(\"params2的梯度为:\\n\", params2.grad.data)\n",
|
||
"\n",
|
||
"my_sgd.step()\n",
|
||
"optim_sgd.step()\n",
|
||
"# 结果为:w - lr * grad - lr * weight_decay_rate * w\n",
|
||
"# w[0] = 1 - 0.5 * 2 - 0.5 * 0.1 * 1 = -0.0500\n",
|
||
"# w[1] = 2 - 0.5 * 2 - 0.5 * 0.1 * 2 = 0.9000\n",
|
||
"print(\"经过L_2正则化后的My_SGD反向传播结果:\\n\", params1.data)\n",
|
||
"print(\"经过L_2正则化后的torch.optim.SGD反向传播结果:\\n\", params2.data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "fe1fd309-0f2f-4bb8-98b0-152e2c09c547",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"weight_decay_rate=0.0\n",
|
||
"Epoch [1/161], Train Loss: 18.3586504459, Test Acc: 11.350%\n",
|
||
"Epoch [41/161], Train Loss: 2.2273855805, Test Acc: 91.870%\n",
|
||
"Epoch [81/161], Train Loss: 1.4378820062, Test Acc: 94.420%\n",
|
||
"Epoch [121/161], Train Loss: 0.9473107532, Test Acc: 96.220%\n",
|
||
"Epoch [161/161], Train Loss: 0.9763946906, Test Acc: 95.850%\n",
|
||
"weight_decay_rate=0.0025\n",
|
||
"Epoch [1/161], Train Loss: 18.3720602989, Test Acc: 11.790%\n",
|
||
"Epoch [41/161], Train Loss: 2.4159298837, Test Acc: 90.460%\n",
|
||
"Epoch [81/161], Train Loss: 1.6433586627, Test Acc: 94.170%\n",
|
||
"Epoch [121/161], Train Loss: 1.5564490259, Test Acc: 94.790%\n",
|
||
"Epoch [161/161], Train Loss: 1.7672693580, Test Acc: 62.780%\n",
|
||
"weight_decay_rate=0.005\n",
|
||
"Epoch [1/161], Train Loss: 18.3529415131, Test Acc: 20.810%\n",
|
||
"Epoch [41/161], Train Loss: 3.0496689975, Test Acc: 86.750%\n",
|
||
"Epoch [81/161], Train Loss: 1.7128281891, Test Acc: 93.060%\n",
|
||
"Epoch [121/161], Train Loss: 1.4363404214, Test Acc: 94.830%\n",
|
||
"Epoch [161/161], Train Loss: 1.1253674477, Test Acc: 95.720%\n",
|
||
"weight_decay_rate=0.0075\n",
|
||
"Epoch [1/161], Train Loss: 18.3801815510, Test Acc: 32.990%\n",
|
||
"Epoch [41/161], Train Loss: 3.0067147911, Test Acc: 90.750%\n",
|
||
"Epoch [81/161], Train Loss: 1.9032090753, Test Acc: 93.250%\n",
|
||
"Epoch [121/161], Train Loss: 1.8552065641, Test Acc: 90.470%\n",
|
||
"Epoch [161/161], Train Loss: 3.4438242316, Test Acc: 94.750%\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 700x350 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 8e-2\n",
|
||
"num_epochs = 161\n",
|
||
"plt.figure(figsize=(7, 3.5))\n",
|
||
"color = [\"blue\", \"green\", \"orange\", \"purple\"]\n",
|
||
"for i in np.arange(4):\n",
|
||
" weight_decay_rate = i / 4 * 0.01\n",
|
||
" model = MNIST_CLS_Model(num_classes=10, dropout_rate=0)\n",
|
||
" optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, weight_decay=weight_decay_rate)\n",
|
||
" print(f\"weight_decay_rate={weight_decay_rate}\")\n",
|
||
" train_loss, test_acc = train_MNIST_CLS(model, optimizer, num_epochs=num_epochs)\n",
|
||
" plt.subplot(1, 2, 1)\n",
|
||
" plt.plot(range(1, num_epochs + 1), train_loss, label=f'weight_decay_rate={weight_decay_rate}', color=color[i])\n",
|
||
" plt.subplot(1, 2, 2)\n",
|
||
" plt.plot(range(1, num_epochs + 1), test_acc, label=f'weight_decay_rate={weight_decay_rate}', color=color[i])\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Train Loss')\n",
