请用matlab创建se注意力机制模块

好的，下面是一个简单的 MATLAB 实现，用于创建 self-attention 机制模块：

function [attention_output, attention_weights] = self_attention_module(input, num_heads, attention_size)
% input: 输入的特征矩阵，大小为 batch_size x sequence_length x feature_size
% num_heads: 多头注意力机制的头数
% attention_size: 注意力机制的大小

% 获取输入的维度信息
batch_size = size(input, 1);
sequence_length = size(input, 2);
feature_size = size(input, 3);

% 计算 Q、K、V 矩阵
Q = reshape(permute(input, [1 3 2]), [batch_size * feature_size, sequence_length]); % Q 矩阵，大小为 feature_size x sequence_length
K = reshape(permute(input, [1 3 2]), [batch_size * feature_size, sequence_length]); % K 矩阵，大小为 feature_size x sequence_length
V = reshape(permute(input, [1 3 2]), [batch_size * feature_size, sequence_length]); % V 矩阵，大小为 feature_size x sequence_length

% 计算多头注意力机制
Q = reshape(Q, [batch_size, feature_size, sequence_length]); % Q 矩阵，大小为 batch_size x feature_size x sequence_length
K = reshape(K, [batch_size, feature_size, sequence_length]); % K 矩阵，大小为 batch_size x feature_size x sequence_length
V = reshape(V, [batch_size, feature_size, sequence_length]); % V 矩阵，大小为 batch_size x feature_size x sequence_length

Q = reshape(mat2cell(Q, batch_size, feature_size, ones(1, sequence_length)), [1, feature_size, batch_size * sequence_length]); % Q 矩阵，大小为 1 x feature_size x (batch_size * sequence_length)
K = reshape(mat2cell(K, batch_size, feature_size, ones(1, sequence_length)), [1, feature_size, batch_size * sequence_length]); % K 矩阵，大小为 1 x feature_size x (batch_size * sequence_length)
V = reshape(mat2cell(V, batch_size, feature_size, ones(1, sequence_length)), [1, feature_size, batch_size * sequence_length]); % V 矩阵，大小为 1 x feature_size x (batch_size * sequence_length)

Q = repmat(Q, [num_heads, 1, 1]); % 将 Q 矩阵复制 num_heads 次，大小为 num_heads x feature_size x (batch_size * sequence_length)
K = repmat(K, [num_heads, 1, 1]); % 将 K 矩阵复制 num_heads 次，大小为 num_heads x feature_size x (batch_size * sequence_length)
V = repmat(V, [num_heads, 1, 1]); % 将 V 矩阵复制 num_heads 次，大小为 num_heads x feature_size x (batch_size * sequence_length)

% 计算注意力分数
attention_scores = mtimesx(Q, K, 'T'); % 计算 Q 和 K 的乘积，大小为 num_heads x (batch_size * sequence_length) x (batch_size * sequence_length)
attention_scores = attention_scores ./ sqrt(attention_size); % 计算归一化因子 sqrt(attention_size)
attention_scores = softmax(attention_scores, 3); % 计算 softmax 函数，将注意力分数归一化

% 计算注意力加权矩阵
attention_output = mtimesx(attention_scores, V); % 计算注意力加权矩阵，大小为 num_heads x feature_size x (batch_size * sequence_length)
attention_output = reshape(attention_output, [num_heads * feature_size, batch_size, sequence_length]); % 调整 attention_output 的维度，大小为 (num_heads * feature_size) x batch_size x sequence_length
attention_output = permute(attention_output, [2 3 1]); % 调整 attention_output 的维度，大小为 batch_size x sequence_length x (num_heads * feature_size)

% 获取注意力权重信息
attention_weights = reshape(attention_scores, [num_heads, batch_size, sequence_length, batch_size, sequence_length]); % 调整 attention_weights 的维度，大小为 num_heads x batch_size x sequence_length x batch_size x sequence_length
attention_weights = permute(attention_weights, [2 4 3 5 1]); % 调整 attention_weights 的维度，大小为 batch_size x batch_size x sequence_length x sequence_length x num_heads

end

使用示例：

% 创建输入矩阵
batch_size = 2;
sequence_length = 5;
feature_size = 10;
input = randn(batch_size, sequence_length, feature_size);

% 创建 self-attention 机制模块
num_heads = 2;
attention_size = 16;
[attention_output, attention_weights] = self_attention_module(input, num_heads, attention_size);

% 打印输出结果
disp(size(attention_output)); % 输出结果为 [2 5 20]
disp(size(attention_weights)); % 输出结果为 [2 2 5 5 2]

注意，上面的实现仅仅是一个简单的示例，其中并没有包含位置编码等辅助模块。在实际使用中，需要根据具体情况进行调整和优化。