Tensor math operations in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this TensorFlow code?

Consider the following code snippet using TensorFlow. What will be the printed output?

TensorFlow

import tensorflow as tf

a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 6], [7, 8]])
c = tf.add(a, b)
print(c.numpy())

A

[[6 8]
 [10 12]]

B

[[4 8]
 [10 12]]

C

[[6 8]
 [11 12]]

D

[[5 7]
 [10 12]]

💡 Hint

tf.add adds corresponding elements of two tensors.

The tf.add function adds each element of tensor 'a' to the corresponding element of tensor 'b'. So 1+5=6, 2+6=8, 3+7=10, 4+8=12.

❓ Model Choice

intermediate

Which TensorFlow operation computes the element-wise product of two tensors?

You want to multiply two tensors element by element in TensorFlow. Which operation should you use?

A tf.matmul

B tf.multiply

C tf.add

D tf.reduce_sum

💡 Hint

Think about the operation that multiplies elements one by one.

tf.multiply performs element-wise multiplication, multiplying each element of one tensor by the corresponding element of the other tensor.

❓ Hyperparameter

advanced

Choosing the correct axis for tf.reduce_sum

Given a tensor of shape (3, 4, 5), which axis should you reduce over to get a tensor of shape (3, 4)?

A axis=0

B axis=1

C axis=None

D axis=2

💡 Hint

Reducing over an axis removes that dimension.

Reducing over axis=2 (the last dimension of size 5) sums across that dimension, resulting in shape (3, 4).

🔧 Debug

advanced

Why does this TensorFlow code raise an error?

Examine the code below. Why does it raise an error?

TensorFlow

import tensorflow as tf

a = tf.constant([1, 2, 3])
b = tf.constant([[1, 2], [3, 4], [5, 6]])
c = tf.add(a, b)
print(c.numpy())

A 'a' and 'b' have different data types

B tf.add cannot add tensors with more than one dimension

C Shapes of 'a' and 'b' are incompatible for broadcasting

D TensorFlow requires explicit reshaping before addition

💡 Hint

Check the shapes of the tensors and how broadcasting works.

Tensor 'a' has shape (3,) and 'b' has shape (3,2). TensorFlow tries to broadcast but the shapes are incompatible because the first dimension of 'a' is missing.

🧠 Conceptual

expert

What is the effect of tf.transpose on a 3D tensor?

You have a tensor of shape (2, 3, 4). What will be the shape after applying tf.transpose with perm=[1, 0, 2]?

A (3, 2, 4)

B (4, 3, 2)

C (2, 4, 3)

D (3, 4, 2)

💡 Hint

tf.transpose permutes the dimensions according to the perm list.

The perm argument [1, 0, 2] swaps the first and second dimensions, so shape (2,3,4) becomes (3,2,4).

Practice

1. What does the TensorFlow function tf.add(tensor1, tensor2) do?

easy

A. Adds two tensors element-wise

B. Multiplies two tensors element-wise

C. Performs matrix multiplication of two tensors

D. Subtracts the second tensor from the first

Solution

1. Step 1: Understand the function name and purpose

   The function tf.add is designed to add values, so it performs addition.

2. Step 2: Check the operation type

   In TensorFlow, tf.add adds two tensors element-wise, meaning it adds corresponding elements from both tensors.

3. Final Answer:

   Adds two tensors element-wise -> Option A

4. Quick Check:

   tf.add = element-wise addition [OK]

Hint: Add means element-wise sum, not matrix multiply [OK]

Common Mistakes:

• Confusing tf.add with matrix multiplication
• Thinking tf.add subtracts tensors
• Assuming tf.add multiplies tensors

2. Which of the following is the correct syntax to perform matrix multiplication of two tensors a and b in TensorFlow?

easy

A. tf.multiply(a, b)

B. tf.add(a, b)

C. tf.matmul(a, b)

D. a.dot(b)

Solution

1. Step 1: Identify the function for matrix multiplication

   TensorFlow uses tf.matmul specifically for matrix multiplication.

2. Step 2: Check other options

   tf.multiply does element-wise multiplication, tf.add adds tensors, and a.dot(b) is invalid since tf.Tensor has no .dot method.

3. Final Answer:

   tf.matmul(a, b) -> Option C

4. Quick Check:

   Matrix multiply = tf.matmul [OK]

Hint: Matrix multiply uses tf.matmul, not tf.multiply [OK]

Common Mistakes:

• Using tf.multiply for matrix multiplication
• Using a.dot(b) like in NumPy
• Confusing addition with multiplication

3. What is the output of the following TensorFlow code?

import tensorflow as tf
x = tf.constant([[1, 2], [3, 4]])
y = tf.constant([[5, 6], [7, 8]])
result = tf.add(x, y)
print(result.numpy())

medium

A. [[6 12]
[10 32]]

B. [[6 8]
[10 12]]

C. [[5 12]
[21 32]]

D. [[1 2]
[3 4]]

Solution

1. Step 1: Understand the operation

   The code uses tf.add to add two 2x2 tensors element-wise.

2. Step 2: Calculate element-wise addition

   Adding corresponding elements: [[1+5, 2+6], [3+7, 4+8]] = [[6, 8], [10, 12]]

3. Final Answer:

   [[6 8] [10 12]] -> Option B

4. Quick Check:

   Element-wise add = [[6 8] [10 12]] [OK]

Hint: Add each element pair to get the result [OK]

Common Mistakes:

• Confusing element-wise add with matrix multiply
• Adding rows or columns incorrectly
• Printing tensor object instead of numpy array

4. Identify the error in this TensorFlow code snippet and choose the fix:

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

medium

A. No error, code runs fine

B. Use tf.add instead of tf.matmul

C. Change x to a 1D tensor

D. Change y to a 2x1 tensor: tf.constant([[5], [6]])

Solution

1. Step 1: Understand matrix multiplication shape rules

   x is shape (2,2), y is shape (2,). TensorFlow tf.matmul requires both inputs to be at least rank 2 tensors.

2. Step 2: Identify the error

   Passing a 1D tensor y to tf.matmul causes a shape error because tf.matmul expects rank >= 2 tensors.

3. Step 3: Fix the error

   Change y to a 2D tensor with shape (2,1): tf.constant([[5], [6]]) to make matrix multiplication valid.

4. Final Answer:

   Change y to a 2x1 tensor: tf.constant([[5], [6]]) -> Option D

5. Quick Check:

   tf.matmul requires rank 2 tensors [OK]

Hint: tf.matmul requires 2D tensors; reshape 1D vector to 2D [OK]

Common Mistakes:

• Assuming 1D tensors cause no shape errors in matmul
• Unnecessarily reshaping y to 2D
• Confusing matmul with element-wise operations

5. You have two tensors:
a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[2, 0], [1, 2]])
Which TensorFlow operation will give the element-wise product of a and b?

hard

A. tf.multiply(a, b)

B. tf.matmul(a, b)

C. tf.add(a, b)

D. tf.tensordot(a, b, axes=1)

Solution

1. Step 1: Understand element-wise product

   Element-wise product multiplies each element of a with the corresponding element of b.

2. Step 2: Identify TensorFlow function for element-wise multiplication

   tf.multiply performs element-wise multiplication, while tf.matmul does matrix multiplication.

3. Final Answer:

   tf.multiply(a, b) -> Option A

4. Quick Check:

   Element-wise multiply = tf.multiply [OK]

Hint: Use tf.multiply for element-wise product, not tf.matmul [OK]

Common Mistakes:

• Using tf.matmul instead of tf.multiply
• Confusing addition with multiplication
• Using tf.tensordot incorrectly