|
||
"plt.legend()\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Test Accuracy')\n",
|
||
"plt.legend()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ba5f7ae8-e42b-4a99-90f0-9bc2d9fa0f56",
|
||
"metadata": {},
|
||
"source": [
|
||
"可以看出$L_2$正则化(即weight decay)的系数越大,loss越大。因为weight decay对权重进行了惩罚,防止其过拟合,导致其介于欠拟合和过拟合之间。在本实验中没有出现过拟合的情况,所以weight decay发挥效果不稳定,大多数情况反而导致正确率有所下降。"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"id": "38403830-5279-42fc-bfba-826b28832011",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 任务三\n",
|
||
"\n",
|
||
"**在多分类任务实验中实现momentum、rmsprop、adam优化器**\n",
|
||
"\n",
|
||
"- 在手动实现多分类的任务中手动实现三种优化算法,并补全Adam中计算部分的内容\n",
|
||
"- 在torch.nn实现多分类的任务中使用torch.nn实现各种优化器,并对比其效果\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d484af56-5822-480f-85df-c45027402c15",
|
||
"metadata": {},
|
||
"source": [
|
||
"首先实现momentum。\n",
|
||
"\n",
|
||
"参数更新为:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{align}\n",
|
||
"w: & = w- \\frac{\\eta}{n}\\sum\\frac{\\partial L_0}{\\partial w} + \\beta_1{\\left(-\\frac{\\eta}{n}\\sum\\frac{\\partial L_0}{\\partial w}\\right)}_{step-1} \\\\\n",
|
||
"b: & = b-\\frac{\\eta}{n}\\sum\\frac{\\partial L_0}{\\partial b} + \\beta_1{\\left(-\\frac{\\eta}{n}\\sum\\frac{\\partial L_0}{\\partial b}\\right)}_{step-1}\n",
|
||
"\\end{align}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"其中$\\beta_1$为momentum的系数。\n",
|
||
"\n",
|
||
"完善任务二中的My_SGD,加入momentum。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "f19333ff-600d-4eb2-a66f-d6011fa80f53",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"My_SGD第1次反向传播结果:\n",
|
||
" tensor([[0., 1.]])\n",
|
||
"torch.optim.SGD第1次反向传播结果:\n",
|
||
" tensor([[0., 1.]])\n",
|
||
"My_SGD第2次反向传播结果:\n",
|
||
" tensor([[0.5000, 1.5000]])\n",
|
||
"torch.optim.SGD第2次反向传播结果:\n",
|
||
" tensor([[0.5000, 1.5000]])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_SGD:\n",
|
||
" def __init__(self, params: list[torch.Tensor], lr: float, weight_decay=0.0, momentum=0.0):\n",
|
||
" self.params = params\n",
|
||
" self.lr = lr\n",
|
||
" self.weight_decay = weight_decay\n",
|
||
" self.momentum = momentum\n",
|
||
" self.velocities = [torch.zeros_like(param.data) for param in params]\n",
|
||
"\n",
|
||
" def step(self):\n",
|
||
" with torch.no_grad():\n",
|
||
" for index, param in enumerate(self.params):\n",
|
||
" if param.grad is not None:\n",
|
||
" if self.weight_decay > 0:\n",
|
||
" if len(param.data.shape) > 1:\n",
|
||
" param.grad.data = param.grad.data + self.weight_decay * param.data\n",
|
||
" self.velocities[index] = self.momentum * self.velocities[index] - self.lr * param.grad\n",
|
||
" param.data = param.data + self.velocities[index]\n",
|
||
"\n",
|
||
" def zero_grad(self):\n",
|
||
" for param in self.params:\n",
|
||
" if param.grad is not None:\n",
|
||
" param.grad.data = torch.zeros_like(param.grad.data)\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试\n",
|
||
"params1 = torch.tensor([[1., 2, ]], requires_grad=True)\n",
|
||
