Invalid training data. Sequence responses must have the same sequence length as the corresponding predictors.

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Igor Lisogurski 2021-12-29

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评论： yanqi liu 2022-1-26

Hi , I have a simple regression NN for time sequences .

Got an error when training the network : "Invalid training data. Sequence responses must have the same sequence length as the corresponding predictors. " . While my XTrain data is a cell array dim(Nx1) of sequences dim(1x10) and YTrain data is cell array dim(Nx1) of sequences sim(1x1) ...Please advise why the response sequences need to be the same length as the predictors ?

Architecture of the network :

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Answer 1

yanqi liu 2021-12-30

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yes，sir，may be set sequenceInputLayer to

sequenceInputLayer(10, ……)

to match the input data，1*10 dimension

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yanqi liu 2022-1-3

在 MATLAB Online 中打开

close all; clc;
%% load data
data=load('tx2rx_connection_rx_pow_minus10dBm__21_12_2021.mat');
x_org=data.in_tx;
y_org=data.tx2rx;
%% normalize data
x=((real(x_org)-mean(real(x_org)))/std(real(x_org))) +1j*(imag(x_org)-mean(imag(x_org)))/std(imag(x_org));
y=((real(y_org)-mean(real(y_org)))/std(real(y_org))) +1j*(imag(y_org)-mean(imag(y_org)))/std(imag(y_org));
%% figure #1 - Input/Output Time Domain
figure
ax1=subplot(2,1,1);plot(real(x));hold on;plot(real(y));hold off;
title('Time Domain - Real Parts');ylabel('[V]');xlabel('[sec]');legend('x','y');
ax2=subplot(2,1,2);plot(imag(x));hold on;plot(imag(y));hold off;
title('Imaginary Parts');xlabel('[sec]');ylabel('[V]');legend('x','y');
linkaxes([ax1,ax2],'xy');
%% figure #2 -Input/Output Frequency Domain
figure
%FFT PSD
% nfft=1024;
% psd_x_real=fftshift(fft(real(x),nfft)).*conj(fftshift(fft(real(x),nfft)))/(length(x)*fs);
% psd_x_imag=fftshift(fft(imag(x),nfft)).*conj(fftshift(fft(imag(x),nfft)))/(length(x)*fs);
% psd_y_real=fftshift(fft(real(y),nfft)).*conj(fftshift(fft(real(y),nfft)))/(length(x)*fs);
% psd_y_imag=fftshift(fft(imag(y),nfft)).*conj(fftshift(fft(imag(y),nfft)))/(length(x)*fs);
%Welch PSD
segment_size=1024;
nfft=2048;
noverlap=segment_size-2;
fs=128e6;
[pwelch_x,f_x]=pwelch(real(x),segment_size,[],nfft,fs,'centered','power');
[pwelch_y,f_y]=pwelch(real(y),segment_size,[],nfft,fs,'centered','power');
ax3=subplot(2,1,1);plot(f_x,10*log10(pwelch_x/1e-3));hold on;plot(f_y,10*log10(pwelch_y/1e-3));hold off;
title('Power Spectrum');xlabel('[Hz]');ylabel('[dBm]');legend('x','y')
ax4=subplot(2,1,2);plot(f_x,10*log10(pwelch_x/1e-3));hold on;plot(f_y,10*log10(pwelch_y/1e-3));hold off
xlabel('[Hz]');ylabel('[dBm]');ylim([-5,15]);legend('x','y')
grid on;
%% NN data preparation %%
train_ratio=0.9;
channel_length=5;
feature_dim=1;
%Divide into train/test arrays
x_train_len=floor(train_ratio*length(x));
y_train_len=floor(train_ratio*length(y));
x_train=[zeros(1,channel_length-1),(x(1:x_train_len))'];
x_test=[zeros(1,channel_length-1),(x(x_train_len+1:end))'];
y_train=y(1:y_train_len);
y_test=y(y_train_len+1:end);
% NN training/validation inputs definition
XTrain=cell(length(y_train),1);
XTest=cell(length(y_test),1);
YTrain=cell(length(y_train),1);
YTest=cell(length(y_test),1);
x_train_sample=zeros(1,2*channel_length);
y_train_sample=zeros(1,1);
XTrain = [];
YTrain = [];
for i=1:length(y_train)
    x_train_sample(1,1:channel_length)=real(x_train(i:i+channel_length-1));
    x_train_sample(1,channel_length+1:end)=imag(x_train(i:i+channel_length-1));
    
    y_train_sample(1,1)=real(y_train(i));
    
    XTrain(:,:,1,i)= x_train_sample(:);
    YTrain(i,1)= y_train_sample;
end
x_test_sample=zeros(1,2*channel_length);
y_test_sample=zeros(1,1);
XTest = [];
YTest = [];
for i=1:length(y_test)
    x_test_sample(1,1:channel_length)=real(x_test(i:i+channel_length-1));
    x_test_sample(1,channel_length+1:end)=imag(x_test(i:i+channel_length-1));
    
    y_test_sample(1,1)=real(y_test(i));
    
    XTest(:,:,1,i)= x_test_sample(:);
    YTest(i,1)= y_test_sample;
end
%% NN Architecture %%
hidden_neuron=8;
layers = [ ...
    imageInputLayer([10 1 1],'Name','Sequence Input Layer')
    convolution2dLayer(3,hidden_neuron,'Padding','same','Name','CNN 2d Layer')
    reluLayer('Name','ReluLayer Hidden Layer')
    fullyConnectedLayer(1,'Name','CNN 2d Output Layer')
    reluLayer('Name','ReluLayer Output Layer')
    regressionLayer('Name','Regression NN')];
lgraph = layerGraph(layers);
analyzeNetwork(lgraph)
epochs=50;
learnign_r=0.0005;
batch_size=128;
drop_out_r=0;
options = trainingOptions('adam', ...
    'MaxEpochs',epochs, ...
    'ValidationData',{XTest,YTest}, ...
    'InitialLearnRate',0.0005, ...
    'MiniBatchSize',32, ...
    'Plots','training-progress',...
    'LearnRateDropFactor',drop_out_r);
%% NN Training %%
[net,info] = trainNetwork(XTrain,YTrain,layers,options);

Igor Lisogurski 2022-1-25

Hi @yanqi liu sorry for late reply ....Thanks a lot , it worked for me , appriciate your help

yanqi liu 2022-1-26

yes，sir，it is my pleasure

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Invalid training data. Sequence responses must have the same sequence length as the corresponding predictors.

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