TensorFlow.js for browser - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is TensorFlow.js for browser?

TensorFlow.js for browser is a library that lets you run machine learning models directly in a web browser using JavaScript, without needing a server.

intermediate

How does TensorFlow.js run models in the browser?

It uses WebGL or WebAssembly to perform fast math operations on your computer's graphics card or CPU, enabling efficient machine learning computations inside the browser.

beginner

What are the main benefits of using TensorFlow.js in the browser?

You get instant results without sending data to a server, better privacy since data stays on the user device, and easy integration with web apps.

intermediate

How do you load a pre-trained TensorFlow.js model in the browser?

You use the function `tf.loadLayersModel()` with the URL or path to the model's JSON file to load it asynchronously in your JavaScript code.

intermediate

What is the difference between `tf.tensor()` and `tf.tensor2d()` in TensorFlow.js?

`tf.tensor()` creates a tensor of any shape from data, while `tf.tensor2d()` specifically creates a 2-dimensional tensor (like a matrix) from data.

Which technology does TensorFlow.js use to speed up math operations in the browser?

A Flash

B Java Applets

C WebGL

D Silverlight

TensorFlow.js uses WebGL to access the graphics card for fast math operations.

What function do you use to load a pre-trained model in TensorFlow.js?

A tf.loadModel()

B tf.loadLayersModel()

C tf.importModel()

D tf.getModel()

The correct function is tf.loadLayersModel() to load models saved in the Layers format.

Which of these is NOT a benefit of running ML models in the browser with TensorFlow.js?

A Faster training than on a GPU server

B No need for internet connection after loading

C Instant feedback to user

D Improved user privacy

Training on a GPU server is usually faster than in the browser; TensorFlow.js is better for inference and small training tasks.

What does `tf.tensor2d()` create?

A A 1-dimensional tensor

B A 3-dimensional tensor

C A scalar value

D A 2-dimensional tensor

tf.tensor2d() creates a 2-dimensional tensor, like a matrix.

Which language do you use to write TensorFlow.js code for the browser?

A JavaScript

B Python

C Java

D C++

TensorFlow.js is a JavaScript library designed to run in web browsers.

Explain how TensorFlow.js enables machine learning in the browser and why this is useful.

Think about how your browser can do math fast and keep data local.

Describe the steps to load and use a pre-trained TensorFlow.js model in a web page.

Loading is async, then you predict with tensors.

Practice

1. What is the main advantage of using TensorFlow.js in a web browser?

easy

A. Needs a separate app to visualize data

B. Requires installing special software on the user's computer

C. Run and train machine learning models directly in the browser without extra software

D. Only works on server-side environments

Solution

1. Step 1: Understand TensorFlow.js purpose

   TensorFlow.js is designed to run and train ML models directly in the browser environment.

2. Step 2: Compare options

   Options A, B, and C mention installing software, server-only use, or separate apps, which are not true for TensorFlow.js.

3. Final Answer:

   Run and train machine learning models directly in the browser without extra software -> Option C

4. Quick Check:

   TensorFlow.js runs ML in browser = D [OK]

Hint: TensorFlow.js runs ML models inside browsers without installs [OK]

Common Mistakes:

• Thinking TensorFlow.js needs server or software install
• Confusing browser use with server-side ML
• Assuming extra apps are required

2. Which of the following is the correct way to create a 1D tensor with values [1, 2, 3] in TensorFlow.js?

easy

A. tf.tensor([1; 2; 3])

B. tf.tensor1d(1, 2, 3)

C. tf.tensor([1 2 3])

D. tf.tensor([1, 2, 3])

Solution

1. Step 1: Recall TensorFlow.js tensor creation syntax

   The correct method to create a 1D tensor with values is tf.tensor([1, 2, 3]).

2. Step 2: Check other options for syntax errors

   tf.tensor1d(1, 2, 3) uses wrong function and arguments, A and D have invalid array syntax.

3. Final Answer:

   tf.tensor([1, 2, 3]) -> Option D

4. Quick Check:

   Correct tensor syntax = C [OK]

Hint: Use tf.tensor with array brackets and commas [OK]

Common Mistakes:

• Using semicolons or spaces instead of commas in arrays
• Wrong function name or arguments
• Missing array brackets

3. What will be the output of this TensorFlow.js code snippet?

const t = tf.tensor([1, 2, 3, 4]);
const result = t.square().arraySync();
console.log(result);

medium

A. [1, 4, 9, 16]

B. [1, 2, 3, 4]

C. [2, 4, 6, 8]

D. Error: square() is not a function

Solution

1. Step 1: Understand the code operations

   The tensor t contains [1, 2, 3, 4]. The square() method squares each element.

2. Step 2: Calculate squared values and output

   Squaring each element: 1²=1, 2²=4, 3²=9, 4²=16. The arraySync() converts tensor to JS array.

3. Final Answer:

   [1, 4, 9, 16] -> Option A

4. Quick Check:

   Tensor square() output = A [OK]

Hint: square() applies element-wise power of 2 [OK]

Common Mistakes:

• Confusing square() with identity (no change)
• Expecting sum instead of element-wise operation
• Thinking square() is not a valid method

4. Identify the error in this TensorFlow.js code snippet:

const t = tf.tensor([1, 2, 3]);
const reshaped = t.reshape([2, 2]);
console.log(reshaped.arraySync());

medium

A. tf.tensor() cannot create 1D tensors

B. The reshape size [2, 2] does not match the number of elements in tensor

C. arraySync() is not a valid method

D. reshape() requires a 1D array argument

Solution

1. Step 1: Check tensor size and reshape target shape

   The tensor has 3 elements, but reshape tries to make shape [2, 2] which needs 4 elements.

2. Step 2: Understand reshape requirements

   Reshape must keep total elements same. Here 3 != 4, so it causes an error.

3. Final Answer:

   The reshape size [2, 2] does not match the number of elements in tensor -> Option B

4. Quick Check:

   Reshape size must match elements = A [OK]

Hint: Reshape shape must multiply to original tensor size [OK]

Common Mistakes:

• Ignoring total element count mismatch
• Thinking arraySync() is invalid
• Believing reshape needs 1D array argument

5. You want to build a simple linear regression model in TensorFlow.js that predicts y from x. Which sequence of steps is correct to train the model in the browser?

hard

A. Define model layers, compile with optimizer and loss, prepare tensors for x and y, then call model.fit() with data

B. Directly call model.predict() on raw arrays without compiling or training

C. Use tf.tensor() to create model weights manually and skip model.fit() entirely

D. Train the model only on the server and send predictions to browser

Solution

1. Step 1: Understand model training workflow in TensorFlow.js

   First, define the model architecture with layers, then compile it specifying optimizer and loss function.

2. Step 2: Prepare input and output data as tensors and call model.fit()

   Training requires data tensors for inputs (x) and outputs (y), then call model.fit() to train the model in the browser.

3. Final Answer:

   Define model layers, compile with optimizer and loss, prepare tensors for x and y, then call model.fit() with data -> Option A

4. Quick Check:

   Model training steps = B [OK]

Hint: Define, compile, prepare tensors, then fit model [OK]

Common Mistakes:

• Skipping compile before training
• Trying to predict without training
• Manually setting weights without training
• Assuming training must happen on server