"params2 = torch.tensor([[1., 2, ]], requires_grad=True)\n",
|
||
"\n",
|
||
"my_sgd = My_SGD(params=[params1], lr=0.5, momentum=1)\n",
|
||
"optim_sgd = torch.optim.SGD(params=[params2], lr=0.5, momentum=1)\n",
|
||
"my_sgd.zero_grad()\n",
|
||
"optim_sgd.zero_grad()\n",
|
||
"\n",
|
||
"loss1 = 2 * params1.sum()\n",
|
||
"loss2 = 2 * params2.sum()\n",
|
||
"# 偏导为2\n",
|
||
"loss1.backward()\n",
|
||
"loss2.backward()\n",
|
||
"my_sgd.step()\n",
|
||
"optim_sgd.step()\n",
|
||
"# 结果为:w - lr * grad + momentum * velocity\n",
|
||
"# w[0] = 1 - 0.5 * 2 + 1 * 0 = 0\n",
|
||
"# w[1] = 2 - 0.5 * 2 + 1 * 0 = 1\n",
|
||
"print(\"My_SGD第1次反向传播结果:\\n\", params1.data)\n",
|
||
"print(\"torch.optim.SGD第1次反向传播结果:\\n\", params2.data)\n",
|
||
"\n",
|
||
"my_sgd.zero_grad()\n",
|
||
"optim_sgd.zero_grad()\n",
|
||
"loss1 = -3 * params1.sum()\n",
|
||
"loss2 = -3 * params2.sum()\n",
|
||
"# 偏导为-3\n",
|
||
"loss1.backward()\n",
|
||
"loss2.backward()\n",
|
||
"my_sgd.step()\n",
|
||
"optim_sgd.step()\n",
|
||
"# 结果为:w - lr * grad + momentum * velocity\n",
|
||
"# w[0] = 0 - 0.5 * -3 + 1 * (-0.5 * 2) = 0.5\n",
|
||
"# w[1] = 1 - 0.5 * -3 + 1 * (-0.5 * 2) = 1.5\n",
|
||
"print(\"My_SGD第2次反向传播结果:\\n\", params1.data)\n",
|
||
"print(\"torch.optim.SGD第2次反向传播结果:\\n\", params2.data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "207bb4bd-ba9d-4a7e-977a-48146000d248",
|
||
"metadata": {},
|
||
"source": [
|
||
"接下来实现RMSprop。\n",
|
||
"\n",
|
||
"参数$w$更新为:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{align}\n",
|
||
"s: & = \\alpha s+\\left(1-\\alpha\\right) {\\left(\\frac{1}{n}\\sum\\frac{\\partial L_0}{\\partial w}\\right)}^2 \\\\\n",
|
||
"w: & = w- \\eta\\frac{s}{\\sqrt{s+\\beta_1} } \\\\\n",
|
||
"\\end{align}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"其中$\\alpha_1$为RMSprop的超参数。参数$b$更新同理。\n",
|
||
"\n",
|
||
"编写RMSprop。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "26136526-e355-4ac7-b7b8-bbabe126dedb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"My_RMSprop第1次反向传播结果:\n",
|
||
" tensor([[-0.4142, 0.5858]])\n",
|
||
"torch.optim.RMSprop第1次反向传播结果:\n",
|
||
" tensor([[-0.4142, 0.5858]])\n",
|
||
"My_RMSprop第2次反向传播结果:\n",
|
||
" tensor([[0.8650, 1.8650]])\n",
|
||
"torch.optim.RMSprop第2次反向传播结果:\n",
|
||
" tensor([[0.8650, 1.8650]])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_RMSprop:\n",
|
||
" def __init__(self, params: list[torch.Tensor], lr=1e-2, alpha=0.99, eps=1e-8):\n",
|
||
" self.params = params\n",
|
||
" self.lr = lr\n",
|
||
" self.alpha = alpha\n",
|
||
" self.eps = eps\n",
|
||
" self.square_avg = [torch.zeros_like(param.data) for param in params]\n",
|
||
" \n",
|
||
"\n",
|
||
" def step(self):\n",
|
||
" with torch.no_grad():\n",
|
||
" for index, param in enumerate(self.params):\n",
|
||
" if param.grad is not None:\n",
|
||
" self.square_avg[index] = self.alpha * self.square_avg[index] + (1 - self.alpha) * param.grad ** 2\n",
|
||
" param.data = param.data - self.lr * param.grad / torch.sqrt(self.square_avg[index] + self.eps)\n",
|
||
"\n",
|
||
" def zero_grad(self):\n",
|
||
