how to load mnist dataset

The MNIST dataset is a collection of images of handwritten digits that has become a standard benchmark for image classification algorithms. Loading the MNIST dataset is a common task in machine learning and computer vision research. In this article, we will discuss how to load the MNIST dataset using Python code. We will cover different methods that can be used to download and preprocess the dataset for various use cases.

Method 1: Load MNIST dataset using TensorFlow

TensorFlow is an open-source machine learning library developed by Google. It offers a convenient method to download, preprocess, and load the MNIST dataset. The following code demonstrates how to use TensorFlow to load the dataset.import tensorflow as tffrom tensorflow.keras.datasets import mnist(x_train, y_train), (x_test, y_test) = mnist.load_data()The mnist.load_data() function returns two tuples, representing the training and testing sets. Each tuple contains a 2D numpy array of the images and a 1D numpy array of the labels. The images are grayscale and have a size of 28x28 pixels.

Method 2: Load MNIST dataset using PyTorch

PyTorch is another popular machine learning library widely used for deep learning research. It also provides an easy way to download and load the MNIST dataset. The following code demonstrates how to use PyTorch to load the dataset.import torchfrom torchvision.datasets import MNISTtrain_dataset = MNIST(root='data', train=True, transform=None, download=True)test_dataset = MNIST(root='data', train=False, transform=None, download=True)x_train, y_train = train_dataset.data.numpy(), train_dataset.targets.numpy()x_test, y_test = test_dataset.data.numpy(), test_dataset.targets.numpy()Here, we use the MNIST class from the torchvision.datasets module to download the dataset. The train and test arguments specify whether to load the training or testing set. The transform argument can be used to apply transformations to the images, such as normalization or flipping. The download argument specifies whether to download the dataset if it is not present.

Method 3: Load MNIST dataset using scikit-learn

scikit-learn is a popular machine learning library that provides many tools for data preprocessing and model evaluation. It also includes a subset of the MNIST dataset as a built-in dataset. The following code demonstrates how to load the MNIST dataset using scikit-learn.from sklearn.datasets import fetch_openmlmnist = fetch_openml('mnist_784')x_train, y_train = mnist.data[:60000]/255., mnist.target[:60000]x_test, y_test = mnist.data[60000:]/255., mnist.target[60000:]The fetch_openml function downloads the MNIST dataset as a dictionary. The images are stored as a 2D numpy array of size 784 (28x28) and the labels are stored as strings. We divide the pixel values by 255 to normalize the data between 0 and 1, and split the data into training and testing sets.

Conclusion

In this article, we have discussed three different methods to load the MNIST dataset using Python. Each method has its own advantages and disadvantages depending on the use case. TensorFlow and PyTorch are popular choices for deep learning research, while scikit-learn provides a simple interface for data preprocessing and model evaluation. Regardless of the method used, the MNIST dataset serves as an important benchmark and starting point for many machine learning and computer vision projects.