How To Use CMD2Model Class

1. Declaration:

CMD2Model *playgirl;

CMD2Model *sword;

2. Init():

playgirl=new CMD2Model;

playgirl->Load("data//playgirl//tris.md2","data//..//..pcx");

sword=new CMD2Model;

sword->Load("data//...","...");

3. Destroy():

if(playgirl)

{

    delete playgirl;

    playgirl=NULL;

}

if(sword)

{

   delete sword;

   sword=NULL;

}

4. Render():

playgirl->AnimateModel(84,94,0.08);

sword->AnimateModel(84,94,0.08);

Homework 5: Image Classification November 19, 2025 Due Date: December 10 by 23:59:59 Introduction In this assignment, you will implement and test various image classification models on the CIFAR-10 dataset. The goals of this assignment are as follows: • Implement and compare the linear classifier and the full-connected neural network. • Train and test two types of classifiers. • Compare the AdamW and the SGD optimizer based on FCNN. • Compare the StepLR and the CosineAnnealingLR scheduler based on FCNN. You can learn how to create, train, and test a model using PyTorch here. You are highly encouraged to go through this tutorial before you start. Here are some other supplementary materials that may help you: • PyTorch Documentation • PyTorch Chinese Documentation • Dive into deep learning Notes for hyper-parameter tuning: you can get full score when accuracy is above 60%, save time for your busy end-of-term season. 1 Define Classifiers (30 pts.) Here are some useful function: • torch.nn.Linear() • torch.nn.ReLU() • torch.nn.Tanh() You are free to use any torch functions. Note: if you want to use Convolution-based classifiers, feel free to have a try. But we don’t set bonus in this homework. 1.1 Linear classifier (15 pts.) Add your own code to the LinearClassifier class to define a linear classifier. Your classifier is required to process a mini-batch data. 1 1.2 Full-connected neural network classifier (15 pts.) Add your own code to the FCNN class to define a full-connected neural network classifier. You are responsible for choosing the network depth, width, and activation type. 2 Implement the training and testing function (40 pts.) There is a whole training code in PyTorch Tutorial: train a classifier, you can learn from it. In this task, you need to implement the train() and test() function that can choose a model, optimizer, scheduler, and so on; see the end of the main.py for details. 3 Report(30 pts.) You can use TensorBoard in PyTorch to record and visualize the loss and accuracy curves. Here is a tutorial introducing TensorBoard. Based on FCNN, analysis sec￾tion 3.1 and section 3.2. 3.1 Compare AdamW and SGD optimizer (10 pts.) Train the classifiers you implemented using the AdamW (torch.optim.AdamW) and SGD (torch.optim.SGD) optimizer and compare the loss and accuracy curves. Put the results in your report. 3.2 Compare StepLR and CosineAnnealingLR scheduler (10 pts.) Train the classifiers you implemented using two learning rate schedulers, including the StepLR (torch.optim.lr scheduler.StepLR) and CosineAnnealingLR (torch.opt￾im.lr scheduler.CosineAnnealingLR) scheduler and compare the loss and accuracy curves. Put the results in your report. 3.3 Visualization (10 pts.) You have now completed the entire process of this project. Put all the visualizations and results in your report: the loss and accuracy curves and the final classification accuracy scores. For the result of Linear Classifier, you can report with arbitrary optimizer or learning rate scheduler. For FCNN, you should report section 3.1 and section 3.2. 4 Submit Be sure to zip your code and final report; Name it as StudentID YourName HW5.zip. Any wrong name will cost you 0.5 pts in final score. 2 这个是题目,你看一下
12-11
基于可靠性评估序贯蒙特卡洛模拟法的配电网可靠性评估研究(Matlab代码实现)内容概要:本文围绕“基于可靠性评估序贯蒙特卡洛模拟法的配电网可靠性评估研究”,介绍了利用Matlab代码实现配电网可靠性的仿真分析方法。重点采用序贯蒙特卡洛模拟法对配电网进行长时间段的状态抽样与统计,通过模拟系统元件的故障与修复过程,评估配电网的关键可靠性指标,如系统停电频率、停电持续时间、负荷点可靠性等。该方法能够有效处理复杂网络结构与设备时序特性,提升评估精度,适用于含分布式电源、电动汽车等新型负荷接入的现代配电网。文中提供了完整的Matlab实现代码与案例分析,便于复现和扩展应用。; 适合人群:具备电力系统基础知识和Matlab编程能力的高校研究生、科研人员及电力行业技术人员,尤其适合从事配电网规划、运行与可靠性分析相关工作的人员; 使用场景及目标:①掌握序贯蒙特卡洛模拟法在电力系统可靠性评估中的基本原理与实现流程;②学习如何通过Matlab构建配电网仿真模型并进行状态转移模拟;③应用于含新能源接入的复杂配电网可靠性定量评估与优化设计; 阅读建议:建议结合文中提供的Matlab代码逐段调试运行,理解状态抽样、故障判断、修复逻辑及指标统计的具体实现方式,同时可扩展至不同网络结构或加入更多不确定性因素进行深化研究。
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