How to Download the MNIST Dataset Using PyTorch
The MNIST dataset is a large collection of handwritten digits that is widely used for training and testing various image processing and machine learning systems. It contains 60,000 training images and 10,000 testing images, each of size 28x28 pixels. The dataset is also available in different formats, such as CSV, PNG, or IDX.
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PyTorch is an open source framework that provides a flexible and easy-to-use interface for building deep learning models. PyTorch also offers a rich set of tools and libraries for loading and processing data, such as torchvision, torchtext, and torchaudio. These libraries provide convenient access to many popular datasets, such as MNIST, CIFAR10, IMDB, or LibriSpeech.
In this tutorial, you will learn how to download the MNIST dataset using PyTorch, and how to apply some basic transformations to prepare the data for your model. You will also learn how to create dataloaders that can generate batches of data for training and testing. By the end of this tutorial, you will be able to:
Download the MNIST dataset using torchvision.datasets.MNIST
Load and transform the images and labels using torchvision.transforms
Create dataloaders using torch.utils.data.DataLoader
Iterate over the dataloaders and visualize some samples
To follow this tutorial, you will need:
Python 3.8 or higher
PyTorch 2.0 or higher
Torchvision 0.15 or higher
Matplotlib 3.4 or higher
A working internet connection
Downloading the MNIST Dataset
The easiest way to download the MNIST dataset using PyTorch is to use the torchvision.datasets.MNIST class. This class inherits from torch.utils.data.Dataset, which is an abstract class that represents a dataset. The MNIST class implements two methods: __len__(), which returns the number of samples in the dataset, and __getitem__(), which returns a sample (image and label) given an index.
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To create an instance of the MNIST class, you need to specify three arguments: root, train, and download. The root argument is a string that specifies the root directory where the dataset files will be stored. The train argument is a boolean that indicates whether you want to create a dataset from the training set (True) or from the test set (False). The download argument is a boolean that indicates whether you want to download the dataset from the internet if it is not already present in the root directory.
For example, to create a dataset from the training set and download it if necessary, you can use the following code:
import torchvision.datasets as datasets mnist_train = datasets.MNIST(root='data', train=True, download=True)
This will create a folder named 'data' in your current working directory, and download four files: train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz, t10. k-images-idx3-ubyte.gz, and t10k-labels-idx1-ubyte.gz. These are compressed files that contain the images and labels in a binary format. The MNIST class will automatically decompress and load them into memory when you access the dataset.
To check the integrity and existence of the downloaded files, you can use the torchvision.datasets.utils.check_integrity and torchvision.datasets.utils.check_exists methods. These methods return True if the files are valid and present, and False otherwise. For example, to check the integrity of the train-images-idx3-ubyte file, you can use the following code:
import torchvision.datasets.utils as utils file_path = 'data/MNIST/raw/train-images-idx3-ubyte' md5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873' print(utils.check_integrity(file_path, md5))
This will print True if the file has the correct md5 hash, and False otherwise. Similarly, to check the existence of the train-labels-idx1-ubyte file, you can use the following code:
file_path = 'data/MNIST/raw/train-labels-idx1-ubyte' print(utils.check_exists(file_path))
This will print True if the file exists in the specified path, and False otherwise.
Loading and Transforming the MNIST Dataset
Once you have downloaded the MNIST dataset, you may want to apply some transformations to the images and labels before feeding them to your model. For example, you may want to convert the images from PIL.Image objects to torch.Tensor objects, normalize the pixel values, or resize the images to a different size.
To apply transformations to the MNIST dataset, you can use the transform and target_transform arguments of the MNIST class. The transform argument is a callable that takes an image as input and returns a transformed image. The target_transform argument is a callable that takes a label as input and returns a transformed label. You can pass any function or object that implements the __call__ method as a transformation.
One of the most convenient ways to create transformations is to use the torchvision.transforms module. This module provides a variety of predefined transformations that can be applied to images and labels, such as cropping, flipping, rotating, padding, scaling, etc. You can also define your own custom transformations by subclassing torchvision.transforms.Transform or implementing a function that takes an input and returns an output.
To apply multiple transformations sequentially, you can use torchvision.transforms.Compose. This is a class that takes a list of transformations as input and returns a composed transformation that applies them one by one. For example, to create a transformation that converts an image to a tensor, normalizes its pixel values, and resizes it to 32x32 pixels, you can use the following code:
import torchvision.transforms as transforms transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)), transforms.Resize((32, 32)) ])
The transforms.ToTensor() transformation converts a PIL.Image or numpy.ndarray object to a torch.Tensor object with shape (C, H, W), where C is the number of channels (1 for grayscale images), H is the height, and W is the width. The pixel values are scaled to the range [0, 1].
The transforms.Normalize(mean, std) transformation normalizes a tensor image with mean and standard deviation. The mean and std arguments are sequences of numbers that match the number of channels in the image. The pixel values are subtracted by the mean and divided by the std for each channel.
The transforms.Resize(size) transformation resizes an image to the given size. The size argument can be an int or a tuple of ints. If it is an int, it is interpreted as the smaller edge of the image. If it is a tuple of ints, it is interpreted as (height, width).
