{"id":17,"date":"2024-09-10T16:00:34","date_gmt":"2024-09-10T16:00:34","guid":{"rendered":"https:\/\/kindsonthegenius.com\/pytorch\/?p=17"},"modified":"2024-10-18T21:40:03","modified_gmt":"2024-10-18T21:40:03","slug":"pytorch-tutorials-obtain-your-datasets","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/pytorch\/pytorch-tutorials-obtain-your-datasets\/","title":{"rendered":"PyTorch Tutorial &#8211; Obtain Your Datasets"},"content":{"rendered":"<p>In this tutorial, we would obtain the dataset we need for image recognition. We would cover the following:<\/p>\n<ol>\n<li><a href=\"#t1\">Download the Training and Test Datasets<\/a><\/li>\n<li><a href=\"#t2\">View the Images<\/a><\/li>\n<li><a href=\"#t3\">Create the DataLoaders<\/a><\/li>\n<li><a href=\"#t4\">Examine the Shape of the Data<\/a><\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h4 id=\"t1\"><strong>1. Download the Training and Test Datasets<\/strong><\/h4>\n<p>Download the training dataset<\/p>\n<p><!-- HTML generated using hilite.me --><\/p>\n<pre style=\"margin: 0; line-height: 125%;\">training_data <span style=\"color: #333333;\">=<\/span> datasets<span style=\"color: #333333;\">.<\/span>FashionMNIST(\r\n    root<span style=\"color: #333333;\">=<\/span><span style=\"background-color: #fff0f0;\">\"data\"<\/span>,\r\n    train<span style=\"color: #333333;\">=<\/span><span style=\"color: #008800; font-weight: bold;\">True<\/span>,\r\n    download<span style=\"color: #333333;\">=<\/span><span style=\"color: #008800; font-weight: bold;\">True<\/span>,\r\n    transform<span style=\"color: #333333;\">=<\/span>ToTensor(),\r\n)\r\n<\/pre>\n<p>&nbsp;<\/p>\n<p>Download the test dataset<br \/>\n<!-- HTML generated using hilite.me --><\/p>\n<pre style=\"margin: 0; line-height: 125%;\">test_data <span style=\"color: #333333;\">=<\/span> datasets<span style=\"color: #333333;\">.<\/span>FashionMNIST(\r\n    root<span style=\"color: #333333;\">=<\/span><span style=\"background-color: #fff0f0;\">\"data\"<\/span>,\r\n    train<span style=\"color: #333333;\">=<\/span><span style=\"color: #008800; font-weight: bold;\">False<\/span>,\r\n    download<span style=\"color: #333333;\">=<\/span><span style=\"color: #008800; font-weight: bold;\">True<\/span>,\r\n    transform<span style=\"color: #333333;\">=<\/span>ToTensor(),\r\n)\r\n<\/pre>\n<p>&nbsp;<\/p>\n<h4 id=\"t2\"><strong>2. View the Images<\/strong><\/h4>\n<p>To see the images, use the code below:<\/p>\n<pre style=\"margin: 0; line-height: 125%;\"><span style=\"color: #008800; font-weight: bold;\">import<\/span> <span style=\"color: #0e84b5; font-weight: bold;\">matplotlib.pyplot<\/span> <span style=\"color: #008800; font-weight: bold;\">as<\/span> <span style=\"color: #0e84b5; font-weight: bold;\">plt<\/span>\r\nimage, label <span style=\"color: #333333;\">=<\/span> training_data[<span style=\"color: #0000dd; font-weight: bold;\">1<\/span>]\r\nplt<span style=\"color: #333333;\">.<\/span>imshow(image<span style=\"color: #333333;\">.<\/span>squeeze(), cmap<span style=\"color: #333333;\">=<\/span><span style=\"background-color: #fff0f0;\">\"gray\"<\/span>)\r\nplt<span style=\"color: #333333;\">.<\/span>title(f<span style=\"background-color: #fff0f0;\">\"Label: {label}\"<\/span>)\r\nplt<span style=\"color: #333333;\">.<\/span>show()\r\n<\/pre>\n<p>&nbsp;<\/p>\n<p>The image displays as shown below:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-19 aligncenter\" src=\"https:\/\/kindsonthegenius.com\/pytorch\/wp-content\/uploads\/2024\/08\/PyTorch-image-289x300.png\" alt=\"\" width=\"289\" height=\"300\" srcset=\"https:\/\/kindsonthegenius.com\/pytorch\/wp-content\/uploads\/2024\/08\/PyTorch-image-289x300.png 289w, https:\/\/kindsonthegenius.com\/pytorch\/wp-content\/uploads\/2024\/08\/PyTorch-image.png 417w\" sizes=\"auto, (max-width: 289px) 100vw, 289px\" \/><\/p>\n<h4 id=\"t3\"><strong>3. Create the DataLoaders<\/strong><\/h4>\n<p>Dataloaders helps in efficiently loading and managing datasets for training and evaluating deep learning models. It is a key component in the data preprocessing pipeline.