GSP637

Overview

In this lab you will learn the basic ‘Hello World' of machine learning where, instead of programming explicit rules in a language such as Java or C++, you build a system that is trained on data to infer the rules that determine a relationship between numbers.

Objectives

In this lab, you will learn how to:

• Set up the development environment in the Jupyter notebook
• Design a machine learning model
• Train a neural network
• Test a model

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 are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials 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.

Introduction

Consider the following problem: you're building a system that performs activity recognition for fitness tracking. You might have access to the speed at which a person is moving and attempt to infer their activity based on this speed using a conditional:

• You could extend this to running with another condition:

• Similarly, you could detect cycling with another condition:

• Now consider what happens when you want to include an activity like golf? Now, it becomes less obvious how to create a rule to determine the activity.

It's extremely difficult to write a program (expressed in code) that helps you detect the golfing activity.

So what do you do? You can use machine learning to solve the problem!

What is machine learning?

In the previous section you encountered a problem when you tried to determine a user's fitness activity. You hit limitations in what you could achieve by writing more code since your conditions have to be more complex to detect an activity like golf.

Consider building applications in the traditional manner as represented in the following diagram:

You express rules in a programming language. These act on data and your program provides answers. In the case of activity detection, the rules (the code you wrote to define types of activities) acted upon the data (the person's movement speed) in order to find an answer -- the return value from the function for determining the activity status of the user (whether they were walking, running, biking, etc.).

The process for detecting this activity via machine learning is very similar -- only the axes are different:

Instead of trying to define the rules and expressing them in a programming language, you provide the answers (typically called labels) along with the data. The machine then infers the rules that determine the relationship between the answers and the data. For example, in a machine learning context, your activity detection scenario might look like this:

You gather lots of data, and label it to effectively say "This is what walking looks like", "This is what running looks like" etc. Then, from the data, the computer can infer the rules that determine what the distinct patterns that denote a particular activity are.

Beyond being an alternative method to programming this scenario, this also gives you the ability to open up new scenarios, such as golfing, which may not have been possible under the traditional rule-based programming approach.

In traditional programming your code compiles into a binary that is typically called a program. In machine learning, the item that you create from the data and labels is called a model.

So if you go back to this diagram:

Consider the result of the above to be a model, which is used like this at runtime:

You pass the model some data, and the model uses the rules it inferred from the training to come up with a prediction -- i.e. "That data looks like walking", "That data looks like biking" etc.

In this lab you will build a very simple ‘Hello World' model made up of the building blocks that can be used in any machine learning scenario!

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 opens in a new browser tab.

Install TensorFlow and additional packages

1. From the Launcher menu, under Other, select Terminal.

2. Check if your Python environment is already configured. Copy and paste the following command in the terminal.

Example output:

3. Run the following command to install the TensorFlow package.

4. To upgrade pip3, run the following command in the terminal.

Pylint is a tool that checks for errors in Python code, and highlights syntactical and stylistic problems in your Python source code.

5. Run the following command to install the pylint package.

6. Install the packages required for the lab in the requirements.txt file:

Now, your environment is set up!

Task 2. Create your first machine learning model

Consider the following sets of numbers. Can you see the relationship between them?

As you read left to right, notice that the X value is increasing by 1 and the corresponding Y value is increasing by 3. So, the relationship should be Y=3X plus or minus some value.

Then, take look at the 0 on X and see that the corresponding Y value is 1.

From both of these observations, you can determine that the relationship is Y=3X+1.

This is almost exactly how you would use code to train a model, known as a neural network, to spot the patterns in the data!

You use data to train the neural network! By feeding it with a set of Xs and a set of Ys, it should be able to figure out the relationship between them.

Create a new notebook and import libraries

1. Click the + icon on the left side of the Workbench to open a new Launcher.

2. From the Launcher menu, under Notebook, select Python3.

You will be presented with a new Jupyter notebook. For more information on how to use Jupyter notebooks, see the Jupyter Notebook documentation.

3. Import and configure logging and google-cloud-logging for Cloud Logging. In the first cell, add the following code:

4. Import tensorflow for training and evaluating the model. Call it tf for ease of use. Add the following code to the first cell.

5. Import numpy, to parse through the data for debugging purposes. Call it np for ease of use. Add the following code to the first cell.

