First PyTorch computation

Introduction

PyTorch helps us do math with numbers easily on computers. It is useful for building smart programs that learn from data.

When you want to add or multiply numbers using a computer program.

When you want to create a simple math example to understand how PyTorch works.

When you want to prepare for building machine learning models.

When you want to try out basic operations on data like addition or multiplication.

When you want to see how PyTorch handles numbers and calculations.

Syntax

PyTorch

import torch

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

Use torch.tensor() to create a tensor, which is like a list of numbers.

You can do math operations like + directly on tensors.

Examples

This example subtracts two tensors element-wise.

PyTorch

import torch

x = torch.tensor([10, 20])
y = torch.tensor([1, 2])
z = x - y
print(z)

This example multiplies two tensors element-wise.

PyTorch

import torch

p = torch.tensor([2, 3])
q = torch.tensor([4, 5])
r = p * q
print(r)

This example divides two tensors element-wise with decimal numbers.

PyTorch

import torch

s = torch.tensor([1.5, 2.5])
t = torch.tensor([2.0, 3.0])
u = s / t
print(u)

Sample Model

This program creates two lists of numbers called tensors. It adds and multiplies them element by element and prints the results.

PyTorch

import torch

# Create two tensors
x = torch.tensor([3, 6, 9])
y = torch.tensor([1, 2, 3])

# Add the tensors
total = x + y

# Multiply the tensors
product = x * y

# Print results
print('Sum:', total)
print('Product:', product)

Important Notes

Tensors are like lists but can do math faster and on special hardware like GPUs.

PyTorch uses tensor objects to hold numbers for calculations.

You can do math operations directly on tensors without loops.

Summary

PyTorch tensors hold numbers for math operations.

You can add, subtract, multiply, and divide tensors easily.

Basic PyTorch computations help you start building machine learning models.

Practice

1. What is a PyTorch tensor mainly used for?

easy

A. Holding numbers for math operations

B. Storing text data only

C. Creating web pages

D. Managing database connections

Solution

1. Step 1: Understand the role of tensors in PyTorch

   PyTorch tensors are like containers that hold numbers for math operations, similar to arrays.

2. Step 2: Compare options with tensor purpose

   Storing text data only, creating web pages, and managing database connections do not relate to math operations or number storage, so they are incorrect.

3. Final Answer:

   Holding numbers for math operations -> Option A

4. Quick Check:

   PyTorch tensor = numbers container [OK]

Hint: Tensors hold numbers for math, not text or web [OK]

Common Mistakes:

• Thinking tensors store only text
• Confusing tensors with web tools
• Assuming tensors manage databases

2. Which of the following is the correct way to create a tensor with values [1, 2, 3] in PyTorch?

easy

A. torch.tensor([1, 2, 3])

B. torch.array([1, 2, 3])

C. torch.list([1, 2, 3])

D. torch.vector([1, 2, 3])

Solution

1. Step 1: Recall PyTorch tensor creation syntax

   PyTorch uses torch.tensor() to create tensors from lists or arrays.

2. Step 2: Check each option's validity

   torch.vector([1, 2, 3]), torch.array([1, 2, 3]), and torch.list([1, 2, 3]) use incorrect functions that do not exist in PyTorch.

3. Final Answer:

   torch.tensor([1, 2, 3]) -> Option A

4. Quick Check:

   Use torch.tensor() to create tensors [OK]

Hint: Use torch.tensor() to create tensors from lists [OK]

Common Mistakes:

• Using torch.array which is not a PyTorch function
• Trying torch.list or torch.vector which don't exist
• Confusing PyTorch with NumPy syntax

3. What will be the output of this PyTorch code?

import torch
x = torch.tensor([2, 4])
y = torch.tensor([3, 1])
z = x + y
print(z)

medium

A. tensor([6, 4])

B. tensor([5, 5])

C. tensor([2, 4, 3, 1])

D. Error: cannot add tensors

Solution

1. Step 1: Add tensors element-wise

   Adding tensors x and y adds each element: 2+3=5 and 4+1=5.

2. Step 2: Confirm output format

   tensor([6, 4]) and tensor([2, 4, 3, 1]) show wrong sums or concatenation, and 'Error: cannot add tensors' is incorrect as addition is valid and produces tensor([5, 5]).

3. Final Answer:

   tensor([5, 5]) -> Option B

4. Quick Check:

   2+3=5 and 4+1=5 [OK]

Hint: Tensor addition is element-wise [OK]

Common Mistakes:

• Thinking addition concatenates tensors
• Mixing element-wise sum with multiplication
• Expecting an error for valid addition

4. Identify the error in this PyTorch code snippet:

import torch
x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5])
z = x + y
print(z)

medium

A. No error, code runs fine

B. Missing import statement

C. Incorrect tensor creation syntax

D. Tensors have different sizes, cannot add

Solution

1. Step 1: Check tensor sizes

   x has 3 elements, y has 2 elements; PyTorch cannot add tensors of different sizes element-wise.

2. Step 2: Identify error cause

   There is no missing import statement and tensor creation syntax is correct. 'No error, code runs fine' is wrong as code raises size mismatch error.

3. Final Answer:

   Tensors have different sizes, cannot add -> Option D

4. Quick Check:

   Tensor sizes must match for addition [OK]

Hint: Check tensor shapes before adding [OK]

Common Mistakes:

• Ignoring size mismatch error
• Assuming PyTorch auto-adjusts sizes
• Thinking import or syntax is wrong

5. You want to multiply two tensors element-wise and then find the sum of all elements in the result. Which PyTorch code correctly does this?

import torch
x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5, 6])
# Choose the correct next line

hard

A. result = torch.dot(x, y).sum()

B. result = x.matmul(y)

C. result = (x * y).sum()

D. result = x + y.sum()

Solution

1. Step 1: Understand element-wise multiplication and sum

   x * y multiplies each element: [1*4, 2*5, 3*6] = [4, 10, 18]. Then sum() adds all: 4+10+18=32.

2. Step 2: Evaluate options

   torch.dot(x, y).sum() applies dot product then sum again (dot product is not element-wise multiplication, and summing a scalar is redundant). x.matmul(y) does matrix multiplication, not element-wise. x + y.sum() adds x to sum of y, not multiplication.

3. Final Answer:

   result = (x * y).sum() -> Option C

4. Quick Check:

   Element-wise multiply then sum = 32 [OK]

Hint: Use (x * y).sum() for element-wise multiply then sum [OK]

Common Mistakes:

• Using matmul instead of element-wise multiply
• Applying sum twice incorrectly
• Adding tensors instead of multiplying