MATLAB: Image Regression using .mat Files and a datastore

deep learningDeep Learning Toolboxneural networkregression

I would like to train a CNN for image regression using a datastore. My images are stored in .mat files (not png or jpeg). This is not image-to-image regression, rather an image to single regression label problem. Is it possible to do this using a datastore, or at least some other out-of-memory approach?

Best Answer

I have solved something similar.

I'm trying to train a CNN for regression. My inputs are numeric matrices of size 32x32x2 (each input includes 2 grayscale images as two channels). My outputs are numeric vectors of length 6.

500 000 is the total amount of data.

I created 500 000 .mat file for inputs in folder 'inputData' and 500 000 .mat file for target in folder 'targetData'. Each .mat file contains only 1 variable of type double called 'C'.

The size of C is 32x32x2 (if input) or 1x6 (if target).

Then, I transformed and combined datastores as here:https://it.mathworks.com/help/deeplearning/examples/train-network-using-out-of-memory-sequence-data.html

inputData=fileDatastore(fullfile('inputData'),'ReadFcn',@load,'FileExtensions','.mat');
targetData=fileDatastore(fullfile('targetData'),'ReadFcn',@load,'FileExtensions','.mat');
inputDatat = transform(inputData,@(data) rearrange_datastore(data));
targetDatat = transform(targetData,@(data) rearrange_datastore(data));
trainData=combine(inputDatat,targetDatat);
% here I defined my network architecture
% here I defined my training options
net=trainNetwork(trainData, Layers, options);
function image = rearrange_datastore(data)
image=data.C;
image= {image};
end

Related Solutions

MATLAB: How to pass images to a cnn for analysis

Hey Teshan,

After you train a neural network for deep learning using trainNetwork into net, follow this procedure:

Use the function augmentedImageDatastore for effective preprocessing of the images
Use the activations function to compute the DL Network layer activations i.e. the image features using the CNN net
Use the predict function to finally make a prediction using the classifier

The overall workflow that I mentioned is available in this example as well.

Hope this helps!

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: How to pass images to a cnn for analysis

MATLAB: Transform and Combine fileDatastores to Train CNN

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