MATLAB实战利用1D-DCGAN生成光谱或信号数据

0.前言

在光谱学或信号处理领域，获取大量高质量的数据可能是一项挑战。利用DCGAN迁移对抗生成光谱或信号数据，可以有效地增加数据集的多样性，提高模型的泛化能力。

该实战项目提供了所有源代码与测试数据，旨在帮助学者快速地掌握了解利用DCGAN对1维数据的生成。

建议开始本项目前提前了解和掌握以下内容：MATLAB代码解析：利用DCGAN实现图像数据的生成全网最细&DCGAN设计-训练入门

项目参考文献：

光谱技术结合水分校正与样本增广的棉田土壤盐分精准反演 - 中国知网

1.训练效果、脚本与文件

1.1训练效果

训练8个周期

训练36个周期

训练115个周期

充分训练后

1.2脚本代码

1.2.1主程序

数据获取
clear all
clc
load("TestData.mat");
%返回相同的数据类型,以行为分配样本
%'IterationDimension'=1以第一个维度划分样本（行），2为第二个维度，以此类推；
ADataStore = arrayDatastore(DataMat,'IterationDimension',1,'OutputType', 'cell');
%%构建生成器
numLatentInputs=200;%输入随机数大小
netG=Creat_1D_Gener(numLatentInputs);
%%判别器
netD=Creat_1D_Discri();
%%指定训练选项
dropoutProb = 0.25;%泄露率
scale = 0.2;
numEpochs = 2000;%训练周期
miniBatchSize = 256;%最小批次
learnRate = 0.00002;
learnRateD=0.00002;
gradientDecayFactor = 0.45;
squaredGradientDecayFactor = 0.999;
flipProb = 0.15;%翻转概率
validationFrequency = 100;%验证频率
%%训练模型
mbq = minibatchqueue(ADataStore, ...MiniBatchSize=miniBatchSize, ...MiniBatchFcn=@preprocessMiniBatch12_2D, ...%预处理方法，与采集的数据类型对应PartialMiniBatch="discard", ...MiniBatchFormat="SCB");trailingAvgG = [];
trailingAvgSqG = [];
trailingAvg = [];
trailingAvgSqD = [];
numValidationImages = 4;
ZValidation = randn(numLatentInputs,numValidationImages,"single");
ZValidation = dlarray(ZValidation,"CB");
if canUseGPUZValidation = gpuArray(ZValidation);
endf = figure;
f.Position(3) = 2*f.Position(3);imageAxes = subplot(1,2,1);
scoreAxes = subplot(1,2,2);C = colororder;
lineScoreG = animatedline(scoreAxes,Color=C(1,:));
lineScoreD = animatedline(scoreAxes,Color=C(2,:));
legend("Generator","Discriminator");
ylim([0 1])
xlabel("Iteration")
ylabel("Score")
grid oniteration = 0;
start = tic;% Loop over epochs.
for epoch = 1:numEpochs% Reset and shuffle datastore.shuffle(mbq);% Loop over mini-batches.while hasdata(mbq)iteration = iteration + 1;% Read mini-batch of data.X = next(mbq);% Generate latent inputs for the generator network. Convert to% dlarray and specify the format "CB" (channel, batch). If a GPU is% available, then convert latent inputs to gpuArray.Z = randn(numLatentInputs,miniBatchSize,"single");Z = dlarray(Z,"CB");if canUseGPUZ = gpuArray(Z);end% Evaluate the gradients of the loss with respect to the learnable% parameters, the generator state, and the network scores using% dlfeval and the modelLoss function.[~,~,gradientsG,gradientsD,stateG,scoreG,scoreD] = ...dlfeval(@modelLoss,netG,netD,X,Z,flipProb);netG.State = stateG;  %%show data%"epoch"%epoch%"scoreG-D"%[scoreG,scoreD]% Update the discriminator network parameters.[netD,trailingAvg,trailingAvgSqD] = adamupdate(netD, gradientsD, ...trailingAvg, trailingAvgSqD, iteration, ...learnRateD, gradientDecayFactor, squaredGradientDecayFactor);% Update the generator network parameters.[netG,trailingAvgG,trailingAvgSqG] = adamupdate(netG, gradientsG, ...trailingAvgG, trailingAvgSqG, iteration, ...learnRate, gradientDecayFactor, squaredGradientDecayFactor);% Every validationFrequency iterations, display batch of generated% images using the held-out generator input.if mod(iteration,validationFrequency) == 0 || iteration == 1% Generate images using the held-out generator input.XGeneratedValidation = predict(netG,ZValidation);% Tile and rescale the images in the range [0 1].I = imtile(extractdata(XGeneratedValidation));I = rescale(I);% Display the images.subplot(1,2,1);plot(I,'DisplayName','I')% xticklabels([0 900]);% yticklabels([-1 1]);title("Generated Curve");% Update the scores plot.subplot(1,2,2)scoreGV = double(extractdata(scoreG));addpoints(lineScoreG,iteration,scoreGV);scoreDV = double(extractdata(scoreD));addpoints(lineScoreD,iteration,scoreDV);% Update the title with training progress information.D = duration(0,0,toc(start),Format="hh:mm:ss");title(..."Epoch: " + epoch + ", " + ..."Iteration: " + iteration + ", " + ..."Elapsed: " + string(D))drawnow%周期性保存数据if mod(iteration,validationFrequency*100)==0%save GANmodel_test.mat netG netD trailingAvg trailingAvgSqD iterationendendend
end
%%生成新图像  
numLatentInputs = 50;
numObservations = 1;
ZNew = randn(numLatentInputs,numObservations,"single");
ZNew = dlarray(ZNew,"CB");
if canUseGPUZNew = gpuArray(ZNew);
endXGeneratedNew = predict(netG,ZNew);I = imtile(extractdata(XGeneratedNew));
I=rescale(I)
figure
plot(I,'DisplayName','I')
axis off
title("Generated Images")

