人工智能导论 实验指导

人工智能导论实验指导手册目录第2章人工智能数学基础 1实验一.全批量梯度下降与小批量梯度下降的优化轨迹对比 1实验二多种优化算法的迭代行为与收敛特性对比 3第3章机器学习基础 8实验一.“泰坦尼克号：生还预测”数据集分类 8实验二.“心衰临床记录”数据集分析 9实验三.“fruits-360”数据集中的苹果图像分类 12实验四.通过动态规划求解迷宫寻路任务的最优策略 16第4章深度学习新方法 21实验一.基于激活最大化的卷积神经网络可视化 21实验二.基于集成梯度算法的归因可视化 23第6章智能体系统 27实验一.基于工具函数调用的智能体任务执行 27实验二.单智能体闭环流程模拟 29第7章人工智能实践 32实验一.旧金山犯罪分类数据集多分类预测 32实验二.基于DQN的CartPole平衡控制 34实验三.KaggleTitanic乘客生存预测 37第2章人工智能数学基础实验一.全批量梯度下降与小批量梯度下降的优化轨迹对比1.实验目的（1）理解批量大小（BatchSize）对梯度估计稳定性、参数更新路径的影响，掌握全批量梯度下降（FGD）与小批量梯度下降（MBGD）的差异。；（2）掌握线性回归模型下均方误差（MSE）目标函数的构造方法，以及基于负梯度方向更新模型参数的基本流程。（3）学会在参数空间中绘制损失等高线并叠加优化轨迹，从可视化角度对比两种方法的“平滑下降”与“振荡前行”。2.实验准备安装Python3.8及以上版本，并确保已安装NumPy与Matplotlib两个依赖库（命令：pipinstallnumpymatplotlib）。3.实验内容编写程序，按以下步骤完成。（1）生成合成数据。设输入服从标准正态分布，噪声项ε服从均值为0、标准差为0.4的正态分布，按方程生成100条样本作为训练集。（2）定义均方误差损失函数，并实现其关于和的一阶偏导数。（3）实现批量梯度下降算法。统一从出发，学习率取0.05，训练50个轮次（Epoch），记录每次参数更新后的和。（4）分别按批量大小为100的全批量梯度下降（FGD）和批量大小为10的小批量梯度下降（MBGD）运行训练过程，并输出两组方法的最终参数。（5）在参数平面上计算MSE损失等高线，将两种方法的优化轨迹叠加在等高线图上，并标注起始点与全局最优点。（6）完成结果分析。从轨迹平滑程度、更新次数、最终参数三个角度，说明小批量随机性对优化过程的影响，并将图像保存为PNG图片。4.参考代码importnumpyasnpimportmatplotlib.pyplotaspltplt.rcParams['font.sans-serif']=['SimHei','MicrosoftYaHei']plt.rcParams['axes.unicode_minus']=False#1.全局参数设置SEED=42LR=0.05EPOCHS=50N_SAMPLES=100TRUE_W,TRUE_B=2.0,1.0INIT_W,INIT_B=-2.0,3.0#2.生成合成数据y=2x+1+epsilonnp.random.seed(SEED)X=np.random.randn(N_SAMPLES)noise=np.random.randn(N_SAMPLES)*0.4y=TRUE_W*X+TRUE_B+noise#3.均方误差损失与梯度defmse_loss(w,b,X,y):returnnp.mean((y-(w*X+b))**2)defmse_grad(w,b,X,y):pred=w*X+bgrad_w=-2.0*np.mean(X*(y-pred))grad_b=-2.0*np.mean(y-pred)returngrad_w,grad_b#4.梯度下降训练deftrain(batch_size):np.random.seed(SEED)w,b=INIT_W,INIT_Bw_hist,b_hist=[w],[b]indices=np.arange(N_SAMPLES)forepochinrange(EPOCHS):np.random.shuffle(indices)forstartinrange(0,N_SAMPLES,batch_size):idx=indices[start:start+batch_size]gw,gb=mse_grad(w,b,X[idx],y[idx])w-=LR*gwb-=LR*gbw_hist.append(w)b_hist.append(b)returnnp.array(w_hist),np.array(b_hist)fgd_w,fgd_b=train(batch_size=100)mbgd_w,mbgd_b=train(batch_size=10)#5.可视化：损失等高线+优化轨迹defplot_results():w_range=np.linspace(-3.0,3.5,100)b_range=np.linspace(-0.5,4.5,100)W_grid,B_grid=np.meshgrid(w_range,b_range)Z_loss=np.zeros_like(W_grid)foriinrange(W_grid.shape[0]):forjinrange(W_grid.shape[1]):Z_loss[i,j]=mse_loss(W_grid[i,j],B_grid[i,j],X,y)fig,ax=plt.subplots(figsize=(8,7))levels=np.arange(0.5,8.0,0.8)cntr=ax.contour(W_grid,B_grid,Z_loss,levels=levels,colors='gray',linewidths=1.0,alpha=0.7)ax.clabel(cntr,inline=True,fontsize=8,fmt='%.1f')ax.plot(mbgd_w,mbgd_b,color='black',linestyle='--',linewidth=2.0,alpha=0.8,label='小批量梯度下降(MBGD)')ax.plot(fgd_w,fgd_b,color='#2e7fb7',linestyle='-',linewidth=2.5,alpha=0.9,zorder=10,label='全批量梯度下降(FGD)')ax.plot(INIT_W,INIT_B,marker='o',color='black',markersize=8,linestyle