GSP632

Overview

A convolution is a filter that passes over an image, processes it, and extracts features that show a commonality in the image. In this lab you explore convolution filters. You learn what they are and how they work by processing an image to extract features from it! You also explore pooling, which compresses your image and further emphasizes the features.

Objectives

In this lab, you will learn how to:

• Load and draw an image from scipy, an open source Python library used for scientific and technical computing
• Create a filter as a 3x3 array and a convolution and see the effects on the image
• Run a pooling to see how it affects the output

Prerequisites

Although this is a self-standing lab, to maximize your learning consider taking these labs before taking this one:

• TensorFlow: Qwik Start
• Introduction to Computer Vision with TensorFlow

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

• Time to complete the lab---remember, once you start, you cannot pause a lab.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

Task 1. Open the notebook in Vertex AI Workbench

1. In the Google Cloud Console, on the Navigation menu, click Vertex AI > Workbench.

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance will open in a new browser tab.

Task 2. Navigate to the lab notebook

1. Click on the file.

2. In the Select Kernel dialog, choose Python 3 from the list of available kernels.

3. Continue the lab in the notebook, and run each cell by clicking the Run icon at the top of the screen. Alternatively, you can execute the code in a cell with SHIFT + ENTER.

Read the narrative and make sure you understand what's happening in each cell.

Click Check my progress to verify the objective. Run the notebook

Congratulations!

This concluded the self-paced lab, Introduction to Convolutions with TensorFlow. You launched the convolutions notebook and explored convolutions and pooling.

Next steps / learn more

• Learn more about TensorFlow at Getting started: training and prediction with TensorFlow Estimator.

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Manual Last Updated October 9, 2024

Lab Last Tested October 8, 2024

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