该代码内容与之前图像生成的思路[1]基本一致，主要变动在于生成器、判别器、数据库构建和数据预处的区别。

[1] https://blog.csdn.net/m0_47787372/article/details/141791275?spm=1001.2014.3001.5501

首先是数据格式的变动，由于1D数据较小，这里直接用内存载入训练。

该程序提供的示例数据为2维的doule类型（每行为1个样本），数据量3658、单样本长度128。这里利用，arrayDatastore函数直接从内存读取该数据作为深度学习的训练样本，'IterationDimension'为样本分割的维数，这里按行分割样本，固设置为1.OutputType设置成same或cell都可以。

ADataStore = arrayDatastore(DataMat,'IterationDimension',1,'OutputType', 'cell');

如果这里不使用arrayDatastore类函数，将无法使用minibatchqueue函数将数据拆分成多个训练批次。后者仅支持datastore数据类型，当然你使用imaginedatastore等其他函数也是可以的（把1维数据变成图像再训练），但这本质上属于还是未对matlab数据类型的实现精准掌握。

1.2.2 生成器


function net=Creat_2D_Gener(numLatentInputs)
net = dlnetwork;Add branches to the dlnetwork. Each branch is a linear array of layers.
tempNet = [featureInputLayer(numLatentInputs,"Name","input")projectAndReshapeLayer([8 2048])transposedConv1dLayer(3,1024,"Name","transposed-conv1d","Cropping","same","Stride",2)instanceNormalizationLayer("Name","instancenorm")reluLayer("Name","relu")transposedConv1dLayer(3,512,"Name","transposed-conv1d_1","Cropping","same","Stride",2)instanceNormalizationLayer("Name","instancenorm_1")reluLayer("Name","relu_1")transposedConv1dLayer(3,256,"Name","transposed-conv1d_2","Cropping","same")instanceNormalizationLayer("Name","instancenorm_2")reluLayer("Name","relu_3")transposedConv1dLayer(3,128,"Name","transposed-conv1d_3","Cropping","same","Stride",2)instanceNormalizationLayer("Name","instancenorm_3")reluLayer("Name","relu_2")transposedConv1dLayer(3,64,"Name","transposed-conv1d_4","Cropping","same","Stride",2)instanceNormalizationLayer("Name","instancenorm_3_1")reluLayer("Name","relu_2_1")transposedConv1dLayer(1,1,"Name","transposed-conv1d_5","Cropping","same")tanhLayer("Name","tanh")];
net = addLayers(net,tempNet);clear tempNet;net = initialize(net);plot(net);end

