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Download the MNIST files from and load the data set into the workspace. To load the data from the files as MATLAB arrays, place the files in the working directory, then use the helper functions processImagesMNIST and processLabelsMNIST, which are used in the example Train Variational Autoencoder (VAE) to Generate Images. To access these functions, open the example as a live script.oldpath = addpath(fullfile(matlabroot,'examples','nnet','main'));filenameImagesTrain = 'train-images-idx3-ubyte.gz';filenameLabelsTrain = 'train-labels-idx1-ubyte.gz';filenameImagesTest = 't10k-images-idx3-ubyte.gz';filenameLabelsTest = 't10k-labels-idx1-ubyte.gz';XTrain = processImagesMNIST(filenameImagesTrain);YTrain = processLabelsMNIST(filenameLabelsTrain);XTest = processImagesMNIST(filenameImagesTest);YTest = processLabelsMNIST(filenameLabelsTest);
Download and extract the Omniglot data set from Set downloadFolder to the location of the data.downloadFolder = tempdir;url = " ";urlTrain = url + "/images_background.zip";urlTest = url + "/images_evaluation.zip";filenameTrain = fullfile(downloadFolder,"images_background.zip");filenameTest = fullfile(downloadFolder,"images_evaluation.zip");dataFolderTrain = fullfile(downloadFolder,"images_background");dataFolderTest = fullfile(downloadFolder,"images_evaluation");if exist(dataFolderTrain,"dir") fprintf("Downloading Omniglot training data set (4.5 MB)... ") websave(filenameTrain,urlTrain); unzip(filenameTrain,downloadFolder); fprintf("Done.\n")endif exist(dataFolderTest,"dir") fprintf("Downloading Omniglot test data (3.2 MB)... ") websave(filenameTest,urlTest); unzip(filenameTest,downloadFolder); fprintf("Done.\n")end
Download and extract the Flowers data set from _images/flower_photos.tgz. The data set is about 218 MB. Set downloadFolder to the location of the data.url = ' _images/flower_photos.tgz';downloadFolder = tempdir;filename = fullfile(downloadFolder,'flower_dataset.tgz');dataFolder = fullfile(downloadFolder,'flower_photos');if exist(dataFolder,'dir') fprintf("Downloading Flowers data set (218 MB)... ") websave(filename,url); untar(filename,downloadFolder) fprintf("Done.\n")end
Download the Example Food Images data set using the downloadSupportFile function and extract the images using the unzip function. This data set is about 77 MB.fprintf("Downloading Example Food Image data set (77 MB)... ")filename = matlab.internal.examples.downloadSupportFile('nnet', ... 'data/ExampleFoodImageDataset.zip');fprintf("Done.\n")filepath = fileparts(filename);dataFolder = fullfile(filepath,'ExampleFoodImageDataset');unzip(filename,dataFolder);
Download and extract the CIFAR-10 data set from -10-matlab.tar.gz. The data set is about 175 MB. Set downloadFolder to the location of the data.url = ' kriz/cifar-10-matlab.tar.gz';downloadFolder = tempdir;filename = fullfile(downloadFolder,'cifar-10-matlab.tar.gz');dataFolder = fullfile(downloadFolder,'cifar-10-batches-mat');if exist(dataFolder,'dir') fprintf("Downloading CIFAR-10 dataset (175 MB)... "); websave(filename,url); untar(filename,downloadFolder); fprintf("Done.\n")endConvert the data to numeric arrays using the helper function loadCIFARData, which is used in the example Train Residual Network for Image Classification. To access this function, open the example as a live script.oldpath = addpath(fullfile(matlabroot,'examples','nnet','main'));[XTrain,YTrain,XValidation,YValidation] = loadCIFARData(downloadFolder);
Download and extract the CamVid data set from The data set is about 573 MB. Set downloadFolder to the location of the data.downloadFolder = tempdir;url = " "urlImages = url + "/files/701_StillsRaw_full.zip";urlLabels = url + "/data/LabeledApproved_full.zip";dataFolder = fullfile(downloadFolder,'CamVid');dataFolderImages = fullfile(dataFolder,'images');dataFolderLabels = fullfile(dataFolder,'labels');filenameLabels = fullfile(dataFolder,'labels.zip');filenameImages = fullfile(dataFolder,'images.zip');if exist(filenameLabels, 'file') exist(imagesZip,'file') mkdir(dataFolder) fprintf("Downloading CamVid data set images (557 MB)... "); websave(filenameImages, urlImages); unzip(filenameImages, dataFolderImages); fprintf("Done.\n") fprintf("Downloading CamVid data set labels (16 MB)... "); websave(filenameLabels, urlLabels); unzip(filenameLabels, dataFolderLabels); fprintf("Done.\n")end
