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Download and Analyze the MNIST Dataset Images with Azure Open Datasets


How to Download and Use MNIST Dataset Images




MNIST dataset is one of the most popular and widely used datasets in the field of image processing and machine learning. It is a large collection of handwritten digits from 0 to 9, with a total of 70,000 images. The images are grayscale, with a resolution of 28 x 28 pixels. The dataset is already divided into training and testing sets, with 60,000 images for training and 10,000 images for testing.


MNIST dataset is very useful for benchmarking and testing various image processing and machine learning algorithms, such as classification, recognition, segmentation, feature extraction, dimensionality reduction, clustering, etc. It is also a great way to learn and practice the basics of image processing and machine learning, as it provides a simple yet challenging problem to solve.




download mnist dataset images


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In this article, we will show you how to download and use MNIST dataset images in Python. We will cover the following topics:


  • How to import the dataset from Keras



  • How to download the dataset using curl and gunzip commands



  • How to read and process the dataset using Python libraries



  • How to plot some images of the digits using matplotlib.pyplot



  • How to display MNIST image using PIL.Image



By the end of this article, you will have a better understanding of how to work with MNIST dataset images and how to apply them to your own projects.


How to Import MNIST Dataset Images from Keras




Keras is a high-level neural network API that provides various tools and modules for building, training, and evaluating deep learning models. One of the features of Keras is that it provides easy access to several common datasets, including MNIST.


To import MNIST dataset images from Keras, you need to first import the mnist module from keras.datasets. Then, you can use the load_data() function to load the training and testing sets into separate variables. The function returns two tuples: (train_X, train_y) and (test_X, test_y), where train_X and test_X are numpy arrays of shape (60000, 28, 28) and (10000, 28, 28) respectively, containing the pixel values of the images; and train_y and test_y are numpy arrays of shape (60000,) and (10000,) respectively, containing the labels of the images.


Here is an example code snippet that shows how to import MNIST dataset images from Keras:



from keras.datasets import mnist #loading the dataset (train_X, train_y), (test_X, test_y) = mnist.load_data()


How to Download MNIST Dataset Images using Curl and Gunzip Commands




If you prefer to download MNIST dataset images directly from the source website, you can use curl and gunzip commands in your terminal or command prompt. Curl is a command-line tool that allows you to transfer data from or to a server using various protocols. Gunzip is a command-line tool that allows you to decompress files compressed by gzip.


How to download mnist dataset images in Python


Download mnist dataset images as jpg files


Download mnist dataset images from Kaggle


Download mnist dataset images for TensorFlow


Download mnist dataset images using Azure Open Datasets


Download mnist dataset images and labels as NumPy arrays


Download mnist dataset images for image classification


Download mnist dataset images with random distortions


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How to download EMNIST dataset images in Python


Download EMNIST dataset images as jpg files


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Download EMNIST dataset images using Azure Open Datasets


Download EMNIST dataset images and labels as NumPy arrays


Download EMNIST dataset images for image classification


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Download EMNIST dataset images using scikit-learn


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Download EMNIST dataset images with different resolutions


Download EMNIST database of handwritten letters and digits


How to download Fashion-MNIST dataset images in Python


Download Fashion-MNIST dataset images as jpg files


Download Fashion-MNIST dataset images from Kaggle


Download Fashion-MNIST dataset images for TensorFlow


Download Fashion-MNIST dataset images using Azure Open Datasets


Download Fashion-MNIST dataset images and labels as NumPy arrays


Download Fashion-MNIST dataset images for image classification


The source website of MNIST dataset is [ leCun.com/exdb/mnist/]( where you can find four files that contain the training and testing sets of images and labels. The files are compressed by gzip, so you need to use gunzip to decompress them after downloading them.


Here is an example code snippet that shows how to download MNIST dataset images using curl and gunzip commands:



#downloading the files curl -O curl -O curl -O curl -O #decompressing the files gunzip train-images-idx3-ubyte.gz gunzip train-labels-idx1-ubyte.gz gunzip t10k-images-idx3-ubyte.gz gunzip t10k-labels-idx1-ubyte.gz


How to Read and Process MNIST Dataset Images using Python Libraries




Once you have downloaded or imported MNIST dataset images, you need to read and process them using Python libraries. There are several ways to do this, but one of the most common and convenient ways is to use the idx2numpy library. This library allows you to convert IDX files, which are the format of MNIST dataset images, into numpy arrays.


To use idx2numpy library, you need to first install it using pip or conda. Then, you can import it and use the convert_from_file() function to read the IDX files and convert them into numpy arrays. The function takes the file name as an argument and returns a numpy array of the corresponding data.


Here is an example code snippet that shows how to read and process MNIST dataset images using idx2numpy library:



import idx2numpy #reading the files and converting them into numpy arrays train_X = idx2numpy.convert_from_file('train-images-idx3-ubyte') train_y = idx2numpy.convert_from_file('train-labels-idx1-ubyte') test_X = idx2numpy.convert_from_file('t10k-images-idx3-ubyte') test_y = idx2numpy.convert_from_file('t10k-labels-idx1-ubyte')


How to Plot MNIST Dataset Images using Matplotlib




Matplotlib is a Python library that provides various functions and methods for creating and displaying graphs, charts, plots, and other visualizations. One of the features of matplotlib is that it can plot images from numpy arrays using the imshow() function. This function takes a numpy array as an argument and displays it as an image.


To plot MNIST dataset images using matplotlib, you need to first import matplotlib.pyplot as plt. Then, you can use the imshow() function to plot any image from the dataset by passing its index as an argument. You can also use the title() function to add a title to the plot, and the show() function to display the plot.


Here is an example code snippet that shows how to plot MNIST dataset images using matplotlib:



import matplotlib.pyplot as plt #plotting an image from the training set with its label plt.imshow(train_X[0], cmap='gray') plt.title('Label: ' + str(train_y[0])) plt.show() #plotting an image from the testing set with its label plt.imshow(test_X[0], cmap='gray') plt.title('Label: ' + str(test_y[0])) plt.show()


How to Display MNIST Image using PIL.Image




PIL (Python Imaging Library) is a Python library that provides various functions and methods for manipulating and processing images. One of the features of PIL is that it can display images from numpy arrays using the Image module. This module provides a class called Image that represents an image object.


To display MNIST image using PIL.Image, you need to first import Image from PIL. Then, you can use the fromarray() function to create an image object from a numpy array. The function takes a numpy array as an argument and returns an image object of the same shape and mode. You can also use the show() method to display the image object.


Here is an example code snippet that shows how to display MNIST image using PIL.Image:



from PIL import Image #creating an image object from a numpy array img = Image.fromarray(train_X[0]) # #displaying the image object img.show()


Conclusion




In this article, we have learned how to download and use MNIST dataset images in Python. We have covered the following topics:


  • How to import the dataset from Keras



  • How to download the dataset using curl and gunzip commands



How to read and process the dat


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