Simplified Training Template.7z
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-Control the design of the UI-Choose the logo-Propose a simplified keymap (customisable)-The .7z format seems more efficient than the .zip and some people get confused with it (even with .zip, actually). If there was a way to automate the unzipping, it could help some users.
Notes:All training files are DS SOLIDWORKS Corp. and are licensed to authorized SOLIDWORKS users under the terms of the Dassault Systèmes SolidWorks Corporation Customer License and Online Services Agreement (CLOSA).\"No results found\" most likely means that the selected title has been obsoleted prior to the selected release year. Check the title of your manual or leave the title field blank.
The MNIST (Modified National Institute of Standards and Technology) data consists of 60,000 training images and 10,000 test images. Each image is a crude 28 x 28 (784 pixels) handwritten digit from \"0\" to \"9.\" Each pixel value is a grayscale integer between 0 and 255.
Most popular neural network libraries, including PyTorch, scikit and Keras, have some form of built-in MNIST dataset designed to work with the library. But there are two problems with using a built-in dataset. First, data access becomes a magic black box and important information is hidden. Second, the built-in datasets use all 60,000 training and 10,000 test images and these are very awkward to work with because they're so large.
A good way to see where this article is headed is to take a look at the screenshot of a Python language program in Figure 1. The source MNIST data files are stored in a proprietary binary format. The program loads the binary pixel and label training files into memory, converts the data to tab-delimited text and saves just the first 1,000 training images and their \"0\" to \"9\" labels. To verify the generated training file, the demo program reads the first training image into memory and displays that image, a \"5\" digit, in the shell and graphically.
The first two files hold the pixels values and the associated labels for the 60,000-item training data. The second two files are the 10,000-item test data. If you click on a link you can download the associated file. I suggest downloading to a directory named ZippedBinary. Unlike .zip compressed files, Windows cannot extract .gz files so you need to use an application. I recommend the 7-Zip utility. After installing 7-Zip you can open Windows File Explorer and then right-click on each .gz file and select the Extract Files option. I suggest extracting to a directory named UnzippedBinary and adding a .bin extension to the unzipped files.
Using MNIST Data in a PyTorch ProgramAfter MNIST data has been saved as a text file, it's possible to code a PyTorch Dataset class to read the data and send to a DataLoader object for training. One possible Dataset implementation is presented in Listing 2. The Dataset assumes the MNIST data is in the format described in this article.
The Fashion-MNIST dataset is closely related to MNIST data. Fashion-MNIST has 60,000 training and 10,000 test images where each image is a 28 x 28 grayscale representing one of 10 types of articles of clothing (dress, coat, shirt, etc.) Fashion-MNIST clothing mages are more difficult to classify than MNIST digit images.
The MNIST and Fashion-MNIST datasets are relatively simple because they use grayscale values (one channel). Working with color images (RGB - three channels) is more difficult. The Hello World of color images is the CIFAR-10 dataset. CIFAR-10 contains 50,000 training and 10,000 test 32 x 32 images with 10 classes: airplane, car, bird, cat, deer, dog, frog, horse, ship and truck.
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