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deep learning_alexnet

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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);

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Accepted Answer

Mohammad Sami
Mohammad Sami on 26 May 2020
Edited: Mohammad Sami on 26 May 2020
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],....

  2 Comments

Srinidhi Gorityala
Srinidhi Gorityala on 26 May 2020
thanks.... it's working.
but i got this error
Error using nnet.cnn.layer.Convolution2DLayer/set.Weights (line 250)
Expected input to be of size 11x11x3x96, but it is of size 11x11x1x96.
Error in convolution2dLayer (line 148)
layer.Weights = args.Weights;
Error in Untitled1 (line 27)
newConv1 = convolution2dLayer(net.Layers(2).FilterSize(1), net.Layers(2).NumFilters,...
Mohammad Sami
Mohammad Sami on 26 May 2020
This is because you are assigning weight for single channel only.
You can remove the following line.
newConv1_Weights = mean(newConv1_Weights(:,:,1:3,:), 3); % taking the mean of kernal channels

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