有没有matlab代码

以下是一个简单的使用MATLAB实现CNN-LSTM-Attention模型的代码示例：

% 定义模型参数
max_sentence_length = 100;
max_word_length = 20;
vocab_size = 10000;
embedding_size = 100;
num_filters = 64;
filter_sizes = [3, 4, 5];
hidden_size = 128;
dropout_rate = 0.5;

% 定义模型输入层
sentence_input = sequenceInputLayer(max_sentence_length, 'Name', 'sentence_input');
word_input = sequenceInputLayer([max_word_length, max_sentence_length], 'Name', 'word_input');

% 定义嵌入层
embedding_layer = wordEmbeddingLayer(vocab_size, embedding_size, 'Name', 'embedding_layer');

% 对句子进行嵌入
embedded_sentence = embedding_layer(sentence_input);

% 对每个单词进行嵌入
embedded_word = sequenceTimeDistributedLayer(embedding_layer, 'Name', 'embedded_word')(word_input);

% 定义卷积层和池化层
conv_outputs = [];
for filter_size = filter_sizes
    conv_layer = convolution1dLayer(filter_size, num_filters, 'Padding', 'same', 'Name', ['conv_', num2str(filter_size)]);
    relu_layer = reluLayer('Name', ['relu_', num2str(filter_size)]);
    pool_layer = maxPooling1dLayer(max_word_length - filter_size + 1, 'Name', ['pool_', num2str(filter_size)]);
    conv_outputs = [conv_outputs, {conv_layer, relu_layer, pool_layer}];
end

% 将卷积输出连接起来
concatenated_layer = concatenationLayer(3, 'Name', 'concatenated_layer');
h_pool = dropoutLayer(dropout_rate, 'Name', 'dropout_layer')(concatenated_layer(conv_outputs{:}));

% 将卷积输出转换为LSTM的输入形式
h_pool = reshapeLayer([-1, num_filters * length(filter_sizes)], 'Name', 'reshape_layer')(h_pool);

% 定义LSTM层
lstm_layer = lstmLayer(hidden_size, 'OutputMode', 'sequence', 'Name', 'lstm_layer')(h_pool);

% 定义注意力层
attention_weights = fullyConnectedLayer(hidden_size, 'Name', 'dense_layer_1')(lstm_layer);
attention_weights = tanhLayer('Name', 'tanh_layer')(attention_weights);
attention_weights = dotProductLayer(2, 'Name', 'dot_layer')({attention_weights, lstm_layer});
attention_weights = softmaxLayer('Name', 'softmax_layer')(attention_weights);
context_vector = dotProductLayer(2, 'Name', 'dot_layer_2')({attention_weights, lstm_layer});

% 将LSTM和注意力层的输出连接起来
lstm_attention = concatenationLayer(2, 'Name', 'concatenated_layer_2')([context_vector, lstm_layer]);

% 定义输出层
output_layer = sequenceFoldingLayer('Name', 'sequence_folding_layer')(lstm_attention);
output_layer = fullyConnectedLayer(1, 'Name', 'dense_layer_2')(output_layer);
output_layer = sigmoidLayer('Name', 'sigmoid_layer')(output_layer);

% 定义模型
model = sequenceFusionLayer('Name', 'model')([sentence_input, word_input]);
model = addLayers(model, embedding_layer, embedded_word, conv_outputs{:}, concatenated_layer, h_pool, lstm_layer, attention_weights, context_vector, lstm_attention, output_layer);

% 连接模型
model = connectLayers(model, 'sentence_input', 'embedding_layer');
model = connectLayers(model, 'word_input', 'embedded_word');
for i = 1:length(filter_sizes)
    model = connectLayers(model, ['embedded_word', num2str(i)], ['conv_', num2str(filter_sizes(i))]);
    model = connectLayers(model, ['conv_', num2str(filter_sizes(i))], ['relu_', num2str(filter_sizes(i))]);
    model = connectLayers(model, ['relu_', num2str(filter_sizes(i))], ['pool_', num2str(filter_sizes(i))]);
end
model = connectLayers(model, 'dropout_layer', 'reshape_layer');
model = connectLayers(model, 'reshape_layer', 'lstm_layer');
model = connectLayers(model, 'lstm_layer', 'dense_layer_1');
model = connectLayers(model, 'dense_layer_1', 'tanh_layer');
model = connectLayers(model, 'tanh_layer', 'dot_layer');
model = connectLayers(model, 'dot_layer', 'softmax_layer');
model = connectLayers(model, 'softmax_layer', 'dot_layer_2');
model = connectLayers(model, 'dot_layer_2', 'concatenated_layer_2');
model = connectLayers(model, 'concatenated_layer_2', 'sequence_folding_layer');
model = connectLayers(model, 'sequence_folding_layer', 'dense_layer_2');
model = connectLayers(model, 'dense_layer_2', 'sigmoid_layer');

% 训练模型
options = trainingOptions('adam', ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 32, ...
    'Verbose', true, ...
    'Plots', 'training-progress');
x_train = {randi([1, vocab_size], [max_sentence_length, 1]), randi([1, vocab_size], [max_word_length, max_sentence_length, 1])};
y_train = randi([0, 1], [1, 1]);
model = trainNetwork(x_train, y_train, model, options);

以上代码是一个简单的CNN-LSTM-Attention模型的MATLAB实现，可以根据实际需求进行修改和扩展。