MATLAB: Deep learning_alexnet

alexnetImage Processing Toolbox

Helo, iam working on Alexnet network…. below is the error i have got.could anyone please help me in solving this.
Error using trainNetwork (line 170)
The training images are of size 227x227x3 but the input layer expects images of size 227x227x1.
Error in Untitled1 (line 63)
net = trainNetwork(augimdsTrain,lgraph,options);
clear all;
clc;
close all;
myTrainingFolder = 'C:\Users\Admin\Desktop\Major Project\cnn_dataset';
%testingFolder = 'C:\Users\Be Happy\Documents\MATLAB\gtsrbtest';
imds = imageDatastore(myTrainingFolder,'IncludeSubfolders', true,  'LabelSource', 'foldernames');
[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);

Best Answer

You have specified the image as single channel in your code. just change it to 3 channels (RGB).
 imageInputLayer([imageSize 1],....
to
 imageInputLayer([imageSize 3],....
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