" for param in self.params:\n",
|
||
" if param.grad is not None:\n",
|
||
" param.grad.data = torch.zeros_like(param.grad.data)\n",
|
||
"\n",
|
||
"\n",
|
||
"# 测试\n",
|
||
"params1 = torch.tensor([[1., 2, ]], requires_grad=True)\n",
|
||
"params2 = torch.tensor([[1., 2, ]], requires_grad=True)\n",
|
||
"\n",
|
||
"my_sgd = My_RMSprop(params=[params1], lr=1, alpha=0.5, eps=1e-8)\n",
|
||
"optim_sgd = torch.optim.RMSprop(params=[params2], lr=1, alpha=0.5, eps=1e-8)\n",
|
||
"my_sgd.zero_grad()\n",
|
||
"optim_sgd.zero_grad()\n",
|
||
"\n",
|
||
"loss1 = 2 * params1.sum()\n",
|
||
"loss2 = 2 * params2.sum()\n",
|
||
"# 偏导为2\n",
|
||
"loss1.backward()\n",
|
||
"loss2.backward()\n",
|
||
"my_sgd.step()\n",
|
||
"optim_sgd.step()\n",
|
||
"# s = alpha * s + (1-alpha) * grad^2 = 0.5 * 0 + (1-0.5) * 2^2 = 2\n",
|
||
"# w = w - lr * grad * (s + eps)^0.5\n",
|
||
"# w[0] = 1 - 1 * 2 / (2 + 1e-8)^0.5 ~= -0.41\n",
|
||
"# w[1] = 2 - 1 * 2 / (2 + 1e-8)^0.5 ~= -0.59\n",
|
||
"print(\"My_RMSprop第1次反向传播结果:\\n\", params1.data)\n",
|
||
"print(\"torch.optim.RMSprop第1次反向传播结果:\\n\", params2.data)\n",
|
||
"\n",
|
||
"my_sgd.zero_grad()\n",
|
||
"optim_sgd.zero_grad()\n",
|
||
"loss1 = -3 * params1.sum()\n",
|
||
"loss2 = -3 * params2.sum()\n",
|
||
"# 偏导为-3\n",
|
||
"loss1.backward()\n",
|
||
"loss2.backward()\n",
|
||
"my_sgd.step()\n",
|
||
"optim_sgd.step()\n",
|
||
"# s = alpha * s + (1-alpha) * grad^2 = 0.5 * 2 + (1-0.5) * (-3)^2 = 5.5\n",
|
||
"# w - lr * grad * (s + eps)^0.5\n",
|
||
"# w[0] = -0.41 - 1 * -3 / (5.5 + 1e-8)^0.5 ~= 0.87\n",
|
||
"# w[1] = 0.59 - 1 * -3 / (5.5 + 1e-8)^0.5 ~= 1.86\n",
|
||
"print(\"My_RMSprop第2次反向传播结果:\\n\", params1.data)\n",
|
||
"print(\"torch.optim.RMSprop第2次反向传播结果:\\n\", params2.data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "d1d540e9-ef66-4bed-81e3-0c9e24a05aa7",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"My_Adam第1次反向传播结果:\n",
|
||
" tensor([[0., 1.]])\n",
|
||
"torch.optim.Adam第1次反向传播结果:\n",
|
||
" tensor([[0., 1.]])\n",
|
||
"My_Adam第2次反向传播结果:\n",
|
||
" tensor([[0.4924, 1.4924]])\n",
|
||
"torch.optim.Adam第2次反向传播结果:\n",
|
||
" tensor([[0.4924, 1.4924]])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"class My_Adam:\n",
|
||
" def __init__(self, params: list[torch.Tensor], lr=1e-3, betas=(0.9, 0.999), eps=1e-8):\n",
|
||
" self.params = params\n",
|
||
" self.lr = lr\n",
|
||
" self.beta1 = betas[0]\n",
|
||
" self.beta2 = betas[1]\n",
|
||
" self.eps = eps\n",
|
||
" self.t = 0\n",
|
||
" self.momentums = [torch.zeros_like(param.data) for param in params]\n",
|
||
" self.velocities = [torch.zeros_like(param.data) for param in params]\n",
|
||
"\n",
|
||
" def step(self):\n",
|
||
" self.t += 1\n",
|
||
" with torch.no_grad():\n",
|
||
" for index, param in enumerate(self.params):\n",
|
||
" if param.grad is not None:\n",
|
||
" self.momentums[index] = (self.beta1 * self.momentums[index] + (1 - self.beta1) * param.grad)\n",
|
||
" self.velocities[index] = (self.beta2 * self.velocities[index] + (1 - self.beta2) * param.grad**2)\n",
|
||
" momentums_hat = self.momentums[index] / (1 - self.beta1**self.t)\n",
