Broadcasting rules in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add two tensors using broadcasting.

TensorFlow

import tensorflow as tf

a = tf.constant([1, 2, 3])
b = tf.constant([[10], [20], [30]])
result = a [1] b
print(result.numpy())

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

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction (-) instead of addition (+).

Trying to multiply (*) when the task is to add.

Using the plus sign (+) adds the two tensors element-wise with broadcasting.

2fill in blank

medium

Complete the code to reshape a tensor for broadcasting.

TensorFlow

import tensorflow as tf

x = tf.constant([1, 2, 3])
x_reshaped = tf.reshape(x, [1])
print(x_reshaped.shape)

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

A(1, 3)

B(3, 1)

C(3,)

D(1,)

Attempts:

3 left

💡 Hint

Common Mistakes

Using (1, 3) which changes the shape incorrectly.

Using (3,) which is the original shape and may not broadcast as intended.

Reshaping to (3, 1) prepares the tensor for broadcasting with a (3, 4) tensor along the second dimension.

3fill in blank

hard

Fix the error in the code to correctly broadcast and multiply tensors.

TensorFlow

import tensorflow as tf

x = tf.constant([[1, 2, 3], [4, 5, 6]])
y = tf.constant([10, 20])
result = x [1] y
print(result.numpy())

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

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition (+) instead of multiplication (*).

Trying to divide (/) which is not the intended operation.

The multiplication operator (*) correctly broadcasts y to match x's shape for element-wise multiplication.

4fill in blank

hard

Fill in the blank to create a tensor and broadcast it for addition.

TensorFlow

import tensorflow as tf

x = tf.constant([[1, 2, 3], [4, 5, 6]])
y = tf.constant([1])
result = x + y
print(result.numpy())

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

A[10, 20, 30]

B[10, 20]

C[[10, 20, 30]]

D[[10], [20]]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a 1D list like [10, 20] which has shape (2,) and may not broadcast as intended.

Using a list with shape (1, 3) which does not match x's shape for broadcasting.

Using [[10], [20]] allows broadcasting along the columns to add to x.

5fill in blank

hard

Fill in the blank to create a tensor and broadcast it for multiplication.

TensorFlow

import tensorflow as tf

x = tf.constant([[1, 2, 3], [4, 5, 6]])
y = tf.constant([1])
result = x * y
print(result.numpy())

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

A[[10], [20]]

B[10, 20]

C[[1, 2, 3]]

D[[10, 20, 30]]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a 1D list like [10, 20] which won't broadcast correctly.

Using a shape that does not match for broadcasting.

y is [[10], [20]] to broadcast along columns; x shape is (2,3); multiplication works element-wise.

Practice

1. What does broadcasting in TensorFlow allow you to do?

easy

A. Perform math operations on tensors with different but compatible shapes

B. Convert tensors into Python lists automatically

C. Change the data type of tensors without copying data

D. Create new tensors with random values

Solution

1. Step 1: Understand broadcasting concept

   Broadcasting lets TensorFlow perform element-wise operations on tensors even if their shapes differ, as long as they are compatible.

2. Step 2: Identify the correct description

   Only Perform math operations on tensors with different but compatible shapes correctly describes this feature; others describe unrelated tensor operations.

3. Final Answer:

   Perform math operations on tensors with different but compatible shapes -> Option A

4. Quick Check:

   Broadcasting = math on compatible shapes [OK]

Hint: Broadcasting = math on tensors with compatible shapes [OK]

Common Mistakes:

• Thinking broadcasting changes data types
• Confusing broadcasting with tensor creation
• Assuming broadcasting converts tensors to lists

2. Which of the following TensorFlow code snippets correctly broadcasts a tensor of shape (3, 1) with a tensor of shape (1, 4)?

easy

A. tf.constant([1, 2, 3]) + tf.constant([4, 5, 6, 7])

B. tf.constant([[1], [2], [3]]) + tf.constant([[4, 5, 6, 7]])

C. tf.constant([[1, 2, 3]]) + tf.constant([[4], [5], [6], [7]])

D. tf.constant([[1], [2], [3]]) + tf.constant([[4], [5], [6], [7]])

Solution

1. Step 1: Check shapes of tensors in each option

   tf.constant([[1], [2], [3]]) + tf.constant([[4, 5, 6, 7]]) adds (3,1) tensor to (1,4) tensor, which are compatible for broadcasting.

