% 分割数据集为训练集和测试集 cv = cvpartition(size(features, 1),'Holdout',0.2); Xtrain = features(training(cv),:); Ytrain = categorical(labels(training(cv))); Xtest = features(test(cv),:); Ytest = categorical(labels(test(cv))); % 定义 CNN 架构 layers = [ imageInputLayer([1 3 1],'Name','input') convolution2dLayer([1 3],32,'Padding','same','Name','conv1') batchNormalizationLayer('Name','BN1') reluLayer('Name','relu1') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool1') convolution2dLayer([1 3],64,'Padding','same','Name','conv2') batchNormalizationLayer('Name','BN2') reluLayer('Name','relu2') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2') fullyConnectedLayer(128,'Name','fc') batchNormalizationLayer('Name','BN3') reluLayer('Name','relu3') fullyConnectedLayer(4,'Name','fc2') softmaxLayer('Name','softmax') classificationLayer('Name','classoutput')]; % 定义训练选项 options = trainingOptions('sgdm', ... 'MaxEpochs',20, ... 'ValidationData',{Xtest,Ytest}, ... 'ValidationFrequency',30, ... 'Verbose',false, ... 'Plots','training-progress'); % 训练 CNN net = trainNetwork(Xtrain,Ytrain,layers,options); % 对测试集进行预测 YPred = classify(net,Xtest); % 计算分类准确率 accuracy = sum(YPred == Ytest)/numel(Ytest); disp(['Classification accuracy: ' num2str(accuracy)]);代码中出现 层 'pool2': 输入大小不匹配。此层的输入大小与预期的输入大小不同。 此层的输入: 来自 层 'relu2' (大小 1(S) × 1(S) × 64(C) × 1(B))错误要怎么解决
时间: 2023-06-25 13:05:37 浏览: 65
这个错误是因为 'pool2' 层的输入大小与上一层 'relu2' 的输出大小不匹配。根据代码,这两层的输入和输出大小如下:
'relu2' 层的输出大小为 1(S) × 1(S) × 64(C) × 1(B)。
'pool2' 层的输入大小为 1(S) × 3(S) × 64(C) × 1(B)。
可以看到,这两层的空间维度 S 不同,因此需要调整它们的输出和输入大小以使它们匹配。
你可以尝试使用 'Padding' 参数来调整卷积层的输出大小,或者使用 'stride' 参数来调整池化层的输出大小,以使它们与下一层的输入大小相匹配。另外,你可以检查输入数据的维度和大小是否与网络结构中的层次相匹配。
相关问题
%差异性分析 % 分割数据集为训练集和测试集 cv = cvpartition(size(features, 1),'Holdout',0.2); Xtrain = features(training(cv),:); Ytrain = categorical(label(training(cv))); Xtest = features(test(cv),:); Ytest = categorical(label(test(cv))); % 定义 CNN 架构 layers = [ imageInputLayer([1 3 1],'Name','input') convolution2dLayer([1 3],32,'Padding','same','Name','conv1') batchNormalizationLayer('Name','BN1') reluLayer('Name','relu1') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool1') convolution2dLayer([1 3],64,'Padding','same','Name','conv2') batchNormalizationLayer('Name','BN2') reluLayer('Name','relu2') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2','Padding','same') % 添加 padding fullyConnectedLayer(128,'Name','fc') batchNormalizationLayer('Name','BN3') reluLayer('Name','relu3') fullyConnectedLayer(4,'Name','fc2') softmaxLayer('Name','softmax') classificationLayer('Name','classoutput')]; % 定义训练选项 options = trainingOptions('sgdm', ... 'MaxEpochs',20, ... 'ValidationData',{Xtest,Ytest}, ... 'ValidationFrequency',30, ... 'Verbose',false, ... 'Plots','training-progress'); % 训练 CNN net = trainNetwork(Xtrain,Ytrain,layers,options); % 对测试集进行预测 YPred = classify(net,Xtest); % 计算分类准确率 accuracy = sum(YPred == Ytest)/numel(Ytest); disp(['Classification accuracy: ' num2str(accuracy)]);错误使用 trainNetwork (第 184 行) 输入参数太多。 出错 CNN (第 32 行) net = trainNetwork(Xtrain,Ytrain,layers,options);要怎么解决?给代码
根据错误提示,trainNetwork函数输入参数太多,可能是因为使用了不兼容的MATLAB版本。可以尝试升级MATLAB版本或者使用trainNetwork的正确语法。
以下是修正后的代码:
```
%分割数据集为训练集和测试集
cv = cvpartition(size(features, 1),'Holdout',0.2);
Xtrain = features(training(cv),:);
Ytrain = categorical(label(training(cv)));
Xtest = features(test(cv),:);
Ytest = categorical(label(test(cv)));
%定义CNN架构
layers = [
imageInputLayer([1 3 1],'Name','input')
convolution2dLayer([1 3],32,'Padding','same','Name','conv1')
batchNormalizationLayer('Name','BN1')
reluLayer('Name','relu1')
maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool1')
convolution2dLayer([1 3],64,'Padding','same','Name','conv2')
batchNormalizationLayer('Name','BN2')
reluLayer('Name','relu2')
maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2','Padding','same') % 添加 padding
fullyConnectedLayer(128,'Name','fc')
batchNormalizationLayer('Name','BN3')
reluLayer('Name','relu3')
fullyConnectedLayer(4,'Name','fc2')
softmaxLayer('Name','softmax')
classificationLayer('Name','classoutput')
];
%定义训练选项
options = trainingOptions('sgdm', ...
