残差神经网络matlab代码

以下是一个简单的残差神经网络的 MATLAB 代码示例：

% 加载训练数据
load('data.mat');

% 定义神经网络结构
input_layer = imageInputLayer([28 28 1]);
conv_layer1 = convolution2dLayer(5, 20);
relu_layer1 = reluLayer();
conv_layer2 = convolution2dLayer(5, 20);
relu_layer2 = reluLayer();
residual_layer1 = additionLayer(2);
conv_layer3 = convolution2dLayer(5, 20);
relu_layer3 = reluLayer();
conv_layer4 = convolution2dLayer(5, 20);
relu_layer4 = reluLayer();
residual_layer2 = additionLayer(2);
pooling_layer = maxPooling2dLayer(2);
fully_connected_layer1 = fullyConnectedLayer(100);
fully_connected_layer2 = fullyConnectedLayer(10);
softmax_layer = softmaxLayer();
classification_layer = classificationLayer();

% 连接神经网络层
layers = [
    input_layer
    conv_layer1
    relu_layer1
    conv_layer2
    relu_layer2
    residual_layer1
    conv_layer3
    relu_layer3
    conv_layer4
    relu_layer4
    residual_layer2
    pooling_layer
    fully_connected_layer1
    fully_connected_layer2
    softmax_layer
    classification_layer
];

% 定义训练选项
options = trainingOptions('sgdm', ...
    'MaxEpochs', 10, ...
    'InitialLearnRate', 0.01, ...
    'Verbose', true, ...
    'Plots', 'training-progress');

% 训练神经网络
net = trainNetwork(train_data, train_labels, layers, options);

% 评估神经网络性能
test_predictions = classify(net, test_data);
accuracy = sum(test_predictions == test_labels) / numel(test_labels);
disp(['Accuracy: ' num2str(accuracy)]);

这个代码示例定义了一个简单的残差神经网络，用于对 MNIST 数据集进行数字分类。代码中的每一层都使用了 MATLAB 的内置函数进行定义，并且在 `layers` 变量中进行连接。训练选项通过 `trainingOptions` 函数进行定义，并且使用 `trainNetwork` 函数进行训练。最后，使用 `classify` 函数和测试数据对神经网络进行评估，并且计算分类准确率。