提高网站排名,成全动漫免费观看在线看,网站建设哪家好知道,重庆网站建设 吧#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码实现 1 概述
基于LIMELocal Interpretable Model-Agnostic Explanations的CNN图像分类研究是一种用于解释CNN模型的方法。LIME是一种解释性模型旨在提供对黑盒模型如CNN预测结果的可解释性。下面是简要的步骤
1. 数据准备首先准备一个用于图像分类的数据集该数据集应包含图像样本和相应的标签。可以使用已有的公开数据集如MNIST、CIFAR-10或ImageNet。
2. 训练CNN模型使用准备好的数据集训练一个CNN模型。可以选择常见的CNN架构如VGG、ResNet或Inception等或者根据具体需求设计自定义的CNN架构。
3. 解释模型的预测结果使用LIME方法来解释CNN模型的预测结果。LIME采用局部特征解释方法在图像中随机生成一组可解释的超像素并对这些超像素进行采样。然后将这些采样结果输入到CNN模型中计算预测结果。
4. 生成解释性结果根据LIME采样的结果计算每个超像素对预测结果的影响程度。可以使用不同的解释性度量如权重、重要性分数或热图等。
5. 分析和验证结果对生成的解释性结果进行分析和验证。可以通过与真实标签进行对比或与其他解释方法进行比较来评估LIME方法的准确性和可靠性。
通过以上步骤可以实现对CNN图像分类模型的解释性研究。LIME方法可以帮助我们理解CNN模型在图像分类任务中的决策过程对于深入了解CNN模型的特征选择和预测行为非常有帮助。
2 运行结果 resultzeros(size(L)); for i1:N ROILi; resultresultROI.*max(mdl.Beta(i),0);% calculate the contribution if the weight is non-zero end % smoothing the LIME result. this is not included in the official % implementation result2imgaussfilt(result,8); % display the final result figure;imshow(I);hold on imagesc(result2,AlphaData,0.5); colormap jet;colorbar;hold off; title(Explanation using LIME); 部分代码
%% Sampling for Local Exploration % This section creates pertubated image as shown below. Each superpixel was % assigned 0 or 1 where the superpixel with 1 is displayed and otherwise colored % by black. % %
% the number of the process to make perturbated images % higher number of sampleNum leads to more reliable result with higher % computation cost sampleNum1000; % calculate similarity with the original image similarityzeros(sampleNum,1); indiceszeros(sampleNum,N); imgzeros(224,224,3,sampleNum); for i1:sampleNum % randomly black-out the superpixels indrand(N,1)rand(1)*.8; mapzeros(size(I,1:2)); for j[find(ind1)] ROILj; mapROImap; end img(:,:,:,i)imresize(I.*uint8(map),[224 224]); % calculate the similarity % other metrics for calculating similarity are also fine % this calculation also affetcts to the result similarity(i)1-nnz(ind)./numSuperPixel; indices(i,:)ind; end %% Predict the perturbated images using CNN model to interpret % Use |activations| function to explore the classification score for cat.
probactivations(net,uint8(img),prob,OutputAs,rows); scoreprob(:,classIdx); %% Fitting using weighted linear model % Use fitrlinear function to perform weighted linear fitting. Specify the weight % like Weights,similarity. The input indices represents 1 or 0. For example, % if the value of the variable indices is [1 0 1] , the first and third superpixels % are active and second superpixel is masked by black. The label to predict is % the score with each perturbated image. Note that this similarity was calculated % using Kernel function in the original paper.
sigma.35; weightsexp(-similarity.^2/(sigma.^2)); mdlfitrlinear(indices,score,Learner,leastsquares,Weights,weights); %% % Confirm the exponential kernel used for the weighting.
3 参考文献 部分理论来源于网络如有侵权请联系删除。 [1] Ribeiro, M.T., Singh, S. and Guestrin, C., 2016, August. Why should I trust you? Explaining the predictions of any classifier. In _Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining_ (pp. 1135-1144).
[2] He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In _Proceedings of the IEEE conference on computer vision and pattern recognition_ (pp. 770-778).
4 Matlab代码实现
