matlab中cnn的优化算法代码
时间: 2024-03-28 14:28:25 浏览: 13
Matlab中CNN的优化算法代码可以参考以下示例:
1. 随机梯度下降(SGD):
```matlab
% 设置学习率、迭代次数和批次大小
learning_rate = 0.01;
num_epochs = 10;
batch_size = 32;
% 初始化权重和偏置
weights = randn(num_filters, filter_size, filter_size, num_channels) / sqrt(num_channels);
bias = zeros(num_filters, 1);
% 训练模型
for epoch = 1:num_epochs
% 打乱数据集
shuffled_indices = randperm(num_samples);
X_train = X_train(:, :, :, shuffled_indices);
Y_train = Y_train(shuffled_indices);
% 进行一次迭代
for batch_start = 1:batch_size:num_samples
% 获取当前批次的数据
batch_end = min(batch_start + batch_size - 1, num_samples);
X_batch = X_train(:, :, :, batch_start:batch_end);
Y_batch = Y_train(batch_start:batch_end);
% 前向传播
conv_output = conv_forward(X_batch, weights, bias);
relu_output = relu_forward(conv_output);
pool_output = pool_forward(relu_output);
fc_input = reshape(pool_output, [], size(pool_output, 4));
fc_output = fc_forward(fc_input, fc_weights, fc_bias);
Y_pred = softmax_forward(fc_output);
% 计算损失和准确率
loss = cross_entropy_loss(Y_pred, Y_batch);
accuracy = mean(argmax(Y_pred) == argmax(Y_batch));
% 反向传播
dY_pred = softmax_backward(Y_pred, Y_batch);
dfc_output = fc_backward(dY_pred, fc_input, fc_weights, fc_bias);
dpool_output = reshape(fc_backward(dfc_output, relu_output, fc_weights(:, 1:end-1), fc_bias), size(pool_output));
drelu_output = pool_backward(dpool_output, relu_output);
dconv_output = relu_backward(drelu_output, conv_output);
% 更新权重和偏置
dweights = conv_backward(dconv_output, X_batch, weights, bias);
dbias = sum(sum(sum(dconv_output, 1), 2), 4);
weights = weights - learning_rate * dweights;
bias = bias - learning_rate * dbias;
% 输出当前迭代的损失和准确率
fprintf('Epoch %d, Batch %d-%d: Loss = %f, Accuracy = %f\n', epoch, batch_start, batch_end, loss, accuracy);
end
end
```
2. Adam优化算法:
```matlab
% 设置超参数
learning_rate = 0.001;
beta1 = 0.9;
beta2 = 0.999;
epsilon = 1e-8;
% 初始化权重、偏置和动量/平方梯度
weights = randn(num_filters, filter_size, filter_size, num_channels) / sqrt(num_channels);
bias = zeros(num_filters, 1);
m_weights = zeros(size(weights));
v_weights = zeros(size(weights));
m_bias = zeros(size(bias));
v_bias = zeros(size(bias));
% 训练模型
for epoch = 1:num_epochs
% 打乱数据集
shuffled_indices = randperm(num_samples);
X_train = X_train(:, :, :, shuffled_indices);
Y_train = Y_train(shuffled_indices);
% 进行一次迭代
for batch_start = 1:batch_size:num_samples
% 获取当前批次的数据
batch_end = min(batch_start + batch_size - 1, num_samples);
X_batch = X_train(:, :, :, batch_start:batch_end);
Y_batch = Y_train(batch_start:batch_end);
% 前向传播
conv_output = conv_forward(X_batch, weights, bias);
relu_output = relu_forward(conv_output);
pool_output = pool_forward(relu_output);
fc_input = reshape(pool_output, [], size(pool_output, 4));
fc_output = fc_forward(fc_input, fc_weights, fc_bias);
Y_pred = softmax_forward(fc_output);
% 计算损失和准确率
loss = cross_entropy_loss(Y_pred, Y_batch);
accuracy = mean(argmax(Y_pred) == argmax(Y_batch));
% 反向传播
dY_pred = softmax_backward(Y_pred, Y_batch);
dfc_output = fc_backward(dY_pred, fc_input, fc_weights, fc_bias);
dpool_output = reshape(fc_backward(dfc_output, relu_output, fc_weights(:, 1:end-1), fc_bias), size(pool_output));
drelu_output = pool_backward(dpool_output, relu_output);
dconv_output = relu_backward(drelu_output, conv_output);
% 计算动量/平方梯度
m_weights = beta1 * m_weights + (1 - beta1) * dweights;
v_weights = beta2 * v_weights + (1 - beta2) * dweights.^2;
m_bias = beta1 * m_bias + (1 - beta1) * dbias;
v_bias = beta2 * v_bias + (1 - beta2) * dbias.^2;
% 更新权重和偏置
weights = weights - learning_rate * m_weights ./ (sqrt(v_weights) + epsilon);
bias = bias - learning_rate * m_bias ./ (sqrt(v_bias) + epsilon);
% 输出当前迭代的损失和准确率
fprintf('Epoch %d, Batch %d-%d: Loss = %f, Accuracy = %f\n', epoch, batch_start, batch_end, loss, accuracy);
end
end
```
以上代码仅为示例,具体实现可能需要根据具体问题做出适当调整。