基于匹配网络的one-shot样本分类matlab代码
时间: 2023-08-13 12:05:56 浏览: 45
以下是基于匹配网络的One-shot样本分类的MATLAB代码示例:
```
% 加载数据集
load('omniglot.mat');
% 设定超参数
num_classes = 5; % 类别数量
num_samples = 1; % 每个类别的样本数量
num_epochs = 10; % 训练轮数
learning_rate = 0.001; % 学习率
% 初始化模型参数
input_shape = size(X_train{1});
filters = [32, 64, 128, 256];
kernel_sizes = [3, 3, 3, 3];
strides = [1, 2, 2, 2];
pool_sizes = [2, 2, 2, 2];
fc_size = 512;
weights = init_weights(input_shape, filters, kernel_sizes, strides, pool_sizes, fc_size);
% 训练模型
for epoch = 1:num_epochs
for i = 1:size(X_train, 2)
% 从训练集中选择一个类别
class_idx = randi(num_classes);
class_samples = X_train{class_idx};
% 从该类别中选择两个样本
sample_idxs = randperm(size(class_samples, 2), num_samples+1);
support_set = class_samples(:, sample_idxs(1:end-1));
query = class_samples(:, sample_idxs(end));
% 计算支持集和查询样本的嵌入向量
support_set_embed = forward_pass(support_set, weights);
query_embed = forward_pass(query, weights);
% 计算支持集和查询样本之间的相似度
similarities = compute_cosine_similarity(query_embed, support_set_embed);
% 计算损失并进行反向传播
loss = compute_loss(similarities);
gradients = backward_pass(similarities);
weights = update_weights(weights, gradients, learning_rate);
end
fprintf('Epoch %d: Loss = %f\n', epoch, loss);
end
% 测试模型
num_correct = 0;
for i = 1:size(X_test, 2)
class_idx = randi(num_classes);
class_samples = X_test{class_idx};
sample_idxs = randperm(size(class_samples, 2), num_samples+1);
support_set = class_samples(:, sample_idxs(1:end-1));
query = class_samples(:, sample_idxs(end));
support_set_embed = forward_pass(support_set, weights);
query_embed = forward_pass(query, weights);
similarities = compute_cosine_similarity(query_embed, support_set_embed);
if similarities(1) == max(similarities)
num_correct = num_correct + 1;
end
end
accuracy = num_correct / size(X_test, 2);
fprintf('Accuracy: %f\n', accuracy);
```
这里的`init_weights`函数用于初始化模型参数,`forward_pass`函数用于计算嵌入向量,`compute_cosine_similarity`函数用于计算相似度,`compute_loss`函数用于计算损失,`backward_pass`函数用于反向传播计算梯度,`update_weights`函数用于更新参数。在这个示例中,我们使用了Omniglot数据集进行训练和测试,其中每个类别只有一个样本。