C++에서 감정 합성과 감정 생성을 수행하는 방법은 무엇입니까?

#include  #include  // 情感词典 std::unordered_map sentimentDict = { { "happy", 3 }, { "sad", -2 }, { "angry", -3 }, // 其他情感词汇 }; // 情感合成函数 int sentimentSynthesis(const std::string& text) { int score = 0; // 按单词拆分文本 std::string word; std::stringstream ss(text); while (ss >> word) { if (sentimentDict.find(word) != sentimentDict.end()) { score += sentimentDict[word]; } } return score; } int main() { std::string text = "I feel happy and excited."; int score = sentimentSynthesis(text); std::cout << "Sentiment score: " << score << std::endl; return 0; }

#include  #include  // 循环神经网络模型 struct LSTMModel : torch::nn::Module { LSTMModel(int inputSize, int hiddenSize, int outputSize) : lstm(torch::nn::LSTMOptions(inputSize, hiddenSize).layers(1)), linear(hiddenSize, outputSize) { register_module("lstm", lstm); register_module("linear", linear); } torch::Tensor forward(torch::Tensor input) { auto lstmOut = lstm(input); auto output = linear(std::get<0>(lstmOut)[-1]); return output; } torch::nn::LSTM lstm; torch::nn::Linear linear; }; int main() { torch::manual_seed(1); // 训练数据 std::vector happySeq = { 0, 1, 2, 3 }; // 对应编码 std::vector sadSeq = { 4, 5, 6, 3 }; std::vector angrySeq = { 7, 8, 9, 3 }; std::vector> sequences = { happySeq, sadSeq, angrySeq }; // 情感编码与文本映射 std::unordered_map sentimentDict = { { 0, "I" }, { 1, "feel" }, { 2, "happy" }, { 3, "." }, { 4, "I" }, { 5, "feel" }, { 6, "sad" }, { 7, "I" }, { 8, "feel" }, { 9, "angry" } }; // 构建训练集 std::vector inputs, targets; for (const auto& seq : sequences) { torch::Tensor input = torch::zeros({ seq.size()-1, 1, 1 }); torch::Tensor target = torch::zeros({ seq.size()-1 }); for (size_t i = 0; i < seq.size() - 1; ++i) { input[i][0][0] = seq[i]; target[i] = seq[i + 1]; } inputs.push_back(input); targets.push_back(target); } // 模型参数 int inputSize = 1; int hiddenSize = 16; int outputSize = 10; // 模型 LSTMModel model(inputSize, hiddenSize, outputSize); torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(0.01)); // 训练 for (int epoch = 0; epoch < 100; ++epoch) { for (size_t i = 0; i < inputs.size(); ++i) { torch::Tensor input = inputs[i]; torch::Tensor target = targets[i]; optimizer.zero_grad(); torch::Tensor output = model.forward(input); torch::Tensor loss = torch::nn::functional::nll_loss(torch::log_softmax(output, 1).squeeze(), target); loss.backward(); optimizer.step(); } } // 生成 torch::Tensor input = torch::zeros({ 1, 1, 1 }); input[0][0][0] = 0; // 输入情感：happy std::cout << sentimentDict[0] << " "; for (int i = 1; i < 5; ++i) { torch::Tensor output = model.forward(input); int pred = output.argmax().item(); std::cout << sentimentDict[pred] << " "; input[0][0][0] = pred; } std::cout << std::endl; return 0; }