To apply this transformation to the MNIST dataset, you can pass it as an argument to the MNIST class. For example, to create a dataset from the test set and apply the transformation, you can use the following code:
mnist_test = datasets.MNIST(root='data', train=False, download=True, transform=transform)
This will create a dataset that contains tensor images with shape (1, 32, 32) and normalized pixel values, and integer labels from 0 to 9.
Creating DataLoaders for the MNIST Dataset
After creating and transforming the MNIST dataset, you may want to create dataloaders that can generate batches of data for training and testing your model. A dataloader is an object that can iterate over a dataset and return a batch of data at each iteration. A batch is a tuple of tensors that contains a subset of images and labels from the dataset.
To create dataloaders for the MNIST dataset, you can use the torch.utils.data.DataLoader class. This class takes a dataset as input and returns a dataloader as output. The dataloader has several arguments that control how the data is batched and shuffled. Some of the most important arguments are:
batch_size: the number of samples per batch. The default value is 1.
shuffle: a boolean that indicates whether to shuffle the data before batching. The default value is False.
num_workers: the number of subprocesses to use for data loading. The default value is 0, which means the data will be loaded in the main process.
For example, to create dataloaders for the training and test sets with a batch size of 64, shuffle enabled, and four workers, you can use the following code:
import torch.utils.data as data train_loader = data.DataLoader(mnist_train, batch_size=64, shuffle=True, num_workers=4) test_loader = data.DataLoader(mnist_test, batch_size=64, shuffle=True, num_workers=4)
This will create two dataloaders that can generate batches of 64 images and labels at each iteration. The images will have shape (64, 1, 32, 32) and the labels will have shape (64).
Iterating over the DataLoaders and Visualizing some Samples
To iterate over the dataloaders and access the batches of data, you can use a for loop or a next function. For example, to get the first batch of data from the train_loader, you can use the following code:
images, labels = next(iter(train_loader))
This will assign two tensors to the variables images and labels. The images tensor will have shape (64, 1, 32, 32) and contain the pixel values of 64 images. The labels tensor will have shape (64) and contain the corresponding labels of 64 images.
To visualize some samples from the batch, you can use matplotlib.pyplot.imshow or torchvision.utils.make_grid. The imshow function takes an image as input and displays it on a plot. The make_grid function takes a batch of images as input and returns a single image that contains a grid of images. For example, to display four images from the batch along with their labels, you can use the following code:
import matplotlib.pyplot as plt import torchvision.utils as utils grid = utils.make_grid(images[:4], nrow=2) plt.imshow(grid.permute(1, 2, 0)) plt.title('Labels: ' + ' '.join(str(labels[:4].numpy()))) plt.show()
This will create a grid image with two rows and two columns, containing four images from the batch. It will also display the labels of these images on the title of the plot. You should see something like this:
Conclusion
In this tutorial, you learned how to download the MNIST dataset using PyTorch, and how to apply some basic transformations to prepare the data for your model. You also learned how to create dataloaders that can generate batches of data for training and testing your model. You also learned how to iterate over the dataloaders and visualize some samples from the batches.
The MNIST dataset is a great resource for learning and experimenting with image processing and machine learning systems. PyTorch provides a convenient and flexible way to access and manipulate this dataset using its tools and libraries. You can use this tutorial as a starting point for building your own models using PyTorch and MNIST.
If you want to learn more about PyTorch and its features , you can check out some of the following resources:
[Welcome to PyTorch Tutorials](^1^): A collection of tutorials that cover various topics and levels of PyTorch, such as vision, text, audio, production, etc.
[Resources PyTorch](^2^): A page that lists various resources for PyTorch developers, such as docs, forums, examples, courses, etc.
[Learn the Basics](^3^): A step-by-step guide to building a complete ML workflow with PyTorch, from loading data to saving models.
We hope you enjoyed this tutorial and learned something new. If you have any feedback or questions, please feel free to leave a comment below or contact us on our [PyTorch Discuss]( forum. We would love to hear from you and help you with your PyTorch journey.
FAQs
Here are some frequently asked questions and answers about downloading the MNIST dataset using PyTorch:
Q: How can I download the MNIST dataset in a different format, such as CSV or PNG?
A: You can use the torchvision.datasets.utils.download_and_extract_archive function to download and extract any archive file that contains the MNIST dataset in a different format. You can find some links to alternative formats of the MNIST dataset [here](
Q: How can I apply different transformations to the training and test sets?
A: You can create different instances of the torchvision.transforms.Compose class and pass them as arguments to the transform argument of the MNIST class. For example, you can apply data augmentation techniques, such as random cropping or flipping, to the training set, and not to the test set.
Q: How can I change the number of classes or samples in the MNIST dataset?
A: You can use the torchvision.datasets.VisionDataset class to create a custom dataset that inherits from the MNIST class and overrides some of its methods. For example, you can override the __len__() method to return a different number of samples, or the __getitem__() method to return a different number of classes.
Q: How can I speed up the data loading process?
A: You can use the num_workers argument of the torch.utils.data.DataLoader class to increase the number of subprocesses that load the data in parallel. You can also use the pin_memory argument to enable memory pinning, which copies the data from CPU memory to GPU memory faster.
Q: How can I save and load the MNIST dataset?
A: You can use the torch.save and torch.load functions to save and load the MNIST dataset as a torch.Tensor object. Alternatively, you can use the torchvision.datasets.utils.save_image and torchvision.datasets.utils.read_image functions to save and load individual images as PIL.Image objects.
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