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Download MNIST Dataset: Best Practices and Common Pitfalls to Avoid


How to Download and Use the MNIST Dataset




The MNIST dataset is a large database of handwritten digits that is commonly used for training various image processing systems and machine learning models. It contains 60,000 training images and 10,000 testing images of digits from 0 to 9. The images are grayscale and have a size of 28x28 pixels.


In this article, I will show you how to download the MNIST dataset from different sources, how to load it into Python using different libraries, and how to plot some examples of the digits using matplotlib. I will also give you some applications and resources for using the MNIST dataset for your own projects.




download mnist dataset



How to Download the MNIST Dataset




There are several ways to download the MNIST dataset, depending on your preference and needs. Here are some of them:


From Keras




Keras is a high-level neural network API that supports multiple backends, including TensorFlow, Theano, and CNTK. It provides a simple way to download and load common datasets, including the MNIST dataset.


To download the MNIST dataset from Keras, you can use the following code:


from keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data()


This will download four files: train-images-idx3-ubyte.gz, train-labels- idx1-ubyte.gz, t10k-images-idx3-ubyte.gz, and t10k-labels-idx1-ubyte.gz from the and store them in the /.keras/datasets folder. It will also load them into four NumPy arrays: train_images, train_labels, test_images, and test_labels. Each image is a 28x28 array of integers between 0 and 255, representing the pixel values. Each label is an integer between 0 and 9, representing the digit class.


From TensorFlow Datasets




TensorFlow Datasets is a collection of datasets ready to use with TensorFlow. It handles downloading and preparing the data and constructing a tf.data.Dataset object. You can also access datasets from other libraries, such as scikit-learn, using TensorFlow Datasets.


To download the MNIST dataset from TensorFlow Datasets, you can use the following code:


import tensorflow_datasets as tfds mnist_data = tfds.load('mnist') train_data, test_data = mnist_data['train'], mnist_data['test']


This will download the same four files as before from the MNIST website and store them in the /tensorflow_datasets/mnist/3.0.1 folder. It will also load them into two tf.data.Dataset objects: train_data and test_data. Each element of the dataset is a dictionary with two keys: 'image' and 'label'. The image is a 28x28x1 tensor of integers between 0 and 255, representing the pixel values. The label is a scalar tensor of integers between 0 and 9, representing the digit class.


From Azure Open Datasets




Azure Open Datasets is a service that provides access to curated open data from various domains, such as weather, census, health, and education. You can download the data as files or access them through Azure Machine Learning or Azure Databricks.


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To download the MNIST dataset from Azure Open Datasets, you can use the following code:


from azureml.opendatasets import MNIST mnist_file_dataset = MNIST.get_file_dataset() mnist_file_dataset.download(target_path='.', overwrite=True)


This will download four files: Train-28x28.csv, Train-label.csv, Test-28x28.csv, and Test-label.csv from the Azure Open Datasets website and store them in the current folder. Each file is a comma-separated values (CSV) file that contains the pixel values or the labels of the images. Each row represents an image or a label, and each column represents a pixel or a class.


How to Load and Plot the MNIST Dataset




Once you have downloaded the MNIST dataset, you can load it into Python using different libraries, depending on how you want to manipulate and analyze the data. Here are some examples:


Using Keras




If you downloaded the MNIST dataset using Keras, you already have it loaded into four NumPy arrays: train_images, train_labels, test_images, and test_labels. You can use these arrays to perform various operations on the data, such as reshaping, normalizing, or augmenting.


To plot some examples of the digits using matplotlib, you can use the following code:


import matplotlib.pyplot as plt %matplotlib inline # Select 16 random images from the training set indices = np.random.choice(range(len(train_images)), 16) # Create a 4x4 grid of subplots fig, axes = plt.subplots(4, 4, figsize=(8, 8)) # Loop over the indices and plot each image with its label for i, ax in zip(indices, axes.flat): image = train_images[i] label = train_labels[i] ax.imshow(image, cmap='gray') ax.set_title(f'Label: label') ax.axis('off') # Show the plot plt.show()


This will produce a plot like this:


Using TensorFlow Datasets




If you downloaded the MNIST dataset using TensorFlow Datasets, you have it loaded into two tf.data.Dataset objects: train_data and test_data. You can use these objects to perform various operations on the data, such as batching, shuffling, or caching.


To plot some examples of the digits using matplotlib, you can use the following code:


import matplotlib.pyplot as plt %matplotlib inline # Take 16 random elements from the training dataset sample_data = train_data.take(16) # Create a 4x4 grid of subplots fig, axes = plt.subplots(4, 4, figsize=(8, 8)) # Loop over the sample data and plot each image with its label for (image, label), ax in zip(sample_data, axes.flat): image = image.numpy().squeeze() label = label.numpy() ax.imshow(image, cmap='gray') ax.set_title(f'Label: label') ax.axis('off') # Show the plot plt.show()


This will produce a similar plot as before.


Using Azure Machine Learning




If you downloaded the MNIST dataset using Azure Open Datasets, you have it stored as four CSV files: Train-28x28.csv, Train-label.csv, Test-28x28.csv, and Test-label.csv. You can use Azure Machine Learning to load these files into pandas DataFrames and perform various operations on the data, such as merging, splitting, or scaling.


To plot some examples of the digits using matplotlib, you can use the following code:


import matplotlib.pyplot as plt %matplotlib inline import pandas as pd # Load the training images and labels into pandas DataFrames train_images_df = pd.read_csv('Train-28x28.csv', header=None) train_labels_df = pd.read_csv('Train-label.csv', header=None) # Select 16 random rows from the DataFrames indices = train_images_df.sample(16).index images = train_images_df.loc[indices] labels = train_labels_df.loc[indices] # Create a 4x4 grid of subplots fig, axes = plt.subplots(4, 4, figsiz


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