MATLAB: Unrecognized function or variable when working with pre-trained network

algorithmdeep learningerrorguiimage processingMATLABneural network

what is the reason of that error and how can i fix it.

i loaded network (alexnet) , and exported layer to workspace in matlab but after runing the project, the layers_1 will disappear in workspace!!

thanks for your help.

here is the layers_1 i exported from DeepNetworkDesigner:

after i Run the project, the layers_1 will disappear:

Best Answer

clear all

deletes all the variables loaded in workspace. It is also unrecommended to add this at top of every script. Remove it to avod deleting any varibles.

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MATLAB: How to fix this issue through running the program

you cannot use pre-trained network unless you adjust it to your data

1. for alexnet, this pre-trained network takes 227x227x3 because it deals with RGB images

2. and that also applies to the first ConveNet which takes 3 channels because its kernels have 3 channels, in which you also have to update

3. you must update the last three classification layers to classify based on your classes

i also think that you are trying to resize your lung dataset to 227x227 in which you may lose some of its quality

this code should work for you, and if it's not clear i can clarify it for you

clear all; close all; clc;
imds = imageDatastore('lung dataset-Labeled', ...
    'IncludeSubfolders',true, 'LabelSource','foldernames', ... % this for labeling by folder names
    'FileExtensions','.dcm','ReadFcn',@readDicomDatastoreImage); % this a function
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);
net = alexnet(); % analyzeNetwork(lgraph)
numClasses = numel(categories(imdsTrain.Labels)); % number of classes = number of folders
imageSize = [227 227]; % you can use here the original dataset size
global GinputSize
GinputSize = imageSize;
lgraph = layerGraph(net.Layers);
lgraph = removeLayers(lgraph, 'fc8');
lgraph = removeLayers(lgraph, 'prob');
lgraph = removeLayers(lgraph, 'output');
% create and add layers
inputLayer = imageInputLayer([imageSize 1], 'Name', net.Layers(1).Name,...
    'DataAugmentation', net.Layers(1).DataAugmentation, ...
    'Normalization', net.Layers(1).Normalization);
lgraph = replaceLayer(lgraph,net.Layers(1).Name,inputLayer);
newConv1_Weights = net.Layers(2).Weights;
newConv1_Weights = mean(newConv1_Weights(:,:,1:3,:), 3); % taking the mean of kernal channels
newConv1 = convolution2dLayer(net.Layers(2).FilterSize(1), net.Layers(2).NumFilters,...
    'Name', net.Layers(2).Name,...
    'NumChannels', inputLayer.InputSize(3),...
    'Stride', net.Layers(2).Stride,...
    'DilationFactor', net.Layers(2).DilationFactor,...
    'Padding', net.Layers(2).PaddingSize,...
    'Weights', newConv1_Weights,...BiasLearnRateFactor 
    'Bias', net.Layers(2).Bias,...
    'BiasLearnRateFactor', net.Layers(2).BiasLearnRateFactor);
lgraph = replaceLayer(lgraph,net.Layers(2).Name,newConv1);
lgraph = addLayers(lgraph, fullyConnectedLayer(numClasses,'Name', 'fc2'));
lgraph = addLayers(lgraph, softmaxLayer('Name', 'softmax'));
lgraph = addLayers(lgraph, classificationLayer('Name','output'));
lgraph = connectLayers(lgraph, 'drop7', 'fc2');
lgraph = connectLayers(lgraph, 'fc2', 'softmax');
lgraph = connectLayers(lgraph, 'softmax', 'output');
% -------------------------------------------------------------------------
augmenter = imageDataAugmenter( ...
    'RandRotation',[-20,20], ...
    'RandXReflection',1,...
    'RandYReflection',1,...
    'RandXTranslation',[-3 3], ...
    'RandYTranslation',[-3 3]);
%augimdsTrain = augmentedImageDatastore([224 224],imdsTrain,'DataAugmentation',augmenter);
%augimdsValidation = augmentedImageDatastore([224 224],imdsValidation,'DataAugmentation',augmenter);
augimdsTrain = augmentedImageDatastore(imageSize,imdsTrain);
augimdsValidation = augmentedImageDatastore(imageSize,imdsValidation);
options = trainingOptions('rmsprop', ...
    'MiniBatchSize',10, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-3, ...
    'Shuffle','every-epoch', ...
    'ValidationData',augimdsValidation, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(augimdsTrain,lgraph,options)
[YPred, probs] = classify(net,augimdsValidation);
accuracy = mean(YPred ==imdsValidation.Labels)
figure
cm=confusionchart (imdsValidation.Labels, YPred);

you also need to update the readDicomDatastoreImage function to resize every image you read with specified size:

function I = readDicomDatastoreImage(filename)
    onState = warning('off', 'backtrace');
    c = onCleanup(@() warning(onState));
    I = dicomread(filename);
    global GinputSize;
    I = imresize(I,GinputSize(1:2));

MATLAB: Want to add regularization (L2) in Resnet50 code

You can specify the L2 regularization factors for the weights and biases in Convolutional and Fully Connected Layers by specifying the BiasL2Factor and WeightL2Factor properties, respectively. trainNetwork then multiplies the L2 regularization factors that you specify by using trainingOptions with these factors.

For more informtion refer to setL2Factor, Solver Options, L2 Regularization, Set Up Parameters in Convolutional and Fully Connected Layers.

Best Answer

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MATLAB: How to fix this issue through running the program

MATLAB: Want to add regularization (L2) in Resnet50 code

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