6. To run the cell, either click the Run button or press Shift + Enter.

7. Save the notebook. Click File -> Save. Name the file model.ipynb and click OK.

Prepare the data

Next up, you will prepare the data your model will be trained on. In this lab, you're using the 6 Xs and 6 Ys used earlier:

As you can see, the relationship between the Xs and Ys is Y=3x+1, so where X = 1, Y = 4 and so on.

A python library called numpy provides lots of array type data structures that are a defacto standard way of feeding in data. To use these, specify the values as an array in numpy using np.array([])

1. In the second cell, add the following code:

Design the model

In this section, you will design your model using TensorFlow.

You will use a machine learning algorithm called neural network to train your model. You will create the simplest possible neural network. It has 1 layer, and that layer has 1 neuron. The neural network's input is only one value at a time. Hence, the input shape must be [1].

1. In the second cell, add the following code:

Compile the model

Next, you will write the code to compile your neural network. When you do, you must specify 2 functions, a loss and an optimizer.

If you've seen lots of math for machine learning, this is where you would usually use it, but tf.keras nicely encapsulates it in functions for you.

• From your previous examination, you know that the relationship between the numbers is y=3x+1.

• When the computer is trying to learn this relationship, it makes a guess...maybe y=10x+10. The loss function measures the guessed answers against the known correct answers and measures how well or how badly it did.

• Next, the model uses the optimizer function to make another guess. Based on the loss function's result, it will try to minimize the loss. At this point, maybe it will come up with something like y=5x+5. While this is still pretty bad, it's closer to the correct result (i.e. the loss is lower).

• The model repeats this for the number of epochs you specify.

1. Add the following code to the second cell:

In the above code snippet, you tell the model to use mean_squared_error for the loss and stochastic gradient descent (sgd) for the optimizer. You don't need to understand the math for these yet, but you will see that they work!

Train the neural network

To train the neural network to 'learn' the relationship between the Xs and Ys, you will use model.fit.

This function will train the model in a loop where it will make a guess, measure how good or bad it is (aka the loss), use the optimizer to make another guess, etc. It will repeat this process for the number of epochs you specify, which in this lab is 500.

1. Add the following code to the second cell:

In the above code model.fit will train the model for a fixed number of epochs.

Now, your file should look like this (note that the code will be in two separate cells):

Run the code

Your script is ready! Run it to see what happens.

1. Click the Run button or press Shift + Enter to run the second cell in the notebook.

2. Look at the output. Notice that the script prints out the loss for each epoch. Your output may be slightly different that what is illustrated here.

If you scroll through the epochs, you see that the loss value is quite large for the first few epochs, but gets smaller with each step. For example:

As the training progresses, the loss gets very small:

And by the time the training is done, the loss becomes extremely small, showing that our model is doing a great job of inferring the relationship between the numbers:

You probably don't need all 500 epochs, try experimenting with different values. Looking at this example, the loss is really small after only 50 epochs, so that might be enough!

Click Check my progress to verify the objective. Create machine learning models

Using the model

You now have a model that has been trained to learn the relationship between X and Y.

You can use the model.predict method to figure out the Y for an X not previously seen by the model during training. So, for example, if X = 10, what do you think Y will be?

1. Add the following code to the third cell to make a prediction:

2. Press Ctrl+s or click File -> Save to save your notebook.

3. To run the third cell, either click the Run button or press Shift + Enter.

The Y value is listed after the training log (epochs).

Example output:

You might have thought Y=31, right? But it ended up being a little over (31.005917). Why do you think that is?

Answer: Neural networks deal with probabilities. It calculated that there is a very high probability that the relationship between X and Y is Y=3X+1. But with only 6 data points it can't know for sure. As a result, the result for 10 is very close to 31, but not necessarily 31.

As you work with neural networks, you'll see this pattern recurring. You will almost always deal with probabilities, not certainties, and will do a little bit of coding to figure out what the result is based on the probabilities, particularly when it comes to classification.

Click Check my progress to verify the objective. Use the model

Congratulations!

Congratulations! In this lab, you have created, trained, and tested your own Machine Learning Model using TensorFlow.

Next steps / learn more

• See all there is to know about Vertex AI.
• Fun stuff with AI! See Experiments with Google.
• Learn more about TensorFlow.
• Bracketology with Google Machine Learning
• Getting Started with BQML
• Predict Taxi Fare with a BigQuery ML Forecasting Model

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Manual Last Updated September 16, 2024

Lab Last Tested September 16, 2024

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