<\/p>\n<p>We would need to pass the Dataset argument to the DataLoader. With it, we have a wrapper for our dataset which we can use to iterate over the dataset in defined batch sizes such as 64. This means that each element in the dataset would return a batch of 64 features and classes.<\/p>\n<p>The dataloader would load the data in batches of 64.<\/p>\n<pre style=\"margin: 0; line-height: 125%;\">batch_size <span style=\"color: #333333;\">=<\/span> <span style=\"color: #0000dd; font-weight: bold;\">64<\/span>\r\ntrain_dataloader <span style=\"color: #333333;\">=<\/span> DataLoader(training_data, batch_size<span style=\"color: #333333;\">=<\/span>batch_size)\r\ntest_dataloader <span style=\"color: #333333;\">=<\/span> DataLoader(test_data, batch_size<span style=\"color: #333333;\">=<\/span>batch_size)\r\n<\/pre>\n<p>&nbsp;<\/p>\n<h4 id=\"t4\"><strong>4. Examine the Shape of the Data<\/strong><\/h4>\n<p>The the dataloader provides the features (X) and the outputs (y). We can use the shape() method to examine the shape of the data<br \/>\nshape() returns a tuple of 4 items<br \/>\nN: batch_size<br \/>\nc: channels, this is the needed to encode each image. For black and white images, the value is 1. this means we only need one color, white and we enocode the intensity. For colored images it would be 3<br \/>\nThis means we must encode 3 colors, Red, Green and Blue (RGB)<br \/>\nH: height of the image<br \/>\nW: width of the image<\/p>\n<p>You can see the shape of the data.<\/p>\n<pre style=\"margin: 0; line-height: 125%;\"><span style=\"color: #008800; font-weight: bold;\">for<\/span> X, y <span style=\"color: #000000; font-weight: bold;\">in<\/span> test_dataloader:\r\n    <span style=\"color: #007020;\">print<\/span>(f<span style=\"background-color: #fff0f0;\">\"Shape of X [N, C, H, W]: {X.shape}\"<\/span>)\r\n    <span style=\"color: #007020;\">print<\/span>(f<span style=\"background-color: #fff0f0;\">\"Shape of y: {y.shape}, {y.dtype}\"<\/span>)\r\n    <span style=\"color: #008800; font-weight: bold;\">break<\/span>\r\n<\/pre>\n<p>&nbsp;<\/p>\n<p>In the next tutorial, we would build the model.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this tutorial, we would obtain the dataset we need for image recognition. We would cover the following: Download the Training and Test Datasets View &hellip; <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[78],"tags":[],"class_list":["post-17","post","type-post","status-publish","format-standard","hentry","category-pytorch-tutorial"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>PyTorch Tutorial - Obtain Your Datasets - PyTorch Simplified Tutorial<\/title>\n<meta name=\"description\" content=\"In this PyTorch tutorial, you will learn how to load the image dataset using DataLoader and examine the images\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/kindsonthegenius.com\/pytorch\/pytorch-tutorials-obtain-your-datasets\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PyTorch Tutorial - Obtain Your Datasets - PyTorch Simplified Tutorial\" \/>\n<meta property=\"og:description\" content=\"In this PyTorch tutorial, you will learn how to load the image dataset using DataLoader and examine the images\" \/>\n<meta property=\"og:url\" content=\"https:\/\/kindsonthegenius.com\/pytorch\/pytorch-tutorials-obtain-your-datasets\/\" \/>\n<meta property=\"og:site_name\" content=\"PyTorch Simplified Tutorial\" \/>\n<meta property=\"article:published_time\" content=\"2024-09-10T16:00:34+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-10-18T21:40:03+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/kindsonthegenius.com\/pytorch\/wp-content\/uploads\/2024\/08\/PyTorch-image-289x300.png\" \/>\n<meta name=\"author\" content=\"Kindson Munonye\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Kindson Munonye\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/kindsonthegenius.com\\\/pytorch\\\/pytorch-tutorials-obtain-your-datasets\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/kindsonthegenius.com\\\/pytorch\\\/pytorch-tutorials-obtain-your-datasets\\\/\"},\"author\":{\"name\":\"Kindson Munonye\",\"@id\":\"https:\\\/\\\/kindsonthegenius.com\\\/pytorch\\\/#\\\/schema\\\/person\\\/2b1e3e1f9e5335a8493d437c3c835c66\"},\"headline\":\"PyTorch Tutorial &#8211; 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