请使用matlab代码将一下代码改为可以进行人脸识别的网络 layers = [ imageInputLayer([28 28 1],"Name","imageinput") convolution2dLayer([3 3],32,"Name","conv_2","Padding","same") reluLayer("Name","relu_2") maxPooling2dLayer([5 5],"Name","maxpool_2","Padding","same") convolution2dLayer([3 3],32,"Name","conv_3","Padding","same") reluLayer("Name","relu_3") maxPooling2dLayer([5 5],"Name","maxpool_3","Padding","same") convolution2dLayer([3 3],32,"Name","conv_1","Padding","same") reluLayer("Name","relu_1") maxPooling2dLayer([5 5],"Name","maxpool_1","Padding","same") fullyConnectedLayer(10,"Name","fc") softmaxLayer("Name","softmax") classificationLayer("Name","classoutput")];
时间: 2023-07-12 22:00:44 浏览: 56
要将该代码改为可以进行人脸识别的网络,需要进行以下几个步骤:
1. 更改输入层的大小为对应的人脸图像大小,一般为[224 224 3]。
2. 将卷积层和池化层的大小调整为适合处理人脸图像的大小。
3. 更改最后一个全连接层的输出大小,以适应人脸识别任务。
4. 在分类层之前添加人脸识别的特有层,如人脸对齐、人脸识别模型等。
下面是一个简单的示例代码:
layers = [
imageInputLayer([224 224 3],"Name","imageinput")
convolution2dLayer([3 3],32,"Name","conv_1","Padding","same")
reluLayer("Name","relu_1")
maxPooling2dLayer([2 2],"Name","maxpool_1","Padding","same")
convolution2dLayer([3 3],64,"Name","conv_2","Padding","same")
reluLayer("Name","relu_2")
maxPooling2dLayer([2 2],"Name","maxpool_2","Padding","same")
convolution2dLayer([3 3],128,"Name","conv_3","Padding","same")
reluLayer("Name","relu_3")
maxPooling2dLayer([2 2],"Name","maxpool_3","Padding","same")
fullyConnectedLayer(256,"Name","fc1")
reluLayer("Name","relu_fc1")
fullyConnectedLayer(128,"Name","fc2")
reluLayer("Name","relu_fc2")
fullyConnectedLayer(10,"Name","fc3")
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")
];
注:以上代码仅为示例,具体实现还需根据实际需要进行调整。