='None',zorder=11,label='起始点')ax.plot(TRUE_W,TRUE_B,marker='*',color='#2e7fb7',markersize=16,linestyle='None',zorder=12,label='全局最优点')ax.set_xlabel('权重参数w',fontsize=14)ax.set_ylabel('偏置参数b',fontsize=14)ax.legend(loc='upperleft',fontsize=12)ax.grid(True,linestyle=':',alpha=0.4)ax.set_xlim(-2.8,3.2)ax.set_ylim(-0.2,4.2)plt.tight_layout()plt.savefig('case2_1_result.png',bbox_inches='tight',dpi=300)plt.show()if__name__=="__main__":plot_results()实验二多种优化算法的迭代行为与收敛特性对比1.实验目的（1）理解Rosenbrock函数作为非凸优化测试函数的典型特征，认识狭长谷形目标函数给梯度优化带来的困难。（2）掌握SGD、Momentum、Adagrad、RMSprop与Adam五种优化算法的更新规则，理解动量项、历史梯度累积与自适应学习率机制的作用。（3）学会通过参数空间优化轨迹图与目标函数对数收敛曲线，对比不同算法的收敛速度、稳定性与路径形态。2.实验准备安装Python3.8及以上版本，并确保已安装NumPy与Matplotlib两个依赖库（命令：pipinstallnumpymatplotlib）。3.实验内容编写程序，按以下步骤完成。（1）实现Rosenbrock函数，其全局最优解为，对应函数值为0。（2）计算其一阶偏导数，实现，其中，。（3）设置停止条件，综合使用梯度范数、函数值变化、位置变化与最大迭代次数4种准则判断算法是否收敛。（4）分别实现SGD、Momentum、Adagrad、RMSprop与Adam五种参数更新规则，统一从初始点出发，记录优化轨迹与目标函数值序列。（5）绘制双子图。左图绘制Rosenbrock函数的等高线，并叠加5种算法的优化轨迹；右图绘制目标函数值随迭代次数变化的半对数曲线。（6）完成结果分析。比较5种算法的迭代次数、最终点与最终函数值，结合动量与自适应学习率机制说明收敛差异，并将图像保存为PNG图片。4.参考代码importnumpyasnpimportmatplotlib.pyplotaspltplt.rcParams['font.sans-serif']=['SimHei','MicrosoftYaHei']plt.rcParams['axes.unicode_minus']=Falsedeffun(x,y):"""Rosenbrock函数"""return(1-x)**2+100*(y-x**2)**2defgrad(x,y):df_dx=-2*(1-x)-400*x*(y-x**2)df_dy=200*(y-x**2)returndf_dx,df_dy#停止条件GRADIENT_TOL=1e-6FUNCTION_TOL=1e-8POSITION_TOL=1e-8MAX_ITER=3000defcheck_convergence(x_new,y_new,x_old,y_old,dx,dy,f_new,f_old,k):ifnp.hypot(dx,dy)<GRADIENT_TOL:returnTrueifk>0andabs(f_new-f_old)<FUNCTION_TOL:returnTrueifk>0andnp.hypot(x_new-x_old,y_new-y_old)<POSITION_TOL:returnTruereturnk>=MAX_ITER-1defrun_optimizer(name,lr=1e-3,**kwargs):x,y=-1.2,1.0path_x,path_y=[x],[y]f_values=[fun(x,y)]vx=vy=0#MomentumGx=Gy=0#AdagradSx=Sy=0#RMSpropmx=my=0#Adam一阶矩vax=vay=0#Adam二阶矩x_old,y_old,f_old=x,y,f_values[0]foriinrange(MAX_ITER):dx,dy=grad(x,y)ifname=='SGD':x-=lr*dxy-=lr*dyelifname=='Momentum':m=kwargs.get('momentum',0.9)vx=m*vx+lr*dxvy=m*vy+lr*dyx-=vxy-=vyelifname=='Adagrad':eps=1e-8Gx+=dx**2Gy+=dy**2x-=lr/np.sqrt(Gx+eps)*dxy-=lr/np.sqrt(Gy+eps)*dyelifname=='RMSprop':d=kwargs.get('decay_rate',0.9)eps=1e-8Sx=d*Sx+(1-d)*dx**2Sy=d*Sy+(1-d)*dy**2x-=lr/np.sqrt(Sx+eps)*dxy-=lr/np.sqrt(Sy+eps)*dyelifname=='Adam':b1=kwargs.get('beta1',0.9)b2=kwargs.get('beta2',0.999)eps=1e-8mx=b1*mx+(1-b1)*dxmy=b1*my+(1-b1)*dyvax=b2*vax+(1-b2)*dx**2vay=b2*vay+(1-b2)*dy**2mxh=mx/(1-b1**(i+1))myh=my/(1-b1**(i+1))vxh=vax/(1-b2**(i+1))vyh=vay/(1-b2**(i+1))x-=lr*mxh/(np.sqrt(vxh)+eps)y-=lr*myh/(np.sqrt(vyh)+eps)f_new=fun(x,y)path_x.append(x)path_y.append(y)f_values.append(f_new)ifcheck_convergence(x,y,x_old,y_old,dx,dy,f_new,f_old,i):breakx_old,y_old,f_old=x,y,f_newreturn{'path_x':np.array(path_x),'path_y':np.array(path_y),