MATLAB中的CNN卷积网络回归预测时loss收敛到0.1就不再变化

在使用CNN卷积网络进行回归预测时，如果loss在训练过程中收敛到0.1，但是不再变化，可能存在以下几种原因：

1. 过拟合：模型在训练集上表现良好，但是在测试集上表现较差。在训练过程中，可以使用一些正则化方法，如L1/L2正则化、dropout等，减少模型过拟合的风险。

2. 学习率设置过大或过小：学习率设置不当可能导致模型无法收敛或者收敛速度过慢。可以尝试调整学习率大小，找到最优的学习率。

3. 数据集不足或质量不佳：数据集太小或者数据质量不佳可能导致模型无法学习到有效的特征。可以尝试增加数据集的大小，或者对数据进行清洗和预处理。

4. 模型复杂度不足：模型复杂度不足可能无法完全捕捉数据中的复杂模式。可以尝试增加模型的复杂度，如增加网络层数、增加卷积核数量等。

相关问题

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。

CNN卷积神经网络手写数字识别 ，matlab代码

以下是一个简单的卷积神经网络手写数字识别的 MATLAB 代码示例：

% 加载 MNIST 数据集
[train_images, train_labels, test_images, test_labels] = load_mnist();

% 设置网络参数
input_size = 28 * 28; % 输入图像大小
hidden_size = 100; % 隐藏层大小
output_size = 10; % 输出层大小
filter_size = 5; % 卷积核大小
num_filters = 20; % 卷积核数量
pool_size = 2; % 池化层大小
conv_stride = 1; % 卷积步长
pool_stride = 2; % 池化步长

% 初始化权重
Wc = randn(filter_size, filter_size, num_filters) * 0.01; % 卷积层权重
bc = zeros(num_filters, 1); % 卷积层偏置
Wh = randn(hidden_size, input_size / (pool_size ^ 2) * num_filters) * 0.01; % 隐藏层权重
bh = zeros(hidden_size, 1); % 隐藏层偏置
Wo = randn(output_size, hidden_size) * 0.01; % 输出层权重
bo = zeros(output_size, 1); % 输出层偏置

% 训练网络
learning_rate = 0.1;
batch_size = 100;
num_epochs = 10;
num_batches = size(train_images, 2) / batch_size;

for epoch = 1:num_epochs
    for batch = 1:num_batches
        % 获取当前批次的数据
        batch_images = train_images(:, (batch - 1) * batch_size + 1:batch * batch_size);
        batch_labels = train_labels(:, (batch - 1) * batch_size + 1:batch * batch_size);

        % 前向传播
        conv_out = convolve(batch_images, Wc, bc, conv_stride);
        pool_out = pool(conv_out, pool_size, pool_stride);
        hidden_out = relu(Wh * reshape(pool_out, [], batch_size) + bh);
        output_out = softmax(Wo * hidden_out + bo);

        % 计算损失和梯度
        loss = cross_entropy(output_out, batch_labels);
        d_output = output_out - batch_labels;
        d_hidden = (Wo' * d_output) .* relu_derivative(hidden_out);
        d_pool = reshape(Wh' * d_hidden, [], sqrt(size(pool_out, 1)), sqrt(size(pool_out, 1)), batch_size);
        d_conv = pool_back(d_pool, conv_out, pool_size, pool_stride) .* relu_derivative(conv_out);
        d_Wo = d_output * hidden_out';
        d_bh = sum(d_hidden, 2);
        d_Wh = d_hidden * reshape(pool_out, [], batch_size)';
        d_bc = squeeze(sum(sum(d_conv, 2), 3));
        d_Wc = zeros(size(Wc));
        for i = 1:num_filters
            for j = 1:size(batch_images, 2)
                d_Wc(:, :, i) = d_Wc(:, :, i) + conv2(rot90(batch_images(:, j), 2), rot90(d_conv(:, :, i, j), 2), 'valid');
            end
        end

        % 更新权重
        Wo = Wo - learning_rate * d_Wo;
        bh = bh - learning_rate * d_bh;
        Wh = Wh - learning_rate * d_Wh;
        bc = bc - learning_rate * d_bc;
        Wc = Wc - learning_rate * d_Wc;
    end

    % 在测试集上测试准确率
    test_out = predict(test_images, Wc, bc, Wh, bh, Wo, bo);
    test_acc = sum(test_out == test_labels) / numel(test_labels);
    fprintf('Epoch %d, Test Accuracy: %f\n', epoch, test_acc);
end

该代码实现了一个包含卷积层、池化层、隐藏层和输出层的卷积神经网络，并使用 MNIST 数据集进行训练和测试。具体来说，它使用了以下函数：

- `load_mnist()`：加载 MNIST 数据集
- `convolve()`：实现卷积运算
- `pool()`：实现池化运算
- `relu()`：实现 ReLU 激活函数
- `softmax()`：实现 Softmax 激活函数
- `cross_entropy()`：计算交叉熵损失
- `relu_derivative()`：计算 ReLU 激活函数的导数
- `pool_back()`：实现反向池化运算
- `predict()`：对测试集进行预测

请注意，这只是一个简单的示例代码，你可以根据自己的需求进行修改和扩展。