deep learning_alexnet

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);

[Walter Roberson]

User tagged with

https://in.mathworks.com/matlabcentral/answers/513441-how-to-fix-this-issue-through-running-the-program

Answer 1

[Mohammad Sami]

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],....

[Srinidhi Gorityala]

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]

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

[shivan artosh]

Hello Mr. Mohammad

I have the same code, 1. coluld you please write some explannation comment only for the following part of code.

2. i think this code is the same work when we modify AlexNet in (deepNetworkDesigner) am i right or not? or is there any different between 2 ways (manually/deepNetworkDesigner and coding)?

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');
% -------------------------------------------------------------------------

[Mohammad Sami]

This code is for transfer learning. That is when you already have a pretrained model that you wish to use for another purpose. The process is explained in detail in MATLAB's documentation.

https://www.mathworks.com/discovery/transfer-learning.html?s_tid=srchtitle

Using Deep Network Designer:

https://www.mathworks.com/help/deeplearning/gs/get-started-with-transfer-learning.html

Usign Manual Method:

https://www.mathworks.com/help/deeplearning/ug/transfer-learning-using-alexnet.html?s_tid=srchtitle

Since the network was trained with different classes then your current purpose, you have to remove the final few layers at least from the last fully connected layer onwards. This is because the final fully connected layer needs to have the same output size as the number of classess in your data.

You then have to have to create new layers for the layers that you removed. The new fully connected layer has the output size which matches the number of classes in your data. Finally you can add these newly connected layers to complete your network.

[shivan artosh]

hello sir

as i said i have this code and i need to exchange AlexNet with (vgg16, vgg19, ResNet18 and densnet201) one by one.

could you please tell me which part of this code should be changed?

i posted my question here:

https://www.mathworks.com/matlabcentral/answers/604027-alexnet-pretrained-network