'f_values':np.array(f_values),'iterations':i+1}#运行5种优化算法results={'SGD':run_optimizer('SGD',lr=0.002),'Momentum':run_optimizer('Momentum',lr=0.001,momentum=0.9),'Adagrad':run_optimizer('Adagrad',lr=0.2),'RMSprop':run_optimizer('RMSprop',lr=0.01),'Adam':run_optimizer('Adam',lr=0.05),}#绘图：左图轨迹，右图收敛曲线defplot_results(results):fig,axes=plt.subplots(1,2,figsize=(14,6),constrained_layout=True)THEME_BLUE='#2e7fb7'styles={'SGD':{'color':'#999999','linestyle':':','linewidth':2.0},'Momentum':{'color':'black','linestyle':'--','linewidth':1.8},'Adagrad':{'color':'black','linestyle':'-.','linewidth':1.8},'RMSprop':{'color':THEME_BLUE,'linestyle':'--','linewidth':2.0},'Adam':{'color':THEME_BLUE,'linestyle':'-','linewidth':2.5},}#子图1:优化轨迹ax1=axes[0]x_range=np.linspace(-1.5,1.5,200)y_range=np.linspace(-0.5,1.5,200)X,Y=np.meshgrid(x_range,y_range)Z=fun(X,Y)levels=np.logspace(-1,3,20)ax1.contour(X,Y,Z,levels=levels,colors='lightgray',linewidths=1.0,alpha=0.8)forname,styleinstyles.items():d=results[name]px,py=d['path_x'],d['path_y']iflen(px)>1000:idx=np.linspace(0,len(px)-1,1000,dtype=int)px,py=px[idx],py[idx]ax1.plot(px,py,label=name,**style,alpha=0.9)ax1.plot(-1.2,1.0,'ko',markersize=8,label='起始点',zorder=10)ax1.plot(1.0,1.0,'*',color=THEME_BLUE,markersize=16,label='全局最优点',zorder=10)ax1.set_xlabel('参数w1',fontsize=14)ax1.set_ylabel('参数w2',fontsize=14)ax1.set_xlim(-1.4,1.4)ax1.set_ylim(-0.2,1.4)ax1.legend(loc='best',fontsize=11)ax1.grid(True,linestyle=':',alpha=0.3)#子图2:目标函数收敛曲线(半对数)ax2=axes[1]forname,styleinstyles.items():ax2.semilogy(results[name]['f_values'],label=name,**style,alpha=0.9)ax2.set_xlabel('迭代次数',fontsize=14)ax2.set_ylabel('目标函数值f(x)(对数刻度)',fontsize=14)ax2.set_xlim(0,500)ax2.grid(True,linestyle=':',alpha=0.3,which='both')ax2.legend(loc='best',fontsize=11)plt.savefig('case2_2_result.png',bbox_inches='tight',dpi=300)plt.show()if__name__=="__main__":plot_results(results)

第3章机器学习基础实验一.“泰坦尼克号：生还预测”数据集分类1.实验目的（1）掌握如何通过XGBoost模型实现数据集分类；（2）掌握k折交叉验证方法。2.实验准备实验环境配置：通过anaconda等工具配置python、pandas、numpy、matplotlib、sklearn等必要的基础环境及扩展程序库。3.实验内容（1）下载“泰坦尼克号：生还预测”数据集（下载地址：/datasciencedojo/datasets/master/titanic.csv）；（2）通过一个包含了10棵决策树的XGBoost模型对数据集进行分类；（3）通过5折交叉验证进行实验验证，最终输出该模型在该数据集上的平均准确率。4.参考代码importpandasaspdimportnumpyasnpimportmatplotlib.pyplotaspltfromsklearn.model_selectionimportKFoldfromsklearn.metricsimportaccuracy_scorefromsklearn.ensembleimportRandomForestClassifierfrommatplotlib.font_managerimportFontProperties#==========================================#1.字体与排版配置#==========================================plt.rcParams['font.family']='serif'plt.rcParams['font.serif']=['TimesNewRoman']plt.rcParams['axes.unicode_minus']=Falseplt.rcParams['mathtext.fontset']='stix'zh_fonts=['SimHei','NotoSansCJKSC','MicrosoftYaHei','sans-serif']font_label=FontProperties(family=zh_fonts,size=18)font_tick=FontProperties(family=zh_fonts,size=14)#==========================================#2.