Tensor operations (add, mul, matmul) in PyTorch - ML Experiment: Train & Evaluate

Experiment - Tensor operations (add, mul, matmul)

Problem:You want to perform basic tensor operations like addition, element-wise multiplication, and matrix multiplication using PyTorch tensors.

Current Metrics:No current metrics as this is a basic tensor operation task.

Issue:You are unsure how to correctly use PyTorch functions for add, mul, and matmul operations and want to see their outputs.

Your Task

Write PyTorch code to create two tensors and perform addition, element-wise multiplication, and matrix multiplication. Verify the outputs are correct.

Use PyTorch tensors only.

Use built-in PyTorch functions or operators for add, mul, and matmul.

Print the results to verify correctness.

Hint 1

Hint 2

Hint 3

Hint 4

Hint 5

Solution

PyTorch

import torch

# Create two tensors
A = torch.tensor([[1, 2], [3, 4]])
B = torch.tensor([[5, 6], [7, 8]])

# Addition
add_result = A + B

# Element-wise multiplication
mul_result = A * B

# Matrix multiplication
matmul_result = torch.matmul(A, B)

# Print results
print('Addition result:\n', add_result)
print('Element-wise multiplication result:\n', mul_result)
print('Matrix multiplication result:\n', matmul_result)

Created two 2x2 tensors A and B.

Performed addition using + operator.

Performed element-wise multiplication using * operator.

Performed matrix multiplication using torch.matmul().

Printed all results to verify correctness.

Results Interpretation

Before: No tensor operations performed.

After: Addition, element-wise multiplication, and matrix multiplication results are correctly computed and printed.

This experiment shows how to use PyTorch tensor operations for basic arithmetic and matrix multiplication, which are foundational for building neural networks and other ML models.

Bonus Experiment

Try performing the same operations on tensors with different shapes and observe what errors or broadcasting behaviors occur.

💡 Hint

Use tensors of shape (2,3) and (3,2) and try addition and matrix multiplication to see shape compatibility requirements.

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