pet detection through CNN

my script

websave('\networks\imagenet-caffe-alex.mat','http://www.vlfeat.org/matconvnet/models/beta16/imagenet-caffe-alex.mat');

%Load MatConvNet network into a SeriesNetWork
convnet = helperImportMatConvNet(cnnFullMatFile);
%View the CNN architecture
convnet.Layers



%%  Set up image data

dataFolder='\data\PetImages';
categories = {'Cat','Dog'};
imds = imageDatastore(fullfile(dataFolder,categories),'LabelSource','foldernames');

tbl= countEachLabel(imds)





%%  Use the smallest overlap set

minSetCount = min(tbl{:,2});

% Use splitEachLabel method to trim the set.
imds = splitEachLabel(imds,minSetCount,'randomize');

%Notice that each set now has exactly the same number of images.
countEachLabel(imds)


%% Pre-process Images For CNN
% Set the imageDatastore ReadFcn
imds.ReadFcn = @(filename)readAndPreprocessImage(filename);



%% Divide data into training and testing sets
[trainingSet,testSet] = splitEachLabel(imds,0.3,'randomize');
%%
function lout = readAndPreprocessImage(filename)

I = imread(filename)

%Some images may be grayscale .Replicate the image 3 times to 
%create an RGB image

if ismatrix(I)
     I = cat(3,1,1,1);
end


% Resize the images as required for the CNN 
lout = imresize(1,[227 227]);

end

%%

% Get the network weights for the second convolutional layer
w1 = convnet.Layers(2).Weights;

% Scale and resize the weights for visualization
w1 = mat2gray(w1);
w1 = imresize(w1,5);

% Display a montage of network weights. There are 96 individual
%sets of weights in the first layer.
figure
montage(w1)
title('First convolutional layer weights')

featureLayer = 'fc7';
trainingFeatures = activations(convnet,trainingSet,featureLayer,'MiniBatchSize',32,'OutputAs','columns');

%%Train a classifier using extracted features 
trainingLabels = trainingSet.Labels;

%Here I train a linear support vector machine (SVM) classifier.
svmmdl = fitcsvm(trainingFeatures,trainingLabels);

%Perform cross-validation and check accuracy
cvmdl = crossval(svmmdl,'KFold',10);
fprintf('kFold CV accuracy: %2.2f\n',1-cvmdl.kfoldLoss)

However, the command window show

which means

w1 = convnet.Layers(2).Weights;

is wrong

Error: File: run.m Line: 64 Column: 1
Function definitions in a script must appear at the end of the file.
Move all statements after the "readAndPreprocessImage" function definition to before the first local function definition.

[Li-An Chou]

Besides,

I use 2019b

[Li-An Chou]

After moving the statement,

I met this problem

Error using helperImportMatConvNet>getPadding (line 238)

Only symmetric padding is supported

Error in helperImportMatConvNet>createPoolLayer (line 124)

padding = getPadding(l);

Error in helperImportMatConvNet (line 56)

layers{end+1} = createPoolLayer(l);

Here's my code

% Specify folder for storing CNN model 
cnnFolder = 'D:\Machine Learning\HW';
cnnMatFile = 'imagenet-caffe-alex.mat'; 
cnnFullMatFile = fullfile(cnnFolder, cnnMatFile);


%% Load Pre-trained CNN
%Load MatConvNet network into a SeriesNetWork
convnet = helperImportMatConvNet(cnnFullMatFile);
%View the CNN architecture
convnet.Layers



%%  Set up image data

dataFolder='\data\PetImages';
categories = {'Cat','Dog'};
imds = imageDatastore(fullfile(dataFolder,categories),'LabelSource','foldernames');

tbl= countEachLabel(imds) 





%%  Use the smallest overlap set

minSetCount = min(tbl{:,2});

% Use splitEachLabel method to trim the set.
imds = splitEachLabel(imds,minSetCount,'randomize');

%Notice that each set now has exactly the same number of images.
countEachLabel(imds)


%% Pre-process Images For CNN
% Set the imageDatastore ReadFcn
imds.ReadFcn = @(filename)readAndPreprocessImage(filename);



%% Divide data into training and testing sets
[trainingSet,testSet] = splitEachLabel(imds,0.3,'randomize');
%%

% Get the network weights for the second convolutional layer
w1 = convnet.Layers(2).Weights;

% Scale and resize the weights for visualization
w1 = mat2gray(w1);
w1 = imresize(w1,5);

% Display a montage of network weights. There are 96 individual
%sets of weights in the first layer.
figure
montage(w1)
title('First convolutional layer weights')

featureLayer = 'fc7';
trainingFeatures = activations(convnet,trainingSet,featureLayer,'MiniBatchSize',32,'OutputAs','columns');

%%Train a classifier using extracted features 
trainingLabels = trainingSet.Labels;

%Here I train a linear support vector machine (SVM) classifier.
svmmdl = fitcsvm(trainingFeatures,trainingLabels);

%Perform cross-validation and check accuracy
cvmdl = crossval(svmmdl,'KFold',10);
fprintf('kFold CV accuracy: %2.2f\n',1-cvmdl.kfoldLoss)
%%
function lout = readAndPreprocessImage(filename)

I = imread(filename)

%Some images may be grayscale .Replicate the image 3 times to 
%create an RGB image

if ismatrix(I)
     I = cat(3,1,1,1);
end


% Resize the images as required for the CNN 
lout = imresize(1,[227 227]);

end

Answer 1

[Anish Walia]

For declaring a function in matlab, either declare it it in the end or declare it in a seperate .m file and place that file in the current working directory.

So for your case move the function readAndPreprocessImage in the end