数据获取与5-Fold执行#==========================================url="/datasciencedojo/datasets/master/titanic.csv"df=pd.read_csv(url)X=df[['Pclass','Sex','Age']].copy()X['Sex']=X['Sex'].map({'female':0,'male':1})X['Age']=X['Age'].fillna(X['Age'].median())y=df['Survived']kf=KFold(n_splits=5,shuffle=True,random_state=42)fold_accuracies=[]forfold_idx,(train_idx,val_idx)inenumerate(kf.split(X)):X_train,X_val=X.iloc[train_idx],X.iloc[val_idx]y_train,y_val=y.iloc[train_idx],y.iloc[val_idx]clf=RandomForestClassifier(n_estimators=10,max_depth=3,random_state=42)clf.fit(X_train,y_train)fold_accuracies.append(accuracy_score(y_val,clf.predict(X_val)))#==========================================#3.可视化绘制#==========================================fig,ax=plt.subplots(figsize=(12,6),facecolor='white')folds_label=[rf"第${i+1}$折"foriinrange(5)]ax.bar(folds_label,fold_accuracies,color='0.9',edgecolor='black',linewidth=1.2)mean_acc=np.mean(fold_accuracies)print(mean_acc)ax.axhline(mean_acc,color='#1f77b4',linestyle='--',linewidth=2.5)text_str=rf"平均准确率:${mean_acc*100:.2f}\%$"ax.text(0.5,mean_acc+0.01,text_str,color='#1f77b4',ha='center',va='bottom',fontproperties=font_label)#标签处理ax.set_ylabel(r"预测准确率",fontproperties=font_label)ax.set_xlabel(r"验证折数",fontproperties=font_label)#纵轴设置ax.set_ylim(0.6,0.88)current_ticks=ax.get_yticks()ax.set_yticks(current_ticks[:-1])ax.set_yticklabels([rf"${t:.1f}$"fortincurrent_ticks[:-1]],fontproperties=font_tick)#刻度设置ax.set_xticklabels(folds_label,fontproperties=font_tick)forlabelinax.get_yticklabels():label.set_fontproperties(font_tick)#移除冗余边框ax.spines['top'].set_visible(False)ax.spines['right'].set_visible(False)ax.grid(axis='y',linestyle=':',alpha=0.5)plt.tight_layout()plt.show()实验二.“心衰临床记录”数据集分析1.实验目的（1）理解逻辑回归模型在二分类任务中的基本应用；（2）掌握临床记录数据的标准化处理与特征贡献分析方法。2.实验准备实验环境配置：通过anaconda等工具配置python、pandas、matplotlib、seaborn、sklearn等必要的基础环境及扩展程序库。3.实验内容（1）下载“心衰临床记录”数据集（下载地址：/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv）；（2）对临床特征进行标准化处理，并训练逻辑回归模型预测死亡事件；（3）根据模型标准化系数分析各临床指标对心衰风险的影响，并绘制特征贡献柱状图。4.参考代码importpandasaspdimportmatplotlib.pyplotaspltimportseabornassnsfromsklearn.linear_modelimportLogisticRegressionfromsklearn.preprocessingimportStandardScalerimportmatplotlib.font_managerasfm#==========================================#1.字体底层配置：确保中英文字体正确加载#==========================================defget_font_names():zh_font='SimHei'en_font='TimesNewRoman'fonts=[forfinfm.fontManager.ttflist]ifzh_fontnotinfonts:zh_font='sans-serif'ifen_fontnotinfonts:en_font='serif'returnzh_font,en_fontzh_font_name,en_font_name=get_font_names()#设定全局字号LABEL_SIZE=18TICK_SIZE=14#==========================================#2.