编写一段双分支卷积神经网络的自注意力机制代码

双分支卷积神经网络的自注意力机制可以通过以下Matlab代码实现：

function [Y, attn] = self_attention(X)
% X: input tensor with shape [batch_size, seq_len, hidden_size]
% Y: output tensor with shape [batch_size, seq_len, hidden_size]
% attn: attention tensor with shape [batch_size, seq_len, seq_len]

batch_size = size(X, 1);
seq_len = size(X, 2);
hidden_size = size(X, 3);

% Compute query, key, and value tensors
Q = reshape(X, [batch_size*seq_len, hidden_size]) * Wq;
K = reshape(X, [batch_size*seq_len, hidden_size]) * Wk;
V = reshape(X, [batch_size*seq_len, hidden_size]) * Wv;

% Reshape query, key, and value tensors
Q = reshape(Q, [batch_size, seq_len, num_heads, head_size]);
K = reshape(K, [batch_size, seq_len, num_heads, head_size]);
V = reshape(V, [batch_size, seq_len, num_heads, head_size]);

% Compute attention scores
scores = sum(Q .* K, 4) / sqrt(head_size);
attn = softmax(scores, 3);

% Apply attention scores to value tensor
Y = sum(attn .* V, 3);

% Reshape output tensor
Y = reshape(Y, [batch_size, seq_len, hidden_size]);
end

其中，`X`是输入张量，`Wq`、`Wk`、`Wv`是可学习的权重矩阵，`num_heads`和`head_size`是超参数，`softmax`是softmax函数。函数的输出包括输出张量`Y`和注意力张量`attn`。

需要注意的是，这段代码只是自注意力机制的实现，还需要将其嵌入到双分支卷积神经网络中才能得到完整的模型。此外，还需要根据具体的任务和数据进行调整和优化。