cnn卷积神经网络matlab代码

以下是一个简单的CNN卷积神经网络的MATLAB代码示例：

% 加载数据和标签
load mnist_uint8;
train_x = double(train_x) / 255;
test_x = double(test_x) / 255;
train_y = double(train_y);
test_y = double(test_y);

% 设置网络参数
input_layer_size = 28 * 28;   % 输入层大小
hidden_layer_size = 50;       % 隐藏层大小
output_layer_size = 10;       % 输出层大小
filter_size = 5;              % 卷积核大小
num_filters = 20;             % 卷积核数量

% 初始化卷积核和偏置项
conv_filter = randn(filter_size, filter_size, num_filters);
conv_bias = zeros(num_filters, 1);

% 初始化隐藏层权重和偏置项
hidden_weights = randn(hidden_layer_size, num_filters * (input_layer_size - filter_size + 1)^2) / sqrt(num_filters * (input_layer_size - filter_size + 1)^2);
hidden_bias = zeros(hidden_layer_size, 1);

% 初始化输出层权重和偏置项
output_weights = randn(output_layer_size, hidden_layer_size) / sqrt(hidden_layer_size);
output_bias = zeros(output_layer_size, 1);

% 训练网络
num_epochs = 10;
learning_rate = 0.1;
batch_size = 100;

for epoch = 1:num_epochs
    % 随机打乱训练数据顺序
    shuffle_index = randperm(size(train_x, 1));
    train_x = train_x(shuffle_index, :);
    train_y = train_y(shuffle_index, :);

    % 每个batch进行一次前向传播和反向传播
    for batch = 1:(size(train_x, 1) / batch_size)
        % 获取一个batch的数据和标签
        batch_start = (batch - 1) * batch_size + 1;
        batch_end = batch * batch_size;
        batch_x = train_x(batch_start:batch_end, :);
        batch_y = train_y(batch_start:batch_end, :);

        % 前向传播
        conv_out = conv_layer(batch_x, conv_filter, conv_bias);
        conv_out_relu = relu_layer(conv_out);
        hidden_out = hidden_layer(conv_out_relu, hidden_weights, hidden_bias);
        hidden_out_relu = relu_layer(hidden_out);
        output_out = output_layer(hidden_out_relu, output_weights, output_bias);

        % 计算损失和准确率
        loss = cross_entropy_loss(output_out, batch_y);
        accuracy = accuracy_metric(output_out, batch_y);

        % 反向传播
        output_error = cross_entropy_loss_derivative(output_out, batch_y);
        hidden_error = hidden_layer_derivative(hidden_out_relu, output_weights, output_error);
        conv_error = conv_layer_derivative(batch_x, conv_filter, conv_bias, conv_out, hidden_error);

        % 更新卷积核和偏置项
        conv_filter = conv_filter - learning_rate * conv_error.filter_gradient;
        conv_bias = conv_bias - learning_rate * conv_error.bias_gradient;

        % 更新隐藏层权重和偏置项
        hidden_weights = hidden_weights - learning_rate * hidden_error.weights_gradient;
        hidden_bias = hidden_bias - learning_rate * hidden_error.bias_gradient;

        % 更新输出层权重和偏置项
        output_weights = output_weights - learning_rate * output_error.weights_gradient;
        output_bias = output_bias - learning_rate * output_error.bias_gradient;
    end

    % 在测试集上测试准确率
    conv_out = conv_layer(test_x, conv_filter, conv_bias);
    conv_out_relu = relu_layer(conv_out);
    hidden_out = hidden_layer(conv_out_relu, hidden_weights, hidden_bias);
    hidden_out_relu = relu_layer(hidden_out);
    output_out = output_layer(hidden_out_relu, output_weights, output_bias);
    accuracy = accuracy_metric(output_out, test_y);
    fprintf('Epoch %d: Test accuracy = %f\n', epoch, accuracy);
end

其中，`conv_layer`、`relu_layer`、`hidden_layer`、`output_layer`、`cross_entropy_loss`、`accuracy_metric`、`cross_entropy_loss_derivative`、`hidden_layer_derivative`、`conv_layer_derivative` 都是各自层的函数，需要自行实现。