图注意力神经网络的matlab仿真程序
时间: 2023-10-01 14:04:52 浏览: 48
以下是一个简单的图注意力神经网络的 MATLAB 代码实现,用于节点分类任务:
首先,导入数据集:
```
load('cora.mat');
X = full(X);
y = full(y);
A = full(A);
```
然后,定义模型的参数:
```
n = size(X,1); % 节点数
f = size(X,2); % 特征数
k = 8; % 每个节点的邻居数
h = 16; % 隐藏层节点数
c = size(unique(y),1); % 类别数
lr = 0.01; % 学习率
epochs = 1000; % 迭代次数
```
接下来,定义模型的参数矩阵:
```
W1 = rand(f,h); % 输入层权重矩阵
W2 = rand(h,c); % 输出层权重矩阵
```
然后,定义图注意力层的计算过程:
```
function [H1] = graph_attention(X,A,W1,k)
f = size(X,2);
h = size(W1,2);
n = size(X,1);
H1 = zeros(n,h);
for i=1:n
xi = X(i,:);
Ai = A(:,i);
Ni = find(Ai);
if length(Ni)<k
Ni = [Ni;zeros(k-length(Ni),1)];
end
for j=1:k
if Ni(j)==0
break;
end
xj = X(Ni(j),:);
eij = tanh(xi*W1*xj');
H1(i,:) = H1(i,:) + eij*xj;
end
end
end
```
然后,定义模型的训练过程:
```
for epoch=1:epochs
H1 = graph_attention(X,A,W1,k);
H2 = tanh(H1*W2);
[loss,grad] = softmax_loss(H2,y);
if mod(epoch,100)==0
fprintf('Epoch %d, loss=%f\n',epoch,loss);
end
W2 = W2 - lr*H1'*grad;
W1 = W1 - lr*graph_attention_grad(X,A,W1,k,grad,H1);
end
```
其中,softmax_loss 函数计算交叉熵损失和梯度,graph_attention_grad 函数计算图注意力层的梯度。
最后,定义模型的预测过程:
```
function [y_pred] = predict(X,A,W1,W2)
H1 = graph_attention(X,A,W1,k);
H2 = tanh(H1*W2);
[~,y_pred] = max(H2,[],2);
end
```
完整的代码实现如下:
```
load('cora.mat');
X = full(X);
y = full(y);
A = full(A);
n = size(X,1); % 节点数
f = size(X,2); % 特征数
k = 8; % 每个节点的邻居数
h = 16; % 隐藏层节点数
c = size(unique(y),1); % 类别数
lr = 0.01; % 学习率
epochs = 1000; % 迭代次数
W1 = rand(f,h); % 输入层权重矩阵
W2 = rand(h,c); % 输出层权重矩阵
for epoch=1:epochs
H1 = graph_attention(X,A,W1,k);
H2 = tanh(H1*W2);
[loss,grad] = softmax_loss(H2,y);
if mod(epoch,100)==0
fprintf('Epoch %d, loss=%f\n',epoch,loss);
end
W2 = W2 - lr*H1'*grad;
W1 = W1 - lr*graph_attention_grad(X,A,W1,k,grad,H1);
end
y_pred = predict(X,A,W1,W2);
fprintf('Accuracy=%f\n',sum(y_pred==y)/n);
function [H1] = graph_attention(X,A,W1,k)
f = size(X,2);
h = size(W1,2);
n = size(X,1);
H1 = zeros(n,h);
for i=1:n
xi = X(i,:);
Ai = A(:,i);
Ni = find(Ai);
if length(Ni)<k
Ni = [Ni;zeros(k-length(Ni),1)];
end
for j=1:k
if Ni(j)==0
break;
end
xj = X(Ni(j),:);
eij = tanh(xi*W1*xj');
H1(i,:) = H1(i,:) + eij*xj;
end
end
end
function [grad] = graph_attention_grad(X,A,W1,k,grad_out,H1)
f = size(X,2);
h = size(W1,2);
n = size(X,1);
grad = zeros(f,h);
for i=1:n
xi = X(i,:);
Ai = A(:,i);
Ni = find(Ai);
if length(Ni)<k
Ni = [Ni;zeros(k-length(Ni),1)];
end
for j=1:k
if Ni(j)==0
break;
end
xj = X(Ni(j),:);
eij = tanh(xi*W1*xj');
grad_eij = grad_out(i,:)*xj;
grad_xj = grad_out(i,:)*eij;
grad_xi = grad_xj*W1';
grad_W1 = xi'*grad_xj*eij';
grad(i,:) = grad(i,:) + grad_xi;
W1 = W1 - lr*grad_W1;
end
end
end
function [loss,grad] = softmax_loss(X,y)
n = size(X,1);
c = size(X,2);
scores = exp(X);
probs = scores./sum(scores,2);
loss = -sum(log(probs(sub2ind(size(probs),1:n,y'))))/n;
grad = probs;
grad(sub2ind(size(grad),1:n,y')) = grad(sub2ind(size(grad),1:n,y'))-1;
grad = grad/n;
end
function [y_pred] = predict(X,A,W1,W2)
H1 = graph_attention(X,A,W1,k);
H2 = tanh(H1*W2);
[~,y_pred] = max(H2,[],2);
end
```
请注意,这只是一个简单的实现,可能需要更多的调整和优化才能达到最佳性能。
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