GRU神经网络matlab程序

以下是一个简单的GRU神经网络的MATLAB实现示例：

% GRU神经网络实现
% 参考：https://github.com/crazydonkey200/neural-networks-for-nlp/blob/master/gru.m

classdef GRUNetwork < handle
    properties
        input_dim
        hidden_dim
        output_dim
        Wz
        Wr
        Wh
        Uz
        Ur
        Uh
        bz
        br
        bh
    end

    methods
        function obj = GRUNetwork(input_dim, hidden_dim, output_dim)
            obj.input_dim = input_dim;
            obj.hidden_dim = hidden_dim;
            obj.output_dim = output_dim;

            obj.Wz = rand(hidden_dim, input_dim);
            obj.Wr = rand(hidden_dim, input_dim);
            obj.Wh = rand(hidden_dim, input_dim);
            obj.Uz = rand(hidden_dim, hidden_dim);
            obj.Ur = rand(hidden_dim, hidden_dim);
            obj.Uh = rand(hidden_dim, hidden_dim);
            obj.bz = zeros(hidden_dim, 1);
            obj.br = zeros(hidden_dim, 1);
            obj.bh = zeros(hidden_dim, 1);
        end

        function [h, y] = forward(obj, x)
            T = size(x, 2);
            h = zeros(obj.hidden_dim, T);
            z = zeros(obj.hidden_dim, T);
            r = zeros(obj.hidden_dim, T);
            y = zeros(obj.output_dim, T);

            h(:, 1) = zeros(obj.hidden_dim, 1);

            for t = 1:T
                z(:, t) = sigmoid(obj.Wz * x(:, t) + obj.Uz * h(:, t) + obj.bz);
                r(:, t) = sigmoid(obj.Wr * x(:, t) + obj.Ur * h(:, t) + obj.br);
                h_tilde = tanh(obj.Wh * x(:, t) + obj.Uh * (r(:, t) .* h(:, t)) + obj.bh);
                h(:, t + 1) = (1 - z(:, t)) .* h(:, t) + z(:, t) .* h_tilde;
                y(:, t) = softmax(h(:, t + 1));
            end

            h = h(:, 2:end);
        end

        function [dWz, dWr, dWh, dUz, dUr, dUh, dbz, dbr, dbh] = backward(obj, x, y_true, y_pred, h)
            T = size(x, 2);
            dWz = zeros(size(obj.Wz));
            dWr = zeros(size(obj.Wr));
            dWh = zeros(size(obj.Wh));
            dUz = zeros(size(obj.Uz));
            dUr = zeros(size(obj.Ur));
            dUh = zeros(size(obj.Uh));
            dbz = zeros(size(obj.bz));
            dbr = zeros(size(obj.br));
            dbh = zeros(size(obj.bh));

            dz = zeros(obj.hidden_dim, T);
            dr = zeros(obj.hidden_dim, T);
            dh_tilde = zeros(obj.hidden_dim, T);
            dh = zeros(obj.hidden_dim, T + 1);
            dy = y_pred - y_true;

            dh(:, end) = zeros(obj.hidden_dim, 1);

            for t = T:-1:1
                dh(:, t) = (1 - z(:, t)) .* dh(:, t + 1);
                dh_tilde(:, t) = z(:, t) .* dy(:, t) .* (1 - tanh(obj.Wh * x(:, t) + obj.Uh * (r(:, t) .* h(:, t)) + obj.bh) .^ 2);
                dz(:, t) = (h_tilde(:, t) - h(:, t)) .* h(:, t + 1) .* z(:, t) .* (1 - z(:, t));
                dr(:, t) = sum(obj.Uh' * dh_tilde(:, t) .* h(:, t + 1) .* z(:, t) .* (1 - r(:, t)), 2);
                dh(:, t) = dh(:, t) + obj.Uh' * dh_tilde(:, t) .* r(:, t) .* (1 - h(:, t + 1) .^ 2);
                dbz = dbz + dz(:, t);
                dbr = dbr + dr(:, t);
                dbh = dbh + sum(dh_tilde(:, t), 2);
                dWz = dWz + dz(:, t) * x(:, t)';
                dWr = dWr + dr(:, t) * x(:, t)';
                dWh = dWh + dh_tilde(:, t) * (r(:, t) .* h(:, t))';
                dUz = dUz + dz(:, t) * h(:, t)';
                dUr = dUr + dr(:, t) * h(:, t)';
                dUh = dUh + dh_tilde(:, t) * (r(:, t) .* h(:, t - 1))';
            end
        end

        function update(obj, dWz, dWr, dWh, dUz, dUr, dUh, dbz, dbr, dbh, lr)
            obj.Wz = obj.Wz - lr * dWz;
            obj.Wr = obj.Wr - lr * dWr;
            obj.Wh = obj.Wh - lr * dWh;
            obj.Uz = obj.Uz - lr * dUz;
            obj.Ur = obj.Ur - lr * dUr;
            obj.Uh = obj.Uh - lr * dUh;
            obj.bz = obj.bz - lr * dbz;
            obj.br = obj.br - lr * dbr;
            obj.bh = obj.bh - lr * dbh;
        end
    end
end

function sigm = sigmoid(x)
    sigm = 1 ./ (1 + exp(-x));
end

function sm = softmax(x)
    ex = exp(x);
    sm = ex / sum(ex);
end

这个实现包括前向和后向传播以及模型参数的更新。你可以使用`GRUNetwork`类来实例化GRU网络并进行训练和测试。