数据处理与建模(Kaggle心力衰竭数据集)#==========================================url="/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv"df=pd.read_csv(url)feature_mapping={'age':'年龄','anaemia':'贫血','creatinine_phosphokinase':'肌酸激酶','diabetes':'糖尿病','ejection_fraction':'射血分数','high_blood_pressure':'高血压','platelets':'血小板','serum_creatinine':'血清肌酐','serum_sodium':'血清钠','sex':'性别','smoking':'吸烟'}X=df[list(feature_mapping.keys())]y=df['DEATH_EVENT']scaler=StandardScaler()X_scaled=scaler.fit_transform(X)model=LogisticRegression(penalty='l2',C=1.0)model.fit(X_scaled,y)attr_df=pd.DataFrame({'特征':[feature_mapping[f]forfinfeature_mapping.keys()],'标准化系数':model.coef_[0]}).sort_values(by='标准化系数',ascending=False)#==========================================#3.绘图#==========================================fig,ax=plt.subplots(figsize=(12,6),facecolor='white')#使用灰度值：深灰(0.3)代表正向风险，浅灰(0.7)代表负向保护#添加黑色边框(edgecolor)提升印刷清晰度colors=['0.3'ifx>0else'0.7'forxinattr_df['标准化系数']]sns.barplot(x='标准化系数',y='特征',data=attr_df,palette=colors,edgecolor='black',linewidth=0.8,ax=ax)#设置标签ax.set_xlabel('标准化系数',fontsize=LABEL_SIZE,fontname=zh_font_name)ax.set_ylabel('临床特征指标',fontsize=LABEL_SIZE,fontname=zh_font_name)#显式设置刻度字体forlabelinax.get_yticklabels():label.set_fontname(zh_font_name)label.set_fontsize(TICK_SIZE)forlabelinax.get_xticklabels():label.set_fontname(en_font_name)label.set_fontsize(TICK_SIZE)#绘制中心参考线ax.axvline(0,color='black',linestyle='-',linewidth=1.0)#设置网格ax.grid(axis='x',linestyle='--',alpha=0.3,color='gray')#添加黑白图例(Legend)frommatplotlib.patchesimportPatchlegend_elements=[Patch(facecolor='0.3',edgecolor='black',label='增加风险'),Patch(facecolor='0.7',edgecolor='black',label='降低风险')]leg=ax.legend(handles=legend_elements,loc='lowerright',prop={'family':zh_font_name,'size':TICK_SIZE},frameon=True)leg.get_frame().set_edgecolor('black')plt.tight_layout()plt.show()实验三.“fruits-360”数据集中的苹果图像分类1.实验目的（1）了解卷积神经网络在图像分类任务中的基本结构与训练流程；（2）掌握使用PyTorch和torchvision构建、训练和评估图像分类模型的方法。2.实验准备实验环境配置：通过anaconda等工具配置python、torch、torchvision、numpy等必要的深度学习环境及扩展程序库，并准备fruits-360数据集。3.实验内容（1）下载并整理fruits-360数据集，按照Training和Test目录组织图像数据；（2）构建包含卷积层、批归一化、池化层和全连接层的卷积神经网络；（3）完成数据增强、模型训练、验证评估和最优模型保存，输出苹果图像分类结果。4.参考代码（用于训练一个简单的卷积神经网络以进行苹果分类）importos,math,time,copy,argparsefrompathlibimportPathimporttorchimporttorch.nnasnnimporttorch.nn.functionalasFfromtorch.utils.dataimportDataLoaderfromtorchvisionimportdatasets,transforms##Model#classCNN64BN(nn.Module):def__init__(self,in_ch=3,num_classes=131,p_drop=0.3):super().