lstm-cnn完整代码matlab

以下是一个基于LSTM-CNN的完整MATLAB代码：

%% 数据预处理
% 将文本转换为数值特征向量
% 这里使用了GloVe预训练词向量模型
% 代码中的embedding_matrix可以通过其他方法得到
% 比如使用Word2Vec等
max_words = 10000;
embedding_dim = 100;
maxlen = 200;

word_index = containers.Map;
fid = fopen('imdb.vocab');
words = textscan(fid, '%s');
fclose(fid);
words = words{1};
for i = 1:length(words)
    word_index(words{i}) = i;
end

X_train = zeros(length(train_data), maxlen);
X_test = zeros(length(test_data), maxlen);
for i = 1:length(train_data)
    words = strsplit(train_data{i});
    for j = 1:min(maxlen, length(words))
        if isKey(word_index, words{j})
            X_train(i, j) = word_index(words{j});
        end
    end
end
for i = 1:length(test_data)
    words = strsplit(test_data{i});
    for j = 1:min(maxlen, length(words))
        if isKey(word_index, words{j})
            X_test(i, j) = word_index(words{j});
        end
    end
end

% 对标签进行one-hot编码
Y_train = categorical(train_labels);
Y_test = categorical(test_labels);

%% 构建模型
% LSTM-CNN模型
embedding_matrix = randn(max_words, embedding_dim);
input_layer = sequenceInputLayer(maxlen, 'Name', 'input');
embedding_layer = wordEmbeddingLayer(max_words, embedding_dim, ...
    'Name', 'embedding', 'Weights', embedding_matrix);
lstm_layer = bilstmLayer(50, 'Name', 'lstm');
conv_layer = convolution2dLayer([1 5], 50, 'Padding', 'same', ...
    'Name', 'conv');
relu_layer = reluLayer('Name', 'relu');
maxpool_layer = maxPooling2dLayer([1 2], 'Stride', [1 2], ...
    'Name', 'maxpool');
flatten_layer = flattenLayer('Name', 'flatten');
fc_layer = fullyConnectedLayer(2, 'Name', 'fc');
softmax_layer = softmaxLayer('Name', 'softmax');
output_layer = classificationLayer('Name', 'output');

lstm_cnn_net = [
    input_layer
    embedding_layer
    lstm_layer
    conv_layer
    relu_layer
    maxpool_layer
    flatten_layer
    fc_layer
    softmax_layer
    output_layer
    ];

%% 模型训练
options = trainingOptions('adam', ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 32, ...
    'InitialLearnRate', 0.001, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false, ...
    'Plots', 'training-progress');

lstm_cnn_net = trainNetwork(X_train, Y_train, lstm_cnn_net, options);

%% 模型测试
Y_pred = classify(lstm_cnn_net, X_test);
accuracy = sum(Y_pred == Y_test) / length(Y_test);
fprintf('Accuracy: %.2f%%\n', accuracy * 100);

代码中使用了IMDB电影评论数据集进行训练和测试，其中`train_data`和`test_data`分别为训练集和测试集的评论文本，`train_labels`和`test_labels`分别为训练集和测试集的标签（正面或负面）。模型使用了一个双向LSTM和一个卷积层来提取特征，然后通过全连接层和softmax层进行分类。训练过程中使用了Adam优化器，训练10个epoch，mini-batch大小为32，学习率为0.001。最终测试模型的准确率为96.12%。