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：

% 定义残差块
function out = residual_block(input, filters)
    x = relu(batch_norm(conv2d(input, filters, 'same')));
    x = batch_norm(conv2d(x, filters, 'same'));
    out = input + x;
end

% 定义卷积层
function out = conv2d(input, filters, padding)
    out = conv2(input, filters, padding, 'same');
end

% 定义批量归一化层
function out = batch_norm(input)
    epsilon = 1e-6;
    gamma = ones(size(input, 3), 1);
    beta = zeros(size(input, 3), 1);
    mu = mean(input, [1, 2]);
    sigma = std(input, 0, [1, 2]);
    out = (input - mu) ./ sqrt(sigma.^2 + epsilon);
    out = gamma' .* out + beta';
    out = permute(out, [2, 1, 3]);
end

% 定义ReLU激活函数
function out = relu(input)
    out = max(input, 0);
end

% 定义残差神经网络
function out = residual_network(input)
    filters = 16;
    layers = 8;
    x = input;
    for i = 1:layers
        x = residual_block(x, filters);
    end
    out = x;
end

此代码实现了一个具有 8 个残差块的残差神经网络，每个块包含两个卷积层和一个批量归一化层，其中第二个卷积层的输出与块的输入相加，以形成残差连接。通过修改 filters 和 layers 的值，可以轻松地更改网络的大小和深度。