% 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 CNNnet = 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)]);中出现原因: 错误使用 gather 输入参数太多。如何用gather解决?给代码
时间: 2023-06-25 14:05:30 浏览: 47
在给出解决方案之前,我们需要先理解 `gather` 函数的作用。
在深度学习中,通常会使用 GPU 进行加速计算,而 GPU 计算是异步进行的。因此,当我们需要从 GPU 中获取数据时,就需要使用 `gather` 函数将数据从 GPU 中取回到 CPU 上。
在你给出的代码中,错误出现在以下这行代码:
```
accuracy = sum(gather(YPred == Ytest))/numel(Ytest);
```
错误信息提示我们输入参数太多,这是因为 `gather` 函数只接受一个输入参数,而你使用了两个参数。
解决方案是将 `YPred` 和 `Ytest` 分别先转换为 CPU 上的变量,然后再使用 `gather` 函数将它们从 GPU 中取回到 CPU 上。修改代码如下:
```
accuracy = sum(gather(Ypred) == gather(Ytest)) / numel(Ytest);
```
这样就可以解决输入参数过多的问题了。
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)]);
```
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