md have a problem

% Location of the compressed data set

url = 'http://download.tensorflow.org/example_images/flower_photos.tgz';

% Store the output in a temporary folder

downloadFolder = tempdir;

filename = fullfile(downloadFolder,'flower_dataset.tgz');

% Uncompressed data set

imageFolder = fullfile(downloadFolder,'flower_photos');

if ~exist(imageFolder,'dir') % download only once

disp('Downloading Flower Dataset (218 MB)...');

websave(filename,url);

untar(filename,downloadFolder)

end

imds = imageDatastore(imageFolder, 'LabelSource', 'foldernames', 'IncludeSubfolders',true);

% Find the first instance of an image for each category

daisy = find(imds.Labels == 'daisy', 1);

figure

imshow(readimage(imds,daisy))

tbl = countEachLabel(imds)

% Determine the smallest amount of images in a category

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

% Limit the number of images to reduce the time it takes

% run this example.

maxNumImages = 100;

minSetCount = min(maxNumImages,minSetCount);

% 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)

%%%%%%%%%%%%%%%%%%%% Load pretrained network %%%%%%%%%%%%%%%%%%%%

net = resnet50();

% Visualize the first section of the network.

figure

plot(net)

title('First section of ResNet-50')

set(gca,'YLim',[150 170]);

% Inspect the first layer

net.Layers(1)

% Inspect the last layer

net.Layers(end)

% Number of class names for ImageNet classification task

numel(net.Layers(end).ClassNames)

[trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize');

%%%%%%%%%%%%%%%%%%%% Pre-process Images For CNN %%%%%%%%%%%%%%%%%%%%

% Create augmentedImageDatastore from training and test sets to resize

% images in imds to the size required by the network.

imageSize = net.Layers(1).InputSize;

augmentedTrainingSet = augmentedImageDatastore(imageSize, trainingSet, 'ColorPreprocessing', 'gray2rgb');

augmentedTestSet = augmentedImageDatastore(imageSize, testSet, 'ColorPreprocessing', 'gray2rgb');

%%%%%%%%%%%%%%%%%%%% Extract Training Features Using CNN %%%%%%%%%%%%%%%%%%%%

% Get the network weights for the second convolutional layer

w1 = net.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 = 'fc1000';

trainingFeatures = activations(net, augmentedTrainingSet, featureLayer, ...

'MiniBatchSize', 32, 'OutputAs', 'columns');

%%%%%%%%%%%%%%%%%%%% Train A Multiclass SVM Classifier Using CNN Features %%%%%%%%%%%%%%%%%%%%

% Get training labels from the trainingSet

trainingLabels = trainingSet.Labels;

% Train multiclass SVM classifier using a fast linear solver, and set

% 'ObservationsIn' to 'columns' to match the arrangement used for training

% features.

classifier = fitcecoc(trainingFeatures, trainingLabels, ...

'Learners', 'Linear', 'Coding', 'onevsall', 'ObservationsIn', 'columns');

%%%%%%%%%%%%%%%%%%%% Evaluate Classifier %%%%%%%%%%%%%%%%%%%%

% Extract test features using the CNN

testFeatures = activations(net, augmentedTestSet, featureLayer, ...

'MiniBatchSize', 32, 'OutputAs', 'columns');

% Pass CNN image features to trained classifier

predictedLabels = predict(classifier, testFeatures, 'ObservationsIn', 'columns');

% Get the known labels

testLabels = testSet.Labels;

% Tabulate the results using a confusion matrix.

confMat = confusionmat(testLabels, predictedLabels);

% Convert confusion matrix into percentage form

confMat = bsxfun(@rdivide,confMat,sum(confMat,2))

% Display the mean accuracy

mean(diag(confMat))

%%%%%%%%%%%%%%%%%%%% Apply the Trained Classifier On One Test Image %%%%%%%%%%%%%%%%%%%%

testImage = readimage(testSet,1);

testLabel = testSet.Labels(1)

% Create augmentedImageDatastore to automatically resize the image when

% image features are extracted using activations.

ds = augmentedImageDatastore(imageSize, testImage, 'ColorPreprocessing', 'gray2rgb');

% Extract image features using the CNN

imageFeatures = activations(net, ds, featureLayer, 'OutputAs', 'columns');

% Make a prediction using the classifier

predictedLabel = predict(classifier, imageFeatures, 'ObservationsIn', 'columns')

[Mohammed Al Qarah]

can someone check the code for CCN network

[Image Analyst]

I pulled this out of the spam folder.

Answer 1

[Mohammed Al Qarah]

llmd

[Image Analyst]

By putting in nonsense like this, you made your post go into the spam folder. I'm pulling it out. Why are you putting in these nonsense comments and answers?

Answer 2

[Mohammed Al Qarah]

md md md md help