|
||
" velocities_hat = self.velocities[index] / (1 - self.beta2**self.t)\n",
|
||
" param.data = param.data - self.lr * momentums_hat / (torch.sqrt(velocities_hat) + self.eps)\n",
|
||
"\n",
|
||
" def zero_grad(self):\n",
|
||
" for param in self.params:\n",
|
||
" if param.grad is not None:\n",
|
||
" param.grad.data = torch.zeros_like(param.grad.data)\n",
|
||
"\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" params1 = torch.tensor([[1.0, 2.0,]], requires_grad=True)\n",
|
||
" params2 = torch.tensor([[1.0, 2.0,]], requires_grad=True)\n",
|
||
"\n",
|
||
" my_sgd = My_Adam(params=[params1], lr=1, betas=(0.5, 0.5), eps=1e-8)\n",
|
||
" optim_sgd = torch.optim.Adam(params=[params2], lr=1, betas=(0.5, 0.5), eps=1e-8)\n",
|
||
" my_sgd.zero_grad()\n",
|
||
" optim_sgd.zero_grad()\n",
|
||
"\n",
|
||
" loss1 = 2 * params1.sum()\n",
|
||
" loss2 = 2 * params2.sum()\n",
|
||
" # 偏导为2\n",
|
||
" loss1.backward()\n",
|
||
" loss2.backward()\n",
|
||
" my_sgd.step()\n",
|
||
" optim_sgd.step()\n",
|
||
" print(\"My_Adam第1次反向传播结果:\\n\", params1.data)\n",
|
||
" print(\"torch.optim.Adam第1次反向传播结果:\\n\", params2.data)\n",
|
||
"\n",
|
||
" my_sgd.zero_grad()\n",
|
||
" optim_sgd.zero_grad()\n",
|
||
" loss1 = -3 * params1.sum()\n",
|
||
" loss2 = -3 * params2.sum()\n",
|
||
" # 偏导为-3\n",
|
||
" loss1.backward()\n",
|
||
" loss2.backward()\n",
|
||
" my_sgd.step()\n",
|
||
" optim_sgd.step()\n",
|
||
" print(\"My_Adam第2次反向传播结果:\\n\", params1.data)\n",
|
||
" print(\"torch.optim.Adam第2次反向传播结果:\\n\", params2.data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "777f4239-974e-4128-bb3e-c0e51af31f72",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"optimizer: SGD\n",
|
||
"Epoch [1/161], Train Loss: 18.4296679497, Test Acc: 12.410%\n",
|
||
"Epoch [41/161], Train Loss: 16.6156928539, Test Acc: 52.890%\n",
|
||
"Epoch [81/161], Train Loss: 6.2243856192, Test Acc: 80.030%\n",
|
||
"Epoch [121/161], Train Loss: 3.7015175521, Test Acc: 86.980%\n",
|
||
"Epoch [161/161], Train Loss: 3.0445261896, Test Acc: 89.070%\n",
|
||
"optimizer: RMSprop\n",
|
||
"Epoch [1/161], Train Loss: 417.8901937008, Test Acc: 15.990%\n",
|
||
"Epoch [41/161], Train Loss: 1.2281078026, Test Acc: 88.820%\n",
|
||
"Epoch [81/161], Train Loss: 1.6108630896, Test Acc: 95.610%\n",
|
||
"Epoch [121/161], Train Loss: 0.4748786949, Test Acc: 97.650%\n",
|
||
"Epoch [161/161], Train Loss: 0.6687553413, Test Acc: 97.190%\n",
|
||
"optimizer: Adam\n",
|
||
"Epoch [1/161], Train Loss: 15.9746286869, Test Acc: 68.570%\n",
|
||
"Epoch [41/161], Train Loss: 0.0055657097, Test Acc: 98.470%\n",
|
||
"Epoch [81/161], Train Loss: 0.0004255227, Test Acc: 98.460%\n",
|
||
"Epoch [121/161], Train Loss: 0.0001335207, Test Acc: 98.460%\n",
|
||
"Epoch [161/161], Train Loss: 0.0000606167, Test Acc: 98.400%\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 700x350 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 1e-3\n",
|
||
"num_epochs = 161\n",
|
||
"plt.figure(figsize=(7, 3.5))\n",
|
||
"\n",
|
||
