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Creating a TensorFlow Image Classifier in AWS SageMaker

TensorFlow is the biggest name in machine learning frameworks. In this lab, you will use TensorFlow to create a neural network that performs a basic image classification task: deciding which LEGO brick is in an image to help you sort your giant pile of blocks.

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Path Info

Clock icon Intermediate
Clock icon 1h 0m
Clock icon Jan 25, 2021

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Table of Contents

  1. Challenge

    Navigate to the Jupyter Notebook

    Log in to the AWS console and navigate to the AWS SageMaker page. From there, load the Jupyter Notebook that has been provided with this hands-on lab.

  2. Challenge

    Load and Prepare the Data

    1. Load the training images and labels into numpy arrays. The images and labels are provided in the files lego-simple-train-images.npy and lego-simple-train-labels.npy, respectively.
    2. Load the testing images and labels into numpy arrays. The images and labels are provided in the files lego-simple-test-images.npy and lego-simple-test-labels.npy, respectively.
    3. Add in the human-readable class names for the labels.
    4. Visualize the first few images from the training data set to better understand the data.
  3. Challenge

    Train the TensorFlow Model

    1. Create a neural network model using Keras.
      • Remember to check your input shape and adjust if necessary.
      • You can get decent performance from a single hidden layer, but feel free to experiment with different model architectures.
      • You should have as many output nodes as labels you are trying to predict. Remember to pick an activation function that will output categorical probabilites.
    2. Compile the model, including accuracy as a metric. Your loss function should be one appropriate for classification tasks.
    3. Train the model using the training data and training labels. You won't need to train for many epochs. Save the history of the training process.
  4. Challenge

    Evaluate the Model

    1. Calculate the loss and accuracy of the model on the testing data.
    2. View the raw output of a model prediction for an image in the test set.
    3. Determine the label that the model predicted for the image and compare that to the actual label.
  5. Challenge

    Make a Batch Prediction

    1. Predict the labels for all of the testing data.
    2. Compare the predictions against the actual labels.

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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.

Provided environment for hands-on practice

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

Guided walkthrough

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

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