# How to turn the data in mini batch into a deep learning array

5 views (last 30 days)
Maik Noungoua on 8 Aug 2020
I am using a deep learning network to predict the distanes of the codewords of a for wireless communications systems. I am trying to update the parameters of my network with the adam algorithm.
I need help of how to turn the data inside the mini batch into data for the dl array.
clc
clear all
S=100E3;
SNR=10;
s_1=-1i; % these are used as the labels
s_2=-1;
s_3=1i;
s_4=1;
m=round(rand(1,2*S));
A=reshape(m,2,S); % the 1x2S row vector is transformed into a 2xS matrix A
x = zeros(1,6);
s = 0;
s = s+(sum(abs(A-[0;0]*ones(1,S)))<0.1)*s_1;
s = s+(sum(abs(A-[0;1]*ones(1,S)))<0.1)*s_2;
s = s+(sum(abs(A-[1;1]*ones(1,S)))<0.1)*s_3;
s = s+(sum(abs(A-[1;0]*ones(1,S)))<0.1)*s_4; % label keeps track of what is originally transmitted
s_= reshape(s,2,S/2);
%% Generation of the complex channel
H = (randn(2,S)+1i*randn(2,S))/2; % Normalised 2x2 Rayleigh MIMO fading channel
n=(randn(2,S/2)+1i*randn(2,S/2))/sqrt(2*10^(SNR/10));
for (k=1:S/2)
nchr_deta= [s_1 s_2 s_3 s_4];
H_ = H(:,2*k-1:2*k);
% x(:,k)=H_*s_(:,k); % This line has been added to compute the numerical SNR: 10*log10(var(reshape(x,1,S))/var(reshape(n,1,S))) (not necessary otherwi
y = H_*s_(:,k)+ n(:,k);
y_arrayReal(:,k) = real(y); %real values of y
y_arrayImag(:,k) = imag(y); %imaginary values of y
H_arrayReal(:,k) = real(H_(1,1));
H_arrayImag(:,k) = imag(H_(1,1));
%% x processsing
x(k,1:2) = [y_arrayReal(1,k) y_arrayReal(2,k)];
x(k,3:4) = [y_arrayImag(1,k) y_arrayImag(2,k)];
x(k,5) = H_arrayReal(k);
x(k,6) = H_arrayImag(k);
%Gerenation of the list of radius for the DNN using MMSE
W_MMSE=(H_'*H_+10^(-SNR/10)*eye(2,2))^-1*H_'; % MMSE equalisation matrix
s_hat_MMSE(:,k)=W_MMSE*y;
outDNN(k,:) = s_hat_MMSE(:,k); %output of the DNN
end
out1 = [real(outDNN(:,1)),imag(outDNN(:,1)),real(outDNN(:,2)),imag(outDNN(:,2))]; %outg of the DNN
%% Defining the network
layers = [
imageInputLayer([50000 6],'Name','Input layer','Mean',ones([50000 6]))
convolution2dLayer([10000 6],5,'Name','conv1')
reluLayer('Name', 'relu1')
% convolution2dLayer([10 6], 1,'Name','conv2')
% reluLayer('Name','relu2')
% convolution2dLayer([1000 6],1,'Name','conv3')
% reluLayer('Name','relu3')
fullyConnectedLayer(5,'Name','Output layer');
]
lgraph = layerGraph(layers);
%% Creation of the dl network
dlnet = dlnetwork(lgraph);
%% Specifying the training options
miniBatchSize = 128;
numEpochs = 20;
numObservations = numel(x);
numIterationsPerEpoch = floor(numObservations./miniBatchSize);
iteration = 1;
%% Initialise the training progress plot
plots = "training-progress";
if plots == "training-progress"
figure
lineLossTrain = animatedline;
xlabel("Total Iterations")
ylabel("Loss")
end
%% Train the network while updating its parameters using adamupdate funtion
for epoch = 1:numEpochs
% Convert mini-batch of data to dlarray.
X = dlarray(x(:,:,:,idx),'SSCB');
% Evaluate the model gradients using dlfeval and the
%Update the network using adam optimiser
% Display the training progress.
if plots == "training-progress"
title("Loss During Training: Epoch - " + epoch + "; Iteration - ")
drawnow
end
% Increment the iteration counter.
iteration = iteration + 1;
end

Divya Gaddipati on 11 Aug 2020
dlarray is used to convert the data to deep learning array, which you are already doing here:
X = dlarray(x(:,:,:,idx),'SSCB');