"model = MNIST_CLS_Model(num_classes=10, dropout_rate=0)\n",
|
||
"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)\n",
|
||
"print(f\"optimizer: SGD\")\n",
|
||
"train_loss, test_acc = train_MNIST_CLS(model, optimizer, num_epochs=num_epochs)\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.plot(range(1, num_epochs + 1), train_loss, label=\"SGD\", color=\"blue\")\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"plt.plot(range(1, num_epochs + 1), test_acc, label=\"SGD\", color=\"blue\")\n",
|
||
"\n",
|
||
"model = MNIST_CLS_Model(num_classes=10, dropout_rate=0)\n",
|
||
"optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate, alpha=0.99, eps=1e-8)\n",
|
||
"print(f\"optimizer: RMSprop\")\n",
|
||
"train_loss, test_acc = train_MNIST_CLS(model, optimizer, num_epochs=num_epochs)\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.plot(range(1, num_epochs + 1), train_loss, label=\"RMSprop\", color=\"green\")\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"plt.plot(range(1, num_epochs + 1), test_acc, label=\"RMSprop\", color=\"green\")\n",
|
||
"\n",
|
||
"model = MNIST_CLS_Model(num_classes=10, dropout_rate=0)\n",
|
||
"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-8)\n",
|
||
"print(f\"optimizer: Adam\")\n",
|
||
"train_loss, test_acc = train_MNIST_CLS(model, optimizer, num_epochs=num_epochs)\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.plot(range(1, num_epochs + 1), train_loss, label=\"Adam\", color=\"orange\")\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"plt.plot(range(1, num_epochs + 1), test_acc, label=\"Adam\", color=\"orange\")\n",
|
||
"\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Train Loss')\n",
|
||
"plt.legend()\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Test Accuracy')\n",
|
||
"plt.legend()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f923ef50-3be9-43ac-baed-e24b2441695f",
|
||
"metadata": {},
|
||
"source": [
|
||
"从实验结果看:\n",
|
||
"\n",
|
||
"- 在`SGD`中应用momentum,效果较为稳定,但是需要较大的学习率和较多的训练轮数才能达到良好的效果。\n",
|
||
"- `RMSprop`的训练过程不稳定,尤其是训练刚开始时,loss巨大,如果学习率较大,容易出现梯度爆炸,模型不能正常训练;\n",
|
||
"- `Adam`效果较好,能够迅速达到优秀的效果,但是学习率较高时,同样无法正常训练。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1b436404-c2d7-4ef9-b074-3afb357ab457",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 任务四\n",
|
||
"\n",
|
||
"**对多分类任务实验中实现早停机制,并在测试集上测试。**\n",
|
||
"\n",
|
||
"- 选择上述实验中效果最好的组合,手动将训练数据划分为训练集和验证集,实现早停机制,并在测试集上进行测试。训练集:验证集=8:2,早停轮数为5。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "e21b56bb-f3af-4f83-a9e6-e07470d21a7b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch [1/161], Train Loss: 12.2845923901, Test Acc: 48.640%, Val Loss: 3.1191009283\n",
|
||
"Epoch [6/161], Train Loss: 2.2487579882, Test Acc: 90.830%, Val Loss: 0.6830908656\n",
|
||
"Epoch [11/161], Train Loss: 1.1002770066, Test Acc: 94.950%, Val Loss: 0.3718443066\n",
|
||
"Epoch [16/161], Train Loss: 0.6551254392, Test Acc: 96.450%, Val Loss: 0.2593539357\n",
|
||
"Epoch [21/161], Train Loss: 0.4071293250, Test Acc: 97.200%, Val Loss: 0.2089994848\n",
|
||
"Epoch [26/161], Train Loss: 0.2799520865, Test Acc: 97.490%, Val Loss: 0.1810068339\n",
