Overview

In this lab, you learn how to classify images of flowers with a linear model using the tf.keras API.

Learning objectives

In this lab, you learn how to:

• Examine and understand the data.
• Implement a linear model using the Keras API.
• Plot the predictions.

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Task 1. Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click OPEN JUPYTERLAB next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 2. Clone a course repo within your JupyterLab interface

To clone the training-data-analyst notebook in your JupyterLab instance:

Step 1

In JupyterLab, click the Terminal icon to open a new terminal.

Step 2

At the command-line prompt, type in the following command and press Enter.

git clone https://github.com/GoogleCloudPlatform/training-data-analyst

Step 3

Confirm that you have cloned the repository by double clicking on the training-data-analyst directory and ensuring that you can see its contents. The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Click Check my progress to verify the objective. Clone a course repo within your JupyterLab interface

Task 3. Classify images with a Linear model

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > computer_vision_fun > labs and open classifying_images_with_a_linear_model.ipynb.

2. In the Select Kernel dialog, choose TensorFlow 2-11 (Local) from the list of available kernels.

3. In the notebook interface, click Edit > Clear All Outputs.

4. Carefully read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code.

Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you. Highlight the text to read the hints, which are in white text.
• To view the complete solution, navigate to training-data-analyst > courses > machine_learning > deepdive2 > computer_vision_fun > solutions, and open classifying_images_with_a_linear_model.ipynb.

Note: If prompted, File save error for classifying_images_with_a_linear_model.ipnyb, click Dismiss.

Click Check my progress to verify the objective. Classify images with a Linear model in your Notebook

End your lab

When you have completed your lab, click End Lab. Qwiklabs removes the resources you’ve used and cleans the account for you.

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

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• 2 stars = Dissatisfied
• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

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