__init__()self.features=nn.Sequential(#64x64nn.Conv2d(in_ch,16,3,padding=1,bias=False),nn.BatchNorm2d(16),nn.ReLU(inplace=True),nn.MaxPool2d(2),#32x32nn.Conv2d(16,32,3,padding=1,bias=False),nn.BatchNorm2d(32),nn.ReLU(inplace=True),nn.MaxPool2d(2),#16x16nn.Conv2d(32,32,3,padding=1,bias=False),nn.BatchNorm2d(32),nn.ReLU(inplace=True),nn.MaxPool2d(2),#8x8nn.Conv2d(32,64,3,padding=1,bias=False),nn.BatchNorm2d(64),nn.ReLU(inplace=True),nn.MaxPool2d(2),#4x4)self.classifier=nn.Sequential(nn.Flatten(),#4*4*64=1024nn.Dropout(p_drop),nn.Linear(1024,256),nn.ReLU(inplace=True),nn.Dropout(p_drop),nn.Linear(256,num_classes))defforward(self,x):x=self.features(x)x=self.classifier(x)returnx##LabelSmoothingCE#classLabelSmoothingCrossEntropy(nn.Module):def__init__(self,smoothing=0.1):super().__init__()self.smoothing=smoothingdefforward(self,preds,target):#preds:(N,C),target:(N,)n_classes=preds.size(-1)log_probs=F.log_softmax(preds,dim=-1)withtorch.no_grad():true_dist=torch.zeros_like(log_probs)true_dist.fill_(self.smoothing/(n_classes-1))true_dist.scatter_(1,target.unsqueeze(1),1.0-self.smoothing)returntorch.mean(torch.sum(-true_dist*log_probs,dim=-1))##Utilities#defaccuracy(logits,y):preds=logits.argmax(dim=1)return(preds==y).float().mean().item()defget_loaders(data_root,img_size=64,batch_size=128,num_workers=4):mean=[0.485,0.456,0.406]std=[0.229,0.224,0.225]train_tfms=transforms.Compose([transforms.RandomResizedCrop(img_size,scale=(0.7,1.0)),transforms.RandomHorizontalFlip(),transforms.RandomRotation(10),transforms.ColorJitter(0.2,0.2,0.2,0.1),transforms.ToTensor(),transforms.Normalize(mean,std),])val_tfms=transforms.Compose([transforms.Resize(int(img_size*1.15)),transforms.CenterCrop(img_size),transforms.ToTensor(),transforms.Normalize(mean,std),])train_dir=Path(data_root)/"Training"val_dir=Path(data_root)/"Test"train_ds=datasets.ImageFolder(train_dir,transform=train_tfms)val_ds=datasets.ImageFolder(val_dir,transform=val_tfms)train_loader=DataLoader(train_ds,batch_size=batch_size,shuffle=True,num_workers=num_workers,pin_memory=True)val_loader=DataLoader(val_ds,batch_size=batch_size,shuffle=False,num_workers=num_workers,pin_memory=True)returntrain_loader,val_loader,len(train_ds.classes)defcosine_warmup(optimizer,total_steps,warmup_steps=500,min_lr=1e-6):deflr_lambda(step):ifstep<warmup_steps:return(step+1)/warmup_stepsprogress=(step-warmup_steps)/max(1,total_steps-warmup_steps)returnmin_lr+0.5*(1-min_lr)*(1+math.cos(math.pi*progress))returntorch.optim.lr_scheduler.LambdaLR(optimizer,lr_lambda)##Train/Validate#deftrain_one_epoch(model,loader,optimizer,scaler,criterion,device,max_grad_norm=1.0):model.train()loss_sum,acc_sum,n=0.0,0.0,0forimgs,labelsinloader:imgs,labels=imgs.to(device,non_blocking=True),labels.to(device,non_blocking=True)optimizer.zero_grad(set_to_none=True)withtorch.cuda.amp.autocast(enabled=(device.type=='cuda')):logits=model(imgs)loss=criterion(logits,labels)scaler.scale(loss).backward()ifmax_grad_normisnotNone:scaler.unscale_(optimizer)torch.nn.utils.clip_grad_norm_(model.parameters(),max_grad_norm)scaler.step(optimizer)scaler.update()bs=labels.size(0)loss_sum+=loss.item()*bsacc_sum+=accuracy(logits.detach(),labels)*bsn+=bsreturnloss_sum/n,acc_sum/n@torch.no_grad()defevaluate(model,loader,criterion,device):model.eval()loss_sum,acc_sum,n=0.0,0.0,0forimgs,labelsinloader:imgs,labels=imgs.to(device,non_blocking=True),labels.to(device,non_blocking=True)logits=model(imgs)loss=criterion(logits,labels)bs=labels.size(0)loss_sum+=loss.item()*bsacc_sum+=accuracy(logits,labels)*bsn+=bsreturnloss_sum/n,acc_sum/n##Main#defmain():parser=argparse.ArgumentParser()parser.add_argument('--data',type=str,required=True,help='pathtofruits-360root(containsTraining/andTest/)')parser.add_argument('--epochs',type=int,default=40)parser.add_argument('--batch-size',type=int,default=128)parser.add_argument('--lr',type=float,default=3e-4)parser.add_argument('--weight-decay',type=float,default=0.01)parser.add_argument('--img-size',type=int,default=64)parser.add_argument('--num-workers',type=int,default=4)parser.add_argument('--label-smoothing',type=float,default=0.1)parser.add_argument('--patience',type=int,default=7,help='earlystoppingpatience(epochs)')parser.add_argument('--dropout',type=float,default=0.3)parser.add_argument('--seed',type=int,default=42)args=parser.parse_args()torch.manual_seed(args.seed)torch.cuda.manual_seed_all(args.seed)device=torch.device('cuda'iftorch.cuda.is_available()else'cpu')train_loader,val_loader,num_classes=get_loaders(args.data,args.img_size,args.batch_size,args.num_workers)model=CNN64BN(in_ch=3,num_classes=num_classes,p_drop=args.dropout).to(device)criterion=LabelSmoothingCrossEntropy(smoothing=args.label_smoothing)optimizer=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=args.weight-decayifhasattr(args,'weight-decay')elseargs.weight_decay)total_steps=args.epochs*len(train_loader)scheduler=cosine_warmup(optimizer,total_steps,warmup_steps=min(500,total_steps//10))scaler=torch.cuda.amp.GradScaler(enabled=(device.type=='cuda'))best_acc,best_w=0.0,copy.deepcopy(model.state_dict())epochs_no_improve=0forepochinrange(1,args.epochs+1):t0=time.time()tr_loss,tr_acc=train_one_epoch(model,train_loader,optimizer,scaler,criterion,device)val_loss,val_acc=evaluate(model,val_loader,criterion,device)scheduler.step()dt=time.time()-t0print(f'Epoch{epoch:>3}/{args.epochs}|'f'trainloss{tr_loss:.4f}acc{tr_acc*100:5.2f}%|'f'valloss{val_loss:.4f}acc{val_acc*100:5.2f}%|'f'{dt:.1f}s')#Earlystoppingonval_accifval_acc>best_acc:best_acc=val_accbest_w=copy.deepcopy(model.state_dict())epochs_no_improve=0torch.save({'model':best_w,'acc':best_acc},'best_fruits360_cnn64bn.pth')else:epochs_no_improve+=1ifepochs_no_improve>=args.patience:print(f'Noimprovementfor{args.patience}epochs.Earlystop.')breakmodel.load_state_dict(best_w)val_loss,val_acc=evaluate(model,val_loader,criterion,device)print(f'BestValAcc:{best_acc*100:.2f}%|FinalEvalAcc:{val_acc*100:.2f}%')print('Savedweights->best_fruits360_cnn64bn.pth')if__name__=='__main__':main()实验四.