
准备工作
- 实验会创建一个 Google Cloud 项目和一些资源,供您使用限定的一段时间
- 实验有时间限制,并且没有暂停功能。如果您中途结束实验,则必须重新开始。
- 在屏幕左上角,点击开始实验即可开始
Launch Vertex AI Workbench instance
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Clone course repo within your Vertex AI Workbench instance
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Classify images with transfer learning
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In this lab, you learn how to build a neural network to classify the tf-flowers (5 flowers) dataset by using a pre-trained image embedding.You load a pre-trained model which is trained on very large, general-purpose datasets and transfer that knowledge to the actual dataset that you want to classify. This means you use a pre-trained model instead of the Flattened layer as your first layer.
You learn how to apply data augmentation in two ways:
For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.
Sign in to Qwiklabs using an incognito window.
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.
When ready, click Start lab.
Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.
Click Open Google Console.
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.
Accept the terms and skip the recovery resource page.
In the Google Cloud console, from the Navigation menu (), select Vertex AI.
Click Enable All Recommended APIs.
In the Navigation menu, click Workbench.
At the top of the Workbench page, ensure you are in the Instances view.
Click Create New.
Configure the Instance:
This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.
Click Check my progress to verify the objective.
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.
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.
In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > computer_vision_fun > labs and open classifying_images_with_transfer_learning.ipynb.
In the Select Kernel dialog, choose TensorFlow 2-11 (Local) from the list of available kernels.
In the notebook interface, click Edit > Clear All Outputs.
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.
Click Check my progress to verify the objective.
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.
The number of stars indicates the following:
You can close the dialog box if you don't want to provide feedback.
For feedback, suggestions, or corrections, please use the Support tab.
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