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⛄ 内容介绍
一种基于长短时记忆网络和卷积神经网络的文本分类方法,首先,利用词向量将输入文本进行向量表示,通过三层CNN提取文本的局部特征,进而整合出全文语义,同时,使用LSTM存储文本序列中历史信息的特征,以获取文本的上下文依赖关系,其次,将输入向量分别与各层CNN的输出相融合,缓解深层神经网络中层与层之间特征传递时出现的特征丢失问题.本发明适用于文本分类,具有运行效率高,运行时间短的优点,解决了传统分类技术在处理复杂抽象和上下文的强相关性文本时的缺陷.
⛄ 部分代码
% Max_iter: Maximum iterations, N: Populatoin size, Convergence_curve: Convergence curve
rand('seed',sum(100 * clock)); % Random number seed
z = 0.03; % Adjustable parameter
% Initialize the position of slime mould
lb = ones(1,dim).*lb; % Lower boundary
ub = ones(1,dim).*ub; % Upper boundary
X = initialization(N,dim,ub,lb); % It can be downloaded from https:///Shihong-Yin
bestPositions = zeros(1,dim); % Optimal food source location
Destination_fitness = inf; % Change this to -inf for maximization problems
weight = ones(N,dim); % Fitness weight of each slime mould
Convergence_curve = zeros(1,Max_iter);
% Main loop
for it = 1:Max_iter
% Check the boundary and calculate the fitness
FU = X>ub; FL = X<lb; X = (X.*(~(FU+FL)))+ub.*FU+lb.*FL;
PopFitness = cec21_bias_shift_rot_func(X',func_num)'; % https:///Shihong-Yin
% Sort the fitness thus update the bF and wF
[SmellOrder,SmellIndex] = sort(PopFitness); % Eq.(2.6)
bestFitness = SmellOrder(1);
worstFitness = SmellOrder(N);
S = bestFitness-worstFitness+eps; % Plus eps to avoid denominator zero
% Calculate the fitness weight of each slime mould
for i = 1:N
if i <= N/2 % Eq.(2.5)
weight(SmellIndex(i),:) = 1+rand(1,dim)*log10((bestFitness-SmellOrder(i))/S+1);
else
weight(SmellIndex(i),:) = 1-rand(1,dim)*log10((bestFitness-SmellOrder(i))/S+1);
end
end
% Update the best position and destination fitness
if bestFitness < Destination_fitness
bestPositions = X(SmellIndex(1),:);
Destination_fitness = bestFitness;
end
a = atanh(-(it/Max_iter)+1); % Eq.(2.4)
vb = unifrnd(-a,a,N,dim); % Eq.(2.3)
b = 1-it/Max_iter;
vc = unifrnd(-b,b,N,dim);
p = tanh(abs(PopFitness-Destination_fitness)); % Eq.(2.2)
r = rand(N,dim);
A = randi([1,N],N,dim); % Two positions randomly selected from population
B = randi([1,N],N,dim);
% Update the Position of search agents
for i = 1:N
if rand < z % Eq.(2.7)
X(i,:) = (ub-lb).*rand(1,dim)+lb; % The original code is (ub-lb)*rand+lb;
else
for j = 1:dim
if r(i,j) < p(i) % Eq.(2.1)
X(i,j) = bestPositions(j)+vb(i,j)*(weight(i,j)*X(A(i,j),j)-X(B(i,j),j));
else
X(i,j) = vc(i,j)*X(i,j);
end
end
end
end
Convergence_curve(it) = Destination_fitness;
end
end
⛄ 运行结果
⛄ 参考文献
[1]尹梓诺, 马海龙, 胡涛. 基于联合注意力机制和一维卷积神经网络-双向长短期记忆网络模型的流量异常检测方法[J]. 电子与信息学报, 2022, 44:1-10.
[2]陈悦, 杨柳, 李帅,等. 基于Softmax函数增强卷积神经网络—双向长短期记忆网络框架的交通拥堵预测算法[J]. 科学技术与工程, 2022, 22(29):10.
