Skip to content

Contact sales

By filling out this form and clicking submit, you acknowledge our privacy policy.
  • Labs icon Lab
  • A Cloud Guru

Classify Images Using TensorFlow

In this lab, you will build an image processing model using TensorFlow that will classify images into one of multiple categories. You will be performing the entire model creation process, from retrieving the data and formatting it properly, to designing a model architecture and training it to meet a desired metric score. This lab is designed to be used as a practice exam to test your skills in preparation for the TensorFlow Developer Certificate, and thus, is a very challenging exercise. Before beginning this lab, you should have PyCharm installed on your local computer. Additionally, you should have installed all packages required by the TensorFlow Developer Certificate exam.


Path Info

Clock icon Advanced
Clock icon 1h 30m
Clock icon Aug 13, 2021

Contact sales

By filling out this form and clicking submit, you acknowledge our privacy policy.

Table of Contents

  1. Challenge

    Retrieve the ibean Datasets

    1. Retrieve the training, validation, and testing ibean datasets:

    2. Extract the compressed data.

  2. Challenge

    Explore the ibean Data

    1. Explore the folder structure created by decompressing the data to understand how to load the images.
    2. Identify the data classes and the file naming convention.
    3. View some of the images from each class to help you understand the data.
  3. Challenge

    Load the ibean Data, and Transform It to a Suitable Form for the Model

    1. Review the model expectations to understand how you should load the data.
    2. Load the training, validation, and test datasets into the program.
    3. Label your data according to the expected model output.
  4. Challenge

    Build and Train a Model to Classify the Images

    1. Review the model expectations to know how the model should accept and output data.
    2. Create an appropriate neural network model using Keras.
    3. Compile your model with the correct loss function for the problem and label type.
    4. Train your model to reach the desired accuracy. Remember to capture the history!
    5. Save your model.
  5. Challenge

    Evaluate Your Model with the Test Data

    1. Generate model statistics on the test data. Ensure you've met or exceeded the desired accuracy.
    2. Plot your model's accuracy and loss for the training process.

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

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!

Did you know?

On average, you retain 75% more of your learning if you get time for practice.

Start learning by doing today

View Plans