cnn-bilstm-attentionmatlab实现

以下是一个简单的 MATLAB 实现，使用了 CNN-BiLSTM-Attention 模型：

1. 加载数据集

在这个例子中，我们将使用一个简单的情感分析数据集。首先，我们需要加载数据集并将其分成训练集和测试集。

% 加载数据集
load('sentiment_dataset.mat');

% 将数据集分成训练集和测试集
XTrain = reviews(1:8000);
YTrain = labels(1:8000);
XTest = reviews(8001:end);
YTest = labels(8001:end);

2. 数据预处理

我们需要对文本进行预处理，包括分词、去除停用词、将文本转换为数字等。

% 分词
XTrain = lower(tokenizedDocument(XTrain));
XTest = lower(tokenizedDocument(XTest));

% 去除停用词
stopWords = stopwords('english');
XTrain = removeWords(XTrain, stopWords);
XTest = removeWords(XTest, stopWords);

% 将文本转换为数字
numWords = 10000;
words = unique([XTrain; XTest]);
[wordCounts, idx] = sort(sum(ismember(words, [XTrain; XTest])), 'descend');
words = words(idx(1:numWords));
XTrain = doc2sequence(XTrain, 'Words', words);
XTest = doc2sequence(XTest, 'Words', words);

3. 创建 CNN 层

我们将使用一个具有多个卷积层和池化层的 CNN。每个卷积层都有一个 ReLU 激活函数和一个最大池化层。我们还将使用批量归一化来加速训练。

% 创建 CNN 层
inputSize = numWords;
embeddingSize = 100;
numFilters = 128;
filterSizes = [3 4 5];
dropoutRate = 0.5;

input = sequenceInputLayer(inputSize);
embedding = wordEmbeddingLayer(words, embeddingSize);
conv1 = convolution2dLayer([filterSizes(1) embeddingSize], numFilters, 'Padding', 1);
conv2 = convolution2dLayer([filterSizes(2) embeddingSize], numFilters, 'Padding', 1);
conv3 = convolution2dLayer([filterSizes(3) embeddingSize], numFilters, 'Padding', 1);
relu1 = reluLayer();
relu2 = reluLayer();
relu3 = reluLayer();
pool1 = maxPooling2dLayer([filterSizes(1) 1], 'Stride', [2 1]);
pool2 = maxPooling2dLayer([filterSizes(2) 1], 'Stride', [2 1]);
pool3 = maxPooling2dLayer([filterSizes(3) 1], 'Stride', [2 1]);
batchNorm1 = batchNormalizationLayer();
batchNorm2 = batchNormalizationLayer();
batchNorm3 = batchNormalizationLayer();
dropout = dropoutLayer(dropoutRate);

% 连接 CNN 层
sequence = input;
sequence = embedding(sequence);
sequence = sequenceLength(sequence, 'OutputMode', 'last');
sequence = reshape(sequence, 1, 1, []);
sequence = conv1(sequence);
sequence = batchNorm1(sequence);
sequence = relu1(sequence);
sequence = pool1(sequence);
sequence = conv2(sequence);
sequence = batchNorm2(sequence);
sequence = relu2(sequence);
sequence = pool2(sequence);
sequence = conv3(sequence);
sequence = batchNorm3(sequence);
sequence = relu3(sequence);
sequence = pool3(sequence);
sequence = dropout(sequence);

4. 创建 BiLSTM 层

接下来，我们将添加一个双向 LSTM 层。

% 创建 BiLSTM 层
hiddenSize = 128;
numClasses = 2;
lstm = bilstmLayer(hiddenSize, 'OutputMode', 'last');

% 连接 BiLSTM 层
sequence = lstm(sequence);

5. 创建 Attention 层

最后，我们将使用一个注意力层来加权输入序列的不同部分。

% 创建 Attention 层
attention = attentionLayer();

% 连接 Attention 层
sequence = attention(sequence);

6. 创建输出层

最后，我们将添加一个全连接层和一个 softmax 层来生成分类输出。

% 创建输出层
fc = fullyConnectedLayer(numClasses);
softmax = softmaxLayer();
classificationLayer = classificationLayer();

% 连接输出层
sequence = fc(sequence);
sequence = softmax(sequence);
sequence = classificationLayer(sequence);

7. 训练模型并评估性能

最后，我们将训练模型并评估其在测试集上的性能。

% 定义训练选项
options = trainingOptions('adam', ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 128, ...
    'InitialLearnRate', 0.001, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 训练模型
net = trainNetwork(XTrain, YTrain, sequence, options);

% 在测试集上评估模型性能
YPred = classify(net, XTest);
accuracy = mean(YPred == YTest);
fprintf('Test accuracy: %0.2f%%\n', accuracy * 100);