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hello everyone, I have faced this error on my CNN: (Layer 5 is expected to have a different size)

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I'm trying to implement the CNN algorithm that used on paper (A Deep-Network Solution Towards Model-less Obstacle Avoidance)for Lei Tai, Shaohua Li, and Ming Liu; and when I put their specification of CNN layers; I got the following error: using nnet.cnn.layer.Layer>iInferSize (line 266) Layer 5 is expected to have a different size. if anyone has an idea what is going on, which size they mean? and why I got this error? plese, let me Know.
layers = [imageInputLayer([120 160 1],'Normalization','none');
convolution2dLayer(5,32,'NumChannels',1);
reluLayer();
maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(5,32,'NumChannels',1);
reluLayer();
maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(5,64)
reluLayer();
maxPooling2dLayer(2,'Stride',2);
fullyConnectedLayer(5);
softmaxLayer
classificationLayer()];

  2 Comments

Khadija Al Jabri
Khadija Al Jabri on 19 Nov 2017
if true
% code
% Load the sample data as an |ImageDatastore| object.
digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
'nndatasets','DigitDataset');
digitData = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
%% Specify Training and Test Sets trainingNumFiles = 50; rng(1) % For reproducibility [trainDigitData,testDigitData] = splitEachLabel(digitData, ... trainingNumFiles,'randomize');
%% Define the Network Layers layers = [imageInputLayer([120 160 1],'Normalization','none');
convolution2dLayer(5,32,'NumChannels',1);
reluLayer();
maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(5,32,'NumChannels',1);
reluLayer();
maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(5,64)
reluLayer();
maxPooling2dLayer(2,'Stride',2);
fullyConnectedLayer(5);
softmaxLayer
classificationLayer()];
%% Specify the Training Options options = trainingOptions('sgdm','MaxEpochs',15, ... 'InitialLearnRate',0.0001);
%% Train the Network Using Training Data convnet = trainNetwork(trainDigitData,layers,options);
%% Classify the Images in the Test Data and Compute Accuracy YTest = classify(convnet,testDigitData); TTest = testDigitData.Labels;
% Calculate the accuracy. accuracy = sum(YTest == TTest)/numel(TTest)
end

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Accepted Answer

Javier Pinzón
Javier Pinzón on 1 Dec 2017
Hello Khadija,
The error is located in the "NumChannels", it must have the same amout of channels of the filters used in the poir Convolution layer, so, the correct way to write it is:
convolution2dLayer(5,32,'NumChannels',1);
reluLayer();
maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(5,32,'NumChannels', 32);
reluLayer();
maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(5,64)
reluLayer();
maxPooling2dLayer(2,'Stride',2);
fullyConnectedLayer(5);
softmaxLayer
classificationLayer()];
In other cases, it may be no necessary to specify the number of channels, and let it be automaticaly get.
Hop it helps and thanks if the answer is accepted.
Any question, feel free to ask.
Regards,
Javier

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Khadija Al Jabri
Khadija Al Jabri on 10 Dec 2017
Dear Mr.Javier
could you please help me in this regard? How can I use "randperm" for each category with Matlab? I tried to do it but I can't find the appropriate command!
I looking forward to hearing from you
Regards
Javier Pinzón
Javier Pinzón on 12 Dec 2017
Hello Khadija,
you can check this link:
With that examples, you can create an array of an amount of numbers. After that, you can play with the datastore. At this moment I cannot show you an example, unfortunately.
Regards

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