matlab受限波尔兹曼基代码
时间: 2023-09-20 21:06:28 浏览: 45
以下是一个使用 Matlab 实现的简单受限波尔兹曼机(Restricted Boltzmann Machine,RBM)的示例代码:
```matlab
% RBM 参数设置
num_visible = 6; % 可见层神经元数量
num_hidden = 4; % 隐藏层神经元数量
learning_rate = 0.1; % 学习率
num_epochs = 1000; % 迭代次数
% 初始化权重和偏置
weights = randn(num_visible, num_hidden);
hidden_bias = zeros(1, num_hidden);
visible_bias = zeros(1, num_visible);
% 导入训练数据
data = load('training_data.mat'); % 自行准备一个训练数据集
num_samples = size(data, 1);
% 训练 RBM
for epoch = 1:num_epochs
% 随机选择一个样本
sample_idx = randi(num_samples);
sample = data(sample_idx, :);
% 正向传播(从可见层到隐藏层)
hidden_activations = sample * weights + hidden_bias;
hidden_probs = sigmoid(hidden_activations);
hidden_states = hidden_probs > rand(size(hidden_probs));
% 反向传播(从隐藏层到可见层)
visible_reconstructions = hidden_states * weights' + visible_bias;
visible_probs = sigmoid(visible_reconstructions);
visible_states = visible_probs > rand(size(visible_probs));
% 更新权重和偏置
positive_gradient = sample' * hidden_probs;
negative_gradient = visible_states' * hidden_states;
weights = weights + learning_rate * (positive_gradient - negative_gradient) / num_samples;
visible_bias = visible_bias + learning_rate * sum(sample - visible_states) / num_samples;
hidden_bias = hidden_bias + learning_rate * sum(hidden_probs - hidden_states) / num_samples;
end
% sigmoid 函数
function output = sigmoid(x)
output = 1 ./ (1 + exp(-x));
end
```
请注意,此代码仅为简单示例,并未进行详尽的参数调整和错误处理。此外,你需要自行准备一个适合的训练数据集(training_data.mat)用于训练 RBM 模型。