Tensor operations (add, mul, matmul) in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add two tensors element-wise.

PyTorch

import torch

a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])
result = a[1]b
print(result)

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

A-

B*

C@

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using - or * instead of + for addition.

Using the + operator adds two tensors element-wise.

2fill in blank

medium

Complete the code to multiply two tensors element-wise.

PyTorch

import torch

a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])
result = a[1]b
print(result)

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

A-

B*

C@

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using + or @ instead of * for element-wise multiplication.

The * operator multiplies tensors element-wise in PyTorch.

3fill in blank

hard

Fix the error in the code to perform matrix multiplication of two 2D tensors.

PyTorch

import torch

a = torch.tensor([[1, 2], [3, 4]])
b = torch.tensor([[5, 6], [7, 8]])
result = a[1]b
print(result)

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

A@

B-

C+

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * which does element-wise multiplication, not matrix multiplication.

The @ operator performs matrix multiplication in PyTorch.

4fill in blank

hard

Fill both blanks to create a tensor of squares for numbers 1 to 5, but only include squares greater than 10.

PyTorch

squares = {x: x[1]2 for x in range(1, 6) if x[2]10}
print(squares)

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

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of ** for square.

Using < instead of > for filtering.

** is used for exponentiation (square), and > filters values greater than 10.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys, values as original numbers, and only include numbers greater than 2.

PyTorch

result = [1]: [2] for [3] in range(1, 6) if [3] > 2}
print(result)

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

Astr(x).upper()

Bx

Dnum

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not converting keys to uppercase strings.

Use str(x).upper() to uppercase keys, x as values, and x as the loop variable.

Practice

1. Which PyTorch function is used for element-wise multiplication of two tensors?

easy

A. torch.matmul()

B. torch.dot()

C. torch.add()

D. torch.mul()

Solution

1. Step 1: Understand element-wise multiplication

   Element-wise multiplication multiplies each element of one tensor by the corresponding element of another tensor.

2. Step 2: Identify the correct PyTorch function

   torch.mul() performs element-wise multiplication, while torch.matmul() is for matrix multiplication.

3. Final Answer:

   torch.mul() -> Option D

4. Quick Check:

   Element-wise multiply = torch.mul() [OK]

Hint: Element-wise multiply uses torch.mul() not matmul [OK]

Common Mistakes:

• Confusing matmul with element-wise multiply
• Using torch.add() instead of multiply
• Trying torch.dot() for element-wise multiply

2. Which of the following is the correct syntax to add two tensors a and b in PyTorch?

easy

A. c = torch.add(a, b)

B. c = a + b + 1

C. c = torch.mul(a, b)

D. c = torch.matmul(a, b)

Solution

1. Step 1: Identify the addition operation

   Adding two tensors element-wise can be done with torch.add(a, b) or a + b.

2. Step 2: Check syntax correctness

   torch.add(a, b) is the correct function call. c = a + b + 1 adds 1 extra, so it's incorrect.

3. Final Answer:

   c = torch.add(a, b) -> Option A

4. Quick Check:

   Tensor add syntax = torch.add(a, b) [OK]

Hint: Use torch.add(a, b) or a + b for tensor addition [OK]

Common Mistakes:

• Adding extra numbers accidentally
• Using mul or matmul instead of add
• Incorrect function call syntax

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

import torch
x = torch.tensor([[1, 2], [3, 4]])
y = torch.tensor([[5, 6], [7, 8]])
z = torch.matmul(x, y)
print(z)

medium

A. tensor([[5, 12], [21, 32]])

B. tensor([[6, 8], [10, 12]])

C. tensor([[19, 22], [43, 50]])

D. tensor([[6, 8], [21, 32]])

Solution

1. Step 1: Perform matrix multiplication manually

   Multiply rows of x by columns of y:
   First row: (1*5 + 2*7) = 19, (1*6 + 2*8) = 22
   Second row: (3*5 + 4*7) = 43, (3*6 + 4*8) = 50

2. Step 2: Confirm output tensor

   The result tensor is [[19, 22], [43, 50]].

3. Final Answer:

   tensor([[19, 22], [43, 50]]) -> Option C

4. Quick Check:

   Matrix multiply result = [[19, 22], [43, 50]] [OK]

Hint: Multiply rows by columns for matmul output [OK]

Common Mistakes:

• Adding elements instead of multiplying
• Confusing element-wise multiply with matmul
• Mixing row and column indices

4. Identify the error in this PyTorch code snippet:

import torch
x = torch.tensor([1, 2, 3])
y = torch.tensor([[4, 5, 6], [7, 8, 9]])
z = torch.matmul(x, y)
print(z)

medium

A. Shape mismatch for matrix multiplication

B. x and y must be the same shape for matmul

C. torch.matmul cannot multiply 1D and 2D tensors

D. No error, code runs fine

Solution

1. Step 1: Check tensor shapes

   x shape is (3,), y shape is (2, 3). For matmul, inner dimensions must match: (N, M) x (M, P).

2. Step 2: Identify mismatch

   Here, x is 1D with length 3, y is 2D with shape (2, 3). The inner dimensions don't align for matmul.

3. Final Answer:

   Shape mismatch for matrix multiplication -> Option A

4. Quick Check:

   Matmul needs matching inner dims = shape mismatch [OK]

Hint: Check tensor shapes before matmul to avoid mismatch [OK]

Common Mistakes:

• Assuming 1D and 2D tensors always multiply
• Ignoring shape rules for matmul
• Thinking matmul works like element-wise multiply

5. Given two tensors a = torch.tensor([[1, 2], [3, 4]]) and b = torch.tensor([[2, 0], [1, 2]]), which code correctly computes the element-wise product and then adds the matrix product of a and b?

hard

A. torch.matmul(torch.mul(a, b), torch.add(a, b))

B. torch.add(torch.mul(a, b), torch.matmul(a, b))

C. torch.mul(torch.add(a, b), torch.matmul(a, b))

D. torch.add(torch.matmul(b, a), torch.mul(a, b))

Solution

1. Step 1: Understand the operations order

   First, compute element-wise product: torch.mul(a, b). Then compute matrix product: torch.matmul(a, b).

2. Step 2: Add the two results

   Use torch.add() to sum the element-wise and matrix products.

3. Final Answer:

   torch.add(torch.mul(a, b), torch.matmul(a, b)) -> Option B

4. Quick Check:

   Element-wise mul + matmul = torch.add(torch.mul(), torch.matmul()) [OK]

Hint: Add element-wise mul and matmul results with torch.add() [OK]

Common Mistakes:

• Mixing order of operations
• Using mul on sums instead of add on products
• Confusing arguments order in add or mul