通过动态规划求解迷宫寻路任务的最优策略1.实验目的（1）理解马尔可夫决策过程中的状态、动作、奖励和策略等基本概念；（2）掌握利用动态规划和值迭代方法求解迷宫寻路最优策略的过程。2.实验准备实验环境配置：通过anaconda等工具配置python、numpy、matplotlib等必要的基础环境及扩展程序库。3.实验内容（1）根据迷宫结构建立网格状态空间，设置墙体、陷阱、起点和目标位置；（2）定义不同状态动作对应的奖励函数，并通过值迭代计算各状态价值；（3）根据最优价值函数生成最优策略，并绘制包含状态价值和方向箭头的迷宫图。4.参考代码from__future__importannotationsfrompathlibimportPathimportmatplotlib.pyplotaspltimportnumpyasnpfrommatplotlib.colorsimportListedColormapfrommatplotlib.patchesimportRectangleOUTPUT_PATH=Path("exp-3-4.png")defbuild_grid()->np.ndarray: """Returna4x4layoutencodedbyintegersforplotting.""" #3:start,4:goal,2:trap,1:wall,0:freespace returnnp.array( [ [0,0,0,0], [0,1,1,0], [1,0,0,0], [0,0,0,0], ] )defbuild_rewards()->dict: """Returntherewardfunctionasperuserspecification.""" return{ (0,0):{3:1,1:-1},#(1,1)右+1,下-1 (0,1):{2:-1,3:1},#(1,2)左-1,右+1 (0,2):{2:-1,3:1},#(1,3)左-1,右+1 (0,3):{2:-1,1:1},#(1,4)左-1,下+1 (1,0):{0:1},#(2,1)上+1 (1,3):{0:-1,1:1},#(2,4)上-1,下+1 (2,1):{3:20,1:-1},#(3,2)右+20(目标),下-1 (2,3):{2:20,0:-1,1:-1},#(3,4)左+20(目标),上-1,下-1 (3,0):{3:1},#(4,1)右+1 (3,1):{2:-1,3:1,0:1},#(4,2)左-1,右+1,上+1 (3,2):{2:-1,3:-1,0:20},#(4,3)左-1,右-1,上+20(目标) (3,3):{2:1,0:1}#(4,4)左+1,上+1 }defbuild_colormap()->ListedColormap: """Createahigh-contrastgrayscalepaletteforeasierdifferentiation.""" colors=[ "#f7f7f7",#freespace "#c2c2c2",#wall "#6a6a6a",#trap "#111111",#start "#ffffff",#goal ] returnListedColormap(colors)defoverlay_textures(ax:plt.Axes,grid:np.ndarray)->None: """Addhatchtexturessowallsandtrapsremaindistinctingrayscaleprints.""" textures={ 1:{"hatch":"//","edgecolor":"#4a4a4a","linewidth":0.6}, 2:{"hatch":None,"edgecolor":"#4a4a4a","linewidth":0.6}, 3:{"hatch":None,"edgecolor":"#111111","linewidth":0.8}, 4:{"hatch":None,"edgecolor":"#444444","linewidth":0.8}, } n_rows,n_cols=grid.shape forrowinrange(n_rows): forcolinrange(n_cols): style=textures.get(grid[row,col]) ifstyleisNone: continue rect=Rectangle( (col-0.5,row-0.5), 1, 1, facecolor=(0,0,0,0), hatch=style["hatch"], edgecolor=style["edgecolor"], linewidth=style["linewidth"], ) ax.add_patch(rect)defvalue_iteration(grid:np.ndarray,rewards:dict,gamma=0.9,epsilon=1e-6)->tuple[dict,dict]: """Performvalueiterationtocomputeoptimalstatevaluesandpolicyusingthegivenrewards.""" n_rows,n_cols=grid.shape states=[(r,c)forrinrange(n_rows)forcinrange(n_cols)ifgrid[r,c]==0] V={s:0forsinstates} goal=(2,2) ifgoalinV: V[goal]=0#absorbingstate actions=[0,1,2,3]#up,down,left,right defget_next(s,a): r,c=s ifa==0:nr,nc=r-1,c elifa==1:nr,nc=r+1,c elifa==2:nr,nc=r,c-1 elifa==3:nr,nc=r