|
||
"Epoch [31/161], Train Loss: 0.1668622196, Test Acc: 97.710%, Val Loss: 0.1702276319\n",
|
||
"Epoch [36/161], Train Loss: 0.1138065355, Test Acc: 97.810%, Val Loss: 0.1672033146\n",
|
||
"Epoch [41/161], Train Loss: 0.0686604744, Test Acc: 98.050%, Val Loss: 0.1577220634\n",
|
||
"Epoch [43/161], Train Loss: 0.0556522962, Test Acc: 97.960%, Val Loss: 0.1697444022\n",
|
||
"Early stopping after 43 epochs.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1050x350 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"learning_rate = 5e-4\n",
|
||
"num_epochs = 161\n",
|
||
"batch_size = 8192\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_mnist_dataset = datasets.MNIST(root=\"./dataset\", train=True, transform=transform, download=True)\n",
|
||
"test_mnist_dataset = datasets.MNIST(root=\"./dataset\", train=False, transform=transform, download=True)\n",
|
||
"\n",
|
||
"train_dataset_length = int(0.8 * len(train_mnist_dataset))\n",
|
||
"val_dataset_length = len(train_mnist_dataset) - train_dataset_length\n",
|
||
"train_mnist_dataset, val_mnist_dataset = random_split(\n",
|
||
" train_mnist_dataset,\n",
|
||
" [train_dataset_length, val_dataset_length],\n",
|
||
" generator=torch.Generator().manual_seed(42),\n",
|
||
")\n",
|
||
"\n",
|
||
"train_loader = DataLoader(dataset=train_mnist_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"val_loader = DataLoader(dataset=val_mnist_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"test_loader = DataLoader(dataset=test_mnist_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)\n",
|
||
"\n",
|
||
"model = MNIST_CLS_Model(num_classes=10, dropout_rate=0.2).to(device)\n",
|
||
"criterion = nn.CrossEntropyLoss()\n",
|
||
"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-8, weight_decay=0)\n",
|
||
"\n",
|
||
"early_stopping_patience = 5\n",
|
||
"best_val_loss = float(\"inf\")\n",
|
||
"current_patience = 0\n",
|
||
"\n",
|
||
"train_loss = list()\n",
|
||
"test_acc = list()\n",
|
||
"val_loss = list()\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" model.train()\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)\n",
|
||
" one_hot_targets = one_hot(targets, num_classes=num_classes).to(dtype=torch.float)\n",
|
||
"\n",
|
||
" outputs = model(images)\n",
|
||
" loss = criterion(outputs, one_hot_targets)\n",
|
||
" total_epoch_loss += loss.item()\n",
|
||
"\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
"\n",
|
||
" model.eval()\n",
|
||
" with torch.no_grad():\n",
|
||
" total_epoch_acc = 0\n",
|
||
" for image, targets in test_loader:\n",
|
||
" image = image.to(device)\n",
|
||
" targets = targets.to(device)\n",
|
||
"\n",
|
||
" outputs = model(image)\n",
|
||
" pred = softmax(outputs, dim=1)\n",
|
||
" total_epoch_acc += (pred.argmax(1) == targets).sum().item()\n",
|
||
" avg_epoch_acc = total_epoch_acc / len(test_mnist_dataset)\n",
|
||
"\n",
|
||
" val_total_epoch_loss = 0\n",
|
||
" for image, targets in val_loader:\n",
|
||
" image = image.to(device)\n",
|
||
" targets = targets.to(device)\n",