2. Step 2: Verify broadcasting rules

   Shapes (3,1) and (1,4) broadcast to (3,4). Other options have incompatible shapes or wrong dimensions.

3. Final Answer:

   tf.constant([[1], [2], [3]]) + tf.constant([[4, 5, 6, 7]]) -> Option B

4. Quick Check:

   Shapes (3,1) + (1,4) broadcast correctly [OK]

Hint: Match trailing dims: 1 and N broadcast fine [OK]

Common Mistakes:

• Ignoring shape dimensions order
• Assuming 1D tensors broadcast like 2D
• Mixing up rows and columns in shapes

3. What is the output shape of the following TensorFlow operation?

import tensorflow as tf
x = tf.constant([[1, 2, 3]])  # shape (1, 3)
y = tf.constant([4, 5, 6, 7])  # shape (4,)
z = x + y

medium

A. (1, 3, 4)

B. (4, 3)

C. (1, 4)

D. Error due to incompatible shapes

Solution

1. Step 1: Analyze shapes of x and y

   x has shape (1,3), y has shape (4,). TensorFlow aligns shapes from the right.

2. Step 2: Apply broadcasting rules

   y's shape (4,) is treated as (1,4). Shapes (1,3) and (1,4) are incompatible because 3 != 4 and neither is 1.

3. Step 3: Check if broadcasting possible

   Since last dimensions differ and neither is 1, broadcasting fails, causing an error.

4. Final Answer:

   Error due to incompatible shapes -> Option D

5. Quick Check:

   Incompatible shapes cause error [OK]

Hint: Broadcast dims must be equal or 1 from right [OK]

Common Mistakes:

• Assuming (4,) broadcasts to (3,)
• Ignoring dimension order in broadcasting
• Expecting automatic reshaping without error

4. You have two tensors:
a = tf.constant([[1, 2, 3], [4, 5, 6]]) (shape (2, 3))
b = tf.constant([1, 2]) (shape (2,))
Why does a + b raise an error, and how can you fix it?

medium

A. Shapes are incompatible; reshape b to (2,1) before adding

B. Data types differ; cast b to a's dtype

C. Tensors must be same shape; reshape a to (2,2)

D. Broadcasting always works; error is from another cause

Solution

1. Step 1: Check shapes of a and b

   a is (2,3), b is (2,). Broadcasting compares from right: 3 vs 2 incompatible.

2. Step 2: Fix shape for broadcasting

   Reshape b to (2,1) so shapes become (2,3) and (2,1), which broadcast to (2,3).

3. Final Answer:

   Shapes are incompatible; reshape b to (2,1) before adding -> Option A

4. Quick Check:

   Reshape b to (2,1) fixes broadcasting [OK]

Hint: Match dims from right; add missing dims with reshape [OK]

Common Mistakes:

• Ignoring shape mismatch causes error
• Trying to reshape a incorrectly
• Assuming broadcasting fixes all shape issues automatically

5. Given a tensor t of shape (5, 1, 3), you want to add a bias tensor b of shape (3,) to each element along the last dimension. Which code correctly applies broadcasting to add b to t?

hard

A. t + tf.reshape(b, (3, 1))

B. t + tf.reshape(b, (3, 1, 1))

C. t + tf.reshape(b, (1, 1, 3))

D. t + tf.reshape(b, (5, 1, 3))

Solution

1. Step 1: Understand shapes and broadcasting

   t shape is (5,1,3), b shape is (3,). To add b along last dim, b must broadcast to (5,1,3).

2. Step 2: Reshape b for broadcasting

   Reshape b to (1,1,3) so it broadcasts correctly across first two dims.

3. Step 3: Check other options

   A reshapes to (3,1), which pads to (1,3,1) and mismatches middle dim. B reshapes to (3,1,1), mismatching first dim. D fails due to element count mismatch (3 vs 15).

4. Final Answer:

   t + tf.reshape(b, (1, 1, 3)) -> Option C

5. Quick Check:

   Reshape bias to (1,1,3) for last-dim addition [OK]

Hint: Reshape bias to add dims before last dimension [OK]

Common Mistakes:

• Not reshaping bias tensor correctly
• Assuming 1D tensor broadcasts without reshape
• Reshaping bias to wrong shape