生成器用的是1维转置卷积层，输入200个随机数，通过自定义的全连接reshape层将数据转化为8*2048的大小。利用Matlab的DL designer工具包进行分析，通过观察Activation属性，我们可以很快的了解数据在生成器中的变化。其中S为空间维度，C为通道维度，B为批次量（根据训练设置变化）。这里的设计的思路就是利用噪声生成一个通道数高的带加工样本，之后每次一维转置卷积中通道维度每减半、空间维度翻倍。最后加工为128*1（C）的1维数据。

其中，rehape自定义层代码如下，和图像生成的项目基本一致，但是输出格式进行了调整（SCB），该层就是将200个输入噪声通过全连接层变为8*2048长度的数据，再reshape成8*2048*1的SCB格式数据。

classdef projectAndReshapeLayer < nnet.layer.Layer ...& nnet.layer.Formattable ...& nnet.layer.Acceleratableproperties% Layer properties.OutputSizeendproperties (Learnable)% Layer learnable parameters.WeightsBiasendmethodsfunction layer = projectAndReshapeLayer(outputSize,NameValueArgs)% layer = projectAndReshapeLayer(outputSize)% creates a projectAndReshapeLayer object that projects and% reshapes the input to the specified output size.%% layer = projectAndReshapeLayer(outputSize,Name=name)% also specifies the layer name.% Parse input arguments.argumentsoutputSizeNameValueArgs.Name = "";end% Set layer name.name = NameValueArgs.Name;layer.Name = name;% Set layer description.layer.Description = "Project and reshape to size " + ...join(string(outputSize));% Set layer type.layer.Type = "Project and Reshape";% Set output size.layer.OutputSize = outputSize;endfunction layer = initialize(layer,layout)% layer = initialize(layer,layout) initializes the layer% learnable parameters.%% Inputs:%         layer  - Layer to initialize%         layout - Data layout, specified as a %                  networkDataLayout object%% Outputs:%         layer - Initialized layer% Layer output size.outputSize = layer.OutputSize;% Initialize fully connect weights.if isempty(layer.Weights)% Find number of channels.idx = finddim(layout,"C");numChannels = layout.Size(idx);% Initialize using Glorot.sz = [prod(outputSize) numChannels];numOut = prod(outputSize);numIn = numChannels;layer.Weights = initializeGlorot(sz,numOut,numIn);end% Initialize fully connect bias.if isempty(layer.Bias)% Initialize with zeros.layer.Bias = initializeZeros([prod(outputSize) 1]);endendfunction Z = predict(layer, X)% Forward input data through the layer at prediction time and% output the result.%% Inputs:%         layer - Layer to forward propagate through%         X     - Input data, specified as a formatted dlarray%                 with a "C" and optionally a "B" dimension.% Outputs:%         Z     - Output of layer forward function returned as%                 a formatted dlarray with format "SSCB".% Fully connect.weights = layer.Weights;bias = layer.Bias;X = fullyconnect(X,weights,bias);% Reshape.outputSize = layer.OutputSize;Z = reshape(X,outputSize(1),outputSize(2),[]);Z = dlarray(Z,"SCB");endend
end