|
||
" one_hot_targets = one_hot(targets, num_classes=num_classes).to(dtype=torch.float)\n",
|
||
"\n",
|
||
" outputs = model(image)\n",
|
||
" loss = criterion(outputs, one_hot_targets)\n",
|
||
" val_total_epoch_loss += loss.item()\n",
|
||
" \n",
|
||
" if epoch % 5 == 0:\n",
|
||
" print(\n",
|
||
" f\"Epoch [{epoch + 1}/{num_epochs}],\",\n",
|
||
" f\"Train Loss: {total_epoch_loss:.10f},\",\n",
|
||
" f\"Test Acc: {avg_epoch_acc * 100:.3f}%,\",\n",
|
||
" f\"Val Loss: {val_total_epoch_loss:.10f}\",\n",
|
||
" )\n",
|
||
" train_loss.append(total_epoch_loss)\n",
|
||
" test_acc.append(avg_epoch_acc * 100)\n",
|
||
" val_loss.append(val_total_epoch_loss)\n",
|
||
"\n",
|
||
" if val_total_epoch_loss < best_val_loss:\n",
|
||
" best_val_loss = val_total_epoch_loss\n",
|
||
" current_patience = 0\n",
|
||
" else:\n",
|
||
" current_patience += 1\n",
|
||
" if current_patience >= early_stopping_patience:\n",
|
||
" print(\n",
|
||
" f\"Epoch [{epoch + 1}/{num_epochs}],\",\n",
|
||
" f\"Train Loss: {total_epoch_loss:.10f},\",\n",
|
||
" f\"Test Acc: {avg_epoch_acc * 100:.3f}%,\",\n",
|
||
" f\"Val Loss: {val_total_epoch_loss:.10f}\",\n",
|
||
" )\n",
|
||
" print(f\"Early stopping after {epoch + 1} epochs.\")\n",
|
||
" num_epochs = epoch + 1\n",
|
||
" break\n",
|
||
" \n",
|
||
"plt.figure(figsize=(10.5, 3.5))\n",
|
||
"plt.subplot(1, 3, 1)\n",
|
||
"plt.plot(range(1, num_epochs + 1), train_loss, label='Train Loss', color='orange')\n",
|
||
"plt.subplot(1, 3, 2)\n",
|
||
"plt.plot(range(1, num_epochs + 1), test_acc, label='Test Acc', color='green')\n",
|
||
"plt.subplot(1, 3, 3)\n",
|
||
"plt.plot(range(1, num_epochs + 1), val_loss, label='Val Loss', color='blue')\n",
|
||
"\n",
|
||
"plt.subplot(1, 3, 1)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Train Loss')\n",
|
||
"plt.legend()\n",
|
||
"plt.subplot(1, 3, 2)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Test Accuracy')\n",
|
||
"plt.legend()\n",
|
||
"plt.subplot(1, 3, 3)\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Val Loss')\n",
|
||
"plt.legend()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e213cd2d-9b8e-4658-bd73-85c37f4d28e4",
|
||
"metadata": {},
|
||
"source": [
|
||
"将`MNIST`数据集的原本的训练集拆分成8:2的两部分,分别作为训练集和验证集。\n",
|
||
"\n",
|
||
"综合之前的训练效果,选择dropout概率为0.2,优化器选择`Adam`进行训练。\n",
|
||
"\n",
|
||
"设置早停机制为:验证集的loss连续5轮不再减少。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3d76958b-d6e8-4725-8d7e-08ef0c187149",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 心得体会\n",
|
||
"\n",
|
||
"这次网络优化实验让我对深度学习中的网络优化技术有了更深入的理解。通过手动实现dropout、L2正则化以及各种优化器算法,我巩固了这些技术的原理和实现过程。尤其是手动实现对我理解这些技术内在机理很有帮助。\n",
|
||
"\n",
|
||
"实验中,我观察到了不同优化技术对模型性能的不同影响。例如,增大dropout概率虽然会增加训练loss,但有利于提高泛化能力;适当的L2正则化可以防止过拟合,但正则化系数过大也会弱化性能。SGD、RMSprop、Adam等优化器也展现出不同的训练特点。这让我意识到选择合适的优化策略对获得一个好的模型非常重要。\n",
|
||
"\n",
|
||
"通过实现早停机制防止过拟合,我了解到了如何在训练过程中使用验证集评估性能,以提前终止训练避免过拟合的技巧。这也展现出合理评估模型在训练中的重要性。\n",
|
||
"\n",
|
||
"通过这次实验,我对深度学习优化技术有了更深的理解,也收获了如何构建一个实际有效的深度学习模型的宝贵经验。这是一次非常有意义的实验,让我对深度学习模型有了更直观的感受。"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|