TensorFlow.js for browser - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple TensorFlow.js tensor.

TensorFlow

const tensor = tf.[1]([1, 2, 3, 4]);

Drag options to blanks, or click blank then click option'

Atensor

Btensor2d

Ctensor1d

Dtensor3d

Attempts:

3 left

💡 Hint

Common Mistakes

Using tf.tensor instead of tf.tensor1d for a 1D array.

Using tf.tensor2d which expects a 2D array.

The tf.tensor1d function creates a 1-dimensional tensor from the given array.

2fill in blank

medium

Complete the code to define a sequential model in TensorFlow.js.

TensorFlow

const model = tf.[1]();

Drag options to blanks, or click blank then click option'

Asequential

BmodelSequential

CSequentialModel

DcreateSequential

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect function names like modelSequential.

Trying to use a constructor instead of a function.

The tf.sequential() function creates a new sequential model in TensorFlow.js.

3fill in blank

hard

Fix the error in the code to add a dense layer with 10 units and ReLU activation.

TensorFlow

model.add(tf.layers.dense({units: [1], activation: 'relu'}));

Drag options to blanks, or click blank then click option'

A'10'

B10

Cunits

D'relu'

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the number 10 inside quotes, making it a string.

Using the string 'units' instead of a number.

The units parameter expects a number, not a string. So use 10 without quotes.

4fill in blank

hard

Fill both blanks to compile the model with optimizer 'adam' and loss 'meanSquaredError'.

TensorFlow

model.compile({optimizer: '[1]', loss: '[2]'});

Drag options to blanks, or click blank then click option'

Aadam

Bsgd

CmeanSquaredError

DcategoricalCrossentropy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sgd' optimizer with 'categoricalCrossentropy' loss which is for classification.

Mixing optimizer and loss values.

Use adam as the optimizer and meanSquaredError as the loss function for regression tasks.

5fill in blank

hard

Fill all three blanks to train the model with data tensors xTrain and yTrain for 5 epochs and batch size 32.

TensorFlow

await model.fit([1], [2], {epochs: [3], batchSize: 32});

Drag options to blanks, or click blank then click option'

AxTrain

ByTrain

C5

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping xTrain and yTrain arguments.

Using 10 epochs instead of 5 as requested.

Use xTrain as input data, yTrain as labels, and train for 5 epochs.

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