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Loading Data from TensorFlow Datasets

TensorFlow provides easy access to many common public datasets. In this lab, you will learn to load the MNIST database of handwritten digits, a common entry-level machine learning dataset, from TensorFlow Datasets. Using this data, you will build a simple model that will learn to predict numbers found in the images.


Path Info

Clock icon Intermediate
Clock icon 45m
Clock icon Aug 13, 2021

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Table of Contents

  1. Challenge

    Load the MNIST Dataset

    Using TensorFlow Datasets, load the MNIST training and testing data into your program. Additionally, load the dataset information provided by TensorFlow.

  2. Challenge

    Explore the MNIST Dataset

    1. Display the dataset information provided by TensorFlow.
    2. Display the class label names.
    3. Display some example images.
  3. Challenge

    Wrangle the MNIST Dataset

    1. Normalize image pixel data to values between 0 and 1.
    2. Since the dataset is small, load all of it into memory for better performance.
    3. Shuffle the training data to help the model generalize, but don't shuffle the test data.
    4. Batch the data to make training faster.
  4. Challenge

    Teach a Model to Predict Handwritten Digits

    1. Create a basic Keras Sequential deep neural network to predict the number in each image.
    2. Compile the model with an appropriate optimizer and loss function.
    3. Train the model for a few epochs using the training data.
    4. Evaluate the model using the test data.
    5. Save the model for later use.

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What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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We will provide the credentials and environment necessary for you to practice right within your browser.

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Follow along with the author’s guided walkthrough and build something new in your provided environment!

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