MATLAB: How to handle labels using fileDatastore in a CNN

cnnfiledatastoreinputlayerlabelstrainnetwrok

I have a collection of 50x1x12 mat files, that I need to upload into some datastore to subsequently pass into a convolutional neural network, how can I handle the labels of this files?, what datastore should I use?,

I have been trying with file datastore, the filedatastore does not handle the labels in the same way as the imagedatastore does getting them from the folder.

-What datastore can I use

-if I use filedatastore how can I handle the labels after getting them from the foler and in the moment of use net = trainNetwork( ? );

-how the inputlayer should initially set

I really appreacite the help I have been stuck with this for along time.

Best Answer

Hi Mauricio,

You can use two different datastores for reading the .mat files and labels separately. Then, you can use combine on the datastores and feed to the CNN network.

Refer https://in.mathworks.com/matlabcentral/answers/459161-image-regression-using-mat-files-and-a-datastore#answer_405285 answer for a similar approach.

Hope this helps.

Related Solutions

MATLAB: Multi-Input CNN for image classification

In order to train Multiple input networks you have to use a datastore & After referring to Input Arguments & ds of trainNetwork, Multiple-Input and Multiple-Output Networks, Input Datastore for Training, Validation, and Inference, Read One Array at a Time From Large MAT-File the following is one possible way:

Save all the categorical labels in a mat file & While loading the labels in command window it should look like below:(N(number of training samples) = 4000 in the below case)

>> labels = load('labelData.mat')
labels = 
  struct with fields:
    labels: [4000×1 categorical]

Note that the order of the labels should be i.e., sorted in the same order of the files in the imageDatastore used for input1 and input2.

trainpath1 = fullfile("PATH TO INPUT1 IMAGES");
trainpath2 = fullfile("PATH TO INPUT2 IMAGES");
imds1 = imageDatastore(trainpath1, 'IncludeSubfolders',true, 'FileExtensions','.PNG','LabelSource','foldernames');
imds2 = imageDatastore(trainpath2, 'IncludeSubfolders',true, 'FileExtensions','.PNG','LabelSource','foldernames');
labelds = fileDatastore('labelData.mat','ReadFcn',@myReadFcn,'ReadMode','partialfile');
cds = combine(imds1,imds2,labelds);
% Change the image sizes accordingly
imsize1 = [32 80 3];
imsize2 = [48 64 3];
numClasses = 5;
%% Define muliple input network
layers1 = [
    imageInputLayer(imsize1,'Name','input1')  
    convolution2dLayer(3,16,'Padding','same','Name','conv_1')
    reluLayer('Name','relu_1')
    fullyConnectedLayer(10,'Name','fc11')
    additionLayer(2,'Name','add')
    fullyConnectedLayer(numClasses,'Name','fc12')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classOutput')];
lgraph = layerGraph(layers1);
layers2 = [imageInputLayer(imsize2,'Name','input2')
    convolution2dLayer(3,16,'Padding','same','Name','conv_2')
    reluLayer('Name','relu_2')
    fullyConnectedLayer(10,'Name','fc21')];
lgraph = addLayers(lgraph,layers2);
lgraph = connectLayers(lgraph,'fc21','add/in2');
plot(lgraph)
%% Define trainingOptions and also set 'Shuffle' to 'never' for this workaround to work
options = trainingOptions('adam', ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise',...
    'MaxEpochs',3, ...
    'MiniBatchSize',1, ...
    'Verbose',1, ...
    'Plots','training-progress');
net = trainNetwork(cds,lgraph,options);
%% custom read function to be used for the labels fileDatastore 
%to ensure the read(fileDatastore) returns 1 label at a time
function [data,userdata,done] = myReadFcn(filename,userdata)
% If variable list is empty, 
% create list of variables from the file
if isempty(userdata) 
    labels = load(filename);
    userdata = labels.labels;
end
% Load a variable from the list of variables
data = userdata(1);
% Remove the newly-read variable from the list
userdata(1) = []; 
% Move on to the next file if this file is done reading.
done = isempty(userdata); 
end

For testing the network:

testimds1 = imageDatastore(testpath1);
testimds2 = imageDatastore(testpath2);
testcds = combine(testimds1,testimds2);
YPred = predict(net,testcds,'MiniBatchSize',4)

MATLAB: Transform and Combine fileDatastores to Train CNN

Hi, this is my code. I know it is not efficient, but it should work! I hope it can help as a starting point to be optimized.

For simplicity, I added information about category inside each .mat file as string variable. However, I'm sure you can get labels from folder names too.

The key idea is to use fileDatastore for input data and tabularTextDatastore for categorical data. Then you can combine them succesfully.

Something similar was done here: https://it.mathworks.com/matlabcentral/answers/586949-multi-input-imagedatastore .

Regards

clear
close all
clc
%% create dummy inputs and targets (.mat file)
% inputs are images of size 50x1x12 [heigth, width, channels]
% target are type of material: categorical array
mkdir trainingData
cd trainingData
n=10; % number of training cases
for i=1:n
    material=ones(50,1,12)*i;
    
    if i<n/2
        matType='typeA';
    else
        matType='typeB';
    end
    mkdir(matType)
    filename=sprintf('material_%d', i);
    save(fullfile(matType,filename),'material', 'matType')
end
cd ..
%% load data
allData=fileDatastore(fullfile('trainingData'),'ReadFcn',@load,'FileExtensions','.mat', 'IncludeSubfolders', true);
%% create inputs
inputData = transform(allData,@(data) rearrange_input(data)); % extract and rearrange input
%% create targets (can be optimized...)
targetData = transform(allData,@(data) rearrange_target(data));
myLabels=targetData.readall;
writematrix(myLabels,'myLabels.txt');
labelStore = tabularTextDatastore('myLabels.txt','TextscanFormats','%C',"ReadVariableNames",false);
read_size=1; % this line is foundamental... I don't know why...
labelStore.ReadSize = read_size;
labelStoreCell = transform(labelStore,@setcat_and_table_to_cell);
%% combininig
cdsTrain = combine(inputData,labelStoreCell);
%% training
% dummy layers and options
numClasses=2;
layers=[
    imageInputLayer([50 1 12],"Name","imageinput")
    batchNormalizationLayer()
    leakyReluLayer(0.1)
    fullyConnectedLayer(numClasses,'Name','fc')
    softmaxLayer('Name','soft')
    classificationLayer('Name','classification')];
    
options = trainingOptions('adam');
net = trainNetwork(cdsTrain,layers,options);
%% functions
function inputData = rearrange_input(data)
inputData=data.material;
inputData= {inputData};
end
function targetData = rearrange_target(data)
targetData=data.matType;
targetData=categorical(cellstr(targetData));
end
function [dataout] = setcat_and_table_to_cell(datain)
validcats = ["typeA", "typeB"];
datain.(1) = setcats(datain.(1),validcats);
dataout = table2cell(datain);
end

Best Answer

Related Solutions

MATLAB: Multi-Input CNN for image classification

MATLAB: Transform and Combine fileDatastores to Train CNN

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