1.2.3 判别器

判别器基本与生成器对称，用1维卷积核，代码如下：


function net=Creat_1D_Discri(droprate,scale)
net = dlnetwork;tempNet = [inputLayer([128 1 NaN],"SCB","Name","input")dropoutLayer(droprate,"Name","dropout")convolution1dLayer(3,32,"Name","conv1d","Padding","same","Stride",2)leakyReluLayer(scale,"Name","leakyrelu_1")convolution1dLayer(3,64,"Name","conv1d_1","Padding","same","Stride",2)batchNormalizationLayer("Name","batchnorm")leakyReluLayer(scale,"Name","leakyrelu_2")convolution1dLayer(3,128,"Name","conv1d_2","Padding","same","Stride",2)batchNormalizationLayer("Name","batchnorm_1")leakyReluLayer(scale,"Name","leakyrelu_3")convolution1dLayer(3,256,"Name","conv1d_3","Padding","same","Stride",2)batchNormalizationLayer("Name","batchnorm_1_1")leakyReluLayer(scale,"Name","leakyrelu_3_1")convolution1dLayer(3,512,"Name","conv1d_4","Padding","same","Stride",2)batchNormalizationLayer("Name","batchnorm_1_2")leakyReluLayer(scale,"Name","leakyrelu_3_2")convolution1dLayer(5,1,"Name","conv1d_5","Padding","same","Stride",4)sigmoidLayer("Name","layer")];
net = addLayers(net,tempNet);% clean up helper variable
clear tempNet;net = initialize(net);plot(net);

具体参数如下

1.2.3数据预处理函数

预处理函数有比较明显的改动，1个是本次示例数据是int12的格式，所以我们这里归一化范围修改了。其次是样本凭借的维度（批次维度）为3，需要注意。同时我们需要调整数据的维度顺序，因为arraydatastore按第一个维度分割样本后的数据格式为1*128*1（CSB），为了对应深度学习模型的输入维度（SCB），在预处理前利用permute函数进行维度调整。

function X = preprocessMiniBatch12_2D(data)% Concatenate mini-batch
X = cat(3,data{:});%调整矩阵的维度，让数值回到空间通道中，通道在第二位置；
X=permute(X, [2, 1, 3]); 
% Rescale the images in the range [-1 1].
X = rescale(X,-1,1,InputMin=0,InputMax=2^12);end

1.2.4 损失函数

无大变化：

反向传播与梯度计算

function [lossG,lossD,gradientsG,gradientsD,stateG,scoreG,scoreD] = ...modelLoss(netG,netD,X,Z,flipProb)% Calculate the predictions for real data with the discriminator network.
YReal = forward(netD,X);
% Calculate the predictions for generated data with the discriminator
% network.
[XGenerated,stateG] = forward(netG,Z);
YGenerated = forward(netD,XGenerated);% Calculate the score of the discriminator.
scoreD = (mean(YReal) + mean(1-YGenerated)) / 2;% Calculate the score of the generator.
scoreG = mean(YGenerated);% Randomly flip the labels of the real images.
numObservations = size(YReal,4);
idx = rand(1,numObservations) < flipProb;
YReal(:,:,:,idx) = 1 - YReal(:,:,:,idx);% Calculate the GAN loss.
[lossG, lossD] = ganLoss(YReal,YGenerated);% For each network, calculate the gradients with respect to the loss.
gradientsG = dlgradient(lossG,netG.Learnables,RetainData=true);
gradientsD = dlgradient(lossD,netD.Learnables);end

损失函数：

function [lossG,lossD] = ganLoss(YReal,YGenerated)% Calculate the loss for the discriminator network.
lossD = -mean(log(YReal)) - mean(log(1-YGenerated));% Calculate the loss for the generator network.
lossG = -mean(log(YGenerated));end

1.3 资源下载

脚本、函数文件与示例数据如下：

通过网盘分享的文件：24-1D_DCGAN_EXAMPLE.zip
链接: https://pan.baidu.com/s/1kxQ4Q3RCzLeeAgdQBWl9KQ?pwd=3wgy 提取码: 3wgy

2.总结

从信息论的角度进行分析，DCGAN 在基于先验知识（真实样本）的条件下尽可能地生成接近真实的样本，这一过程并不会创造新信息，无法通过对抗过程来增加训练集基本特征的多样性。但对于样本多样性而言，DCGAN具有基于现有特征进行缩放及组合以形成更多具有特异性高级特征的能力，因此它具有强化预测模型的泛化能力的潜力，提高模型对特征挖掘和学习能力，但这也导致DCGAN在过少的训练集（基本特征不足）或足够丰富的训练集（高级特征充足）的场景下，很难带来显著的预测性能效果提升[2].