Load the data as a pixel label datastore using the pixelLabelDatastore function and specify the folder containing the label data, the classes, and the label IDs. To make training easier, group some of the original classes to reduce the number of classes from 32 to 11. To get the label IDs, use the helper function camvidPixelLabelIDs, which is used in the example Semantic Segmentation Using Deep Learning. To access this function, open the example as a live script.oldpath = addpath(fullfile(matlabroot,'examples','deeplearning_shared','main'));imds = imageDatastore(dataFolderImages,'IncludeSubfolders',true);classes = ["Sky" "Building" "Pole" "Road" "Pavement" "Tree" ... "SignSymbol" "Fence" "Car" "Pedestrian" "Bicyclist"];labelIDs = camvidPixelLabelIDs;pxds = pixelLabelDatastore(dataFolderLabels,classes,labelIDs);
Download the RIT-18 data set from cnspci/other/data/rit18_data.mat. Set downloadFolder to the location of the data.downloadFolder = tempdir;url = ' cnspci/other/data/rit18_data.mat';filename = fullfile(downloadFolder,'rit18_data.mat');if exist(filename,'file') fprintf("Downloading Hamlin Beach data set (3 GB)... "); websave(filename,url); fprintf("Done.\n")end
Create a directory to store the Zurich RAW to RGB data set.imageDir = fullfile(tempdir,'ZurichRAWToRGB');if exist(imageDir,'dir') mkdir(imageDir);end To download the data set, request access using the Zurich RAW to RGB dataset form. Extract the data into the directory specified by the imageDir variable. If the extraction is successful, then imageDir contains three directories: full_resolution, test, and train.
To download the data set, go to this link: -datasets/SID/Sony.zip. Extract the data into the directory specified by the dataDir variable. When extracted successfully, dataDir contains the directory Sony with two subdirectories: long and short. The files in the long subdirectory have a long exposure and are well-exposed. The files in the short subdirectory have a short exposure and are quite underexposed and dark.
Load the training and test data using the helper functions processTurboFanDataTrain and processTurboFanDataTest, respectively. These functions are used in the example Sequence-to-Sequence Regression Using Deep Learning. To access these functions, open the example as a live script.oldpath = addpath(fullfile(matlabroot,'examples','nnet','main'));filenamePredictors = fullfile(dataFolder,"train_FD001.txt");[XTrain,YTrain] = processTurboFanDataTrain(filenamePredictors);filenamePredictors = fullfile(dataFolder,"test_FD001.txt");filenameResponses = fullfile(dataFolder,"RUL_FD001.txt");[XTest,YTest] = processTurboFanDataTest(filenamePredictors,filenameResponses);
Download the PhysioNet ECG Segmentation data set from the _ECG_segmentation by downloading the ZIP file QT_Database-master.zip. The data set is 72 MB. Set downloadFolder to the location of the data.downloadFolder = tempdir;url = " _ECG_segmentation/raw/master/QT_Database-master.zip";filename = fullfile(downloadFolder,"QT_Database-master.zip");dataFolder = fullfile(downloadFolder,"QT_Database-master");if exist(dataFolder,"dir") fprintf("Downloading Physionet ECG Segmentation data set (72 MB)... ") websave(filename,url); unzip(filename,downloadFolder); fprintf("Done.\n")end
oldpath = addpath(fullfile(matlabroot,'examples','phased','main'));numPed = 1; % Number of pedestrian realizationsnumBic = 1; % Number of bicyclist realizationsnumCar = 1; % Number of car realizations[xPedRec,xBicRec,xCarRec,Tsamp] = helperBackScatterSignals(numPed,numBic,numCar);[SPed,T,F] = helperDopplerSignatures(xPedRec,Tsamp);[SBic,,] = helperDopplerSignatures(xBicRec,Tsamp);[SCar,,] = helperDopplerSignatures(xCarRec,Tsamp);
After you extract the RAR files, get the file names and the labels of the videos by using the helper function hmdb51Files, which used in the example Classify Videos Using Deep Learning. Set dataFolder to the location of the data. To access this function, open the example as a live script.oldpath = addpath(fullfile(matlabroot,'examples','nnet','main'));dataFolder = fullfile(tempdir,"hmdb51_org");[files,labels] = hmdb51Files(dataFolder);
Execute this code to download the data set. url = ' _LidarData.tar.gz';outputFolder = fullfile(tempdir,'WPI');lidarDataTarFile = fullfile(outputFolder,'WPI_LidarData.tar.gz');if exist(lidarDataTarFile, 'file') mkdir(outputFolder); disp('Downloading WPI Lidar driving data (760 MB)...'); websave(lidarDataTarFile, url); untar(lidarDataTarFile,outputFolder); endlidarData = load(fullfile(outputFolder, 'WPI_LidarData.mat'));Depending on your internet connection, the download process can take some time. Alternatively, you can download the data set directly to your local disk from your web browser using the URL, and extract the WPI_LidarData folder. If you do so, change the outputFolder variable in the code to the location of the downloaded file. 2ff7e9595c
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