attention-lstm matlab代码

以下是一个简单的 attention-lstm 的 MATLAB 代码示例：

classdef AttentionLSTM < nnet.layer.Layer
    properties
        HiddenSize
        SequenceLength
        NumHiddenUnits
        NumFeatures
        AttentionWeights
        AttentionBias
        LSTMWeights
        LSTMBias
        OutputWeights
        OutputBias
    end
    
    methods
        function layer = AttentionLSTM(hiddenSize, sequenceLength, numHiddenUnits, numFeatures)
            layer.HiddenSize = hiddenSize;
            layer.SequenceLength = sequenceLength;
            layer.NumHiddenUnits = numHiddenUnits;
            layer.NumFeatures = numFeatures;

            % Initialize weights and biases
            layer.AttentionWeights = randn(hiddenSize, numHiddenUnits);
            layer.AttentionBias = zeros(hiddenSize, 1);
            layer.LSTMWeights = randn(4*numHiddenUnits, numFeatures);
            layer.LSTMBias = zeros(4*numHiddenUnits, 1);
            layer.OutputWeights = randn(hiddenSize, numHiddenUnits);
            layer.OutputBias = zeros(hiddenSize, 1);
        end
        
        function Z = predict(layer, X)
            % Initialize hidden and cell state
            h = zeros(layer.NumHiddenUnits, 1);
            c = zeros(layer.NumHiddenUnits, 1);

            % Initialize attention context vector
            a = zeros(layer.HiddenSize, 1);

            % Loop through sequence
            for i = 1:layer.SequenceLength
                % Compute attention weights
                e = layer.AttentionWeights * h + layer.AttentionBias;
                alpha = softmax(e);

                % Compute attention context vector
                a = X(:, i) * alpha';

                % Concatenate input and attention context vector
                z = [X(:, i); a];

                % Compute LSTM gates
                gates = layer.LSTMWeights * z + layer.LSTMBias;
                f = sigmoid(gates(1:layer.NumHiddenUnits));
                i = sigmoid(gates(layer.NumHiddenUnits+1:2*layer.NumHiddenUnits));
                o = sigmoid(gates(2*layer.NumHiddenUnits+1:3*layer.NumHiddenUnits));
                g = tanh(gates(3*layer.NumHiddenUnits+1:end));

                % Update cell state and hidden state
                c = f .* c + i .* g;
                h = o .* tanh(c);
            end

            % Apply output layer
            Z = layer.OutputWeights * h + layer.OutputBias;
        end
    end
end

这个类定义了一个 Attention LSTM 层，它包括一个注意力机制和一个 LSTM 细胞。在预测时，输入的数据是一个大小为 `(numFeatures, sequenceLength)` 的矩阵 `X`。在循环中，我们首先计算注意力权重，然后使用它们计算注意力上下文向量。接下来，我们将输入和注意力上下文向量连接起来，计算 LSTM 门的值，并更新细胞状态和隐藏状态。最后，我们应用输出层并返回输出矩阵 `Z`。