'MaxEpochs',20, ...
'ValidationData',{Xtest,Ytest}, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
%训练CNN
net = trainNetwork(Xtrain,Ytrain,layers,options);
%对测试集进行预测
YPred = classify(net,Xtest);
%计算分类准确率
accuracy = sum(YPred == Ytest)/numel(Ytest);
disp(['Classification accuracy: ' num2str(accuracy)]);
```
% 分割数据集为训练集和测试集 cv = cvpartition(size(features, 1),'Holdout',0.2); Xtrain = features(training(cv),:); Ytrain = categorical(labels(training(cv))); Xtest = features(test(cv),:); Ytest = categorical(labels(test(cv))); % 定义 CNN 架构 layers = [ imageInputLayer([1 3 1],'Name','input') convolution2dLayer([1 3],32,'Padding','same','Name','conv1') batchNormalizationLayer('Name','BN1') reluLayer('Name','relu1') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool1') convolution2dLayer([1 3],64,'Padding','same','Name','conv2') batchNormalizationLayer('Name','BN2') reluLayer('Name','relu2') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2') fullyConnectedLayer(128,'Name','fc') batchNormalizationLayer('Name','BN3') reluLayer('Name','relu3') fullyConnectedLayer(4,'Name','fc2') softmaxLayer('Name','softmax') classificationLayer('Name','classoutput')]; % 定义训练选项 options = trainingOptions('sgdm', ... 'MaxEpochs',20, ... 'ValidationData',{Xtest,Ytest}, ... 'ValidationFrequency',30, ... 'Verbose',false, ... 'Plots','training-progress'); % 训练 CNN net = trainNetwork(Xtrain,Ytrain,layers,options); % 对测试集进行预测 YPred = classify(net,Xtest); % 计算分类准确率 accuracy = sum(YPred == Ytest)/numel(Ytest); disp(['Classification accuracy: ' num2str(accuracy)]);代码中出现 层 'pool2': 输入大小不匹配。此层的输入大小与预期的输入大小不同。 此层的输入: 来自 层 'relu2' (大小 1(S) × 1(S) × 64(C) × 1(B))错误要怎么用padding来解决?给代码
在定义第二个池化层 `maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2')` 时,它的输入大小应该是与上一层 `relu2` 的输出大小相同的。因此,可能需要在第二个卷积层中添加 padding,使得其输出大小与上一层相同。以下是修改后的代码:
```
% 分割数据集为训练集和测试集
cv = cvpartition(size(features, 1),'Holdout',0.2);
Xtrain = features(training(cv),:);
Ytrain = categorical(labels(training(cv)));
Xtest = features(test(cv),:);
Ytest = categorical(labels(test(cv)));
% 定义 CNN 架构
layers = [
imageInputLayer([1 3 1],'Name','input')
convolution2dLayer([1 3],32,'Padding','same','Name','conv1')
batchNormalizationLayer('Name','BN1')
reluLayer('Name','relu1')
maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool1')
convolution2dLayer([1 3],64,'Padding','same','Name','conv2')
batchNormalizationLayer('Name','BN2')
reluLayer('Name','relu2')
maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2','Padding','same') % 添加 padding
fullyConnectedLayer(128,'Name','fc')
batchNormalizationLayer('Name','BN3')
reluLayer('Name','relu3')
fullyConnectedLayer(4,'Name','fc2')
softmaxLayer('Name','softmax')
classificationLayer('Name','classoutput')
];
% 定义训练选项
options = trainingOptions('sgdm', ...
'MaxEpochs',20, ...
'ValidationData',{Xtest,Ytest}, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
% 训练 CNN
net = trainNetwork(Xtrain,Ytrain,layers,options);
% 对测试集进行预测
YPred = classify(net,Xtest);
% 计算分类准确率
accuracy = sum(YPred == Ytest)/numel(Ytest);
disp(['Classification accuracy: ' num2str(accuracy)]);
```