假设对LSTM进行改进,增加一个记忆门来控制信息的保留和遗忘,使用matlab语言,编写改进的LSTM神经网络代码
时间: 2023-11-30 08:17:41 浏览: 26
以下是使用MATLAB编写的改进LSTM神经网络代码,其中增加了一个记忆门:
```matlab
classdef lstmMemoryGate < nnet.layer.Layer
properties
HiddenSize % 隐藏层大小
NumInputs % 输入数量
end
properties (Learnable)
% 权重和偏置变量
InputWeights
InputBias
ForgetWeights
ForgetBias
MemoryWeights
MemoryBias
OutputWeights
OutputBias
end
methods
function layer = lstmMemoryGate(hiddenSize, numInputs, name)
% 创建LSTM层
layer.HiddenSize = hiddenSize;
layer.NumInputs = numInputs;
layer.Name = name;
% 初始化权重和偏置变量
[layer.InputWeights, layer.ForgetWeights, layer.MemoryWeights, layer.OutputWeights] = ...
nnet.internal.cnn.layer.util.createInternalWeights4Lstm(hiddenSize, numInputs);
[layer.InputBias, layer.ForgetBias, layer.MemoryBias, layer.OutputBias] = ...
nnet.internal.cnn.layer.util.createInternalBias4Lstm(hiddenSize);
end
function Z = predict(layer, X, H, C)
% 计算LSTM层输出
[input, forget, memory, output] = layer.gates(X, H, C);
% 计算记忆门
memoryGate = tanh(layer.MemoryWeights*X + layer.MemoryWeights*H + layer.MemoryBias);
% 将门控输出加权求和
Z = memoryGate.*memory + input.*tanh(layer.InputWeights*X + layer.InputWeights*H + layer.InputBias) + ...
forget.*C + output.*tanh(layer.OutputWeights*X + layer.OutputWeights*H + layer.OutputBias);
end
function [dX, dH, dC, dInputWeights, dInputBias, dForgetWeights, dForgetBias, ...
dMemoryWeights, dMemoryBias, dOutputWeights, dOutputBias] = ...
backward(layer, X, H, C, ~, dZ, ~)
% 反向传播计算梯度
[input, forget, memory, output] = layer.gates(X, H, C);
memoryGate = tanh(layer.MemoryWeights*X + layer.MemoryWeights*H + layer.MemoryBias);
dMemoryGate = dZ.*memory;
dMemory = dZ.*memoryGate;
dInput = dZ.*input.*(1 - tanh(layer.InputWeights*X + layer.InputWeights*H + layer.InputBias).^2);
dForget = dZ.*C.*forget.*(1 - forget);
dOutput = dZ.*tanh(layer.OutputWeights*X + layer.OutputWeights*H + layer.OutputBias).*...
(1 - tanh(layer.OutputWeights*X + layer.OutputWeights*H + layer.OutputBias).^2);
dH = dInput'*layer.InputWeights + dForget'*layer.ForgetWeights + dMemory'*layer.MemoryWeights + ...
dOutput'*layer.OutputWeights;
dX = dInput'*layer.InputWeights' + dForget'*layer.ForgetWeights' + dMemory'*layer.MemoryWeights' + ...
dOutput'*layer.OutputWeights';
dC = dZ.*forget + dMemoryGate.*(1 - memoryGate.^2).*C;
dInputWeights = dInput'*[X H];
dInputBias = sum(dInput, 1);
dForgetWeights = dForget'*[X H];
dForgetBias = sum(dForget, 1);
dMemoryWeights = dMemory'*[X H];
dMemoryBias = sum(dMemoryGate.*(1 - memoryGate.^2).*dZ, 1);
dOutputWeights = dOutput'*[X H];
dOutputBias = sum(dOutput, 1);
end
function [input, forget, memory, output] = gates(layer, X, H, C)
% 计算输入门、遗忘门、记忆门和输出门
input = sigmoid(layer.InputWeights*X + layer.InputWeights*H + layer.InputBias);
forget = sigmoid(layer.ForgetWeights*X + layer.ForgetWeights*H + layer.ForgetBias);
memory = tanh(layer.MemoryWeights*X + layer.MemoryWeights*H + layer.MemoryBias);
output = sigmoid(layer.OutputWeights*X + layer.OutputWeights*H + layer.OutputBias);
end
end
end
```
以上代码为一个名为`lstmMemoryGate`的LSTM层类,其中增加了一个记忆门,可以通过调用`predict`方法和`backward`方法来进行前向和反向传播计算。