First PyTorch computation - ML Experiment: Train & Evaluate

Experiment - First PyTorch computation

Problem:You want to perform a simple computation using PyTorch tensors to understand how PyTorch works.

Current Metrics:No metrics yet because no computation or model training has been done.

Issue:You are new to PyTorch and need to learn how to create tensors and perform basic operations.

Your Task

Create two tensors, add them, and print the result. Then multiply the result by 2 and print it.

Use PyTorch tensors only.

Do not use NumPy or other libraries for the computation.

Hint 1

Hint 2

Hint 3

Solution

PyTorch

import torch

# Create two tensors
x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5, 6])

# Add tensors
z = x + y
print(f"Sum of tensors: {z}")

# Multiply the result by 2
result = z * 2
print(f"Result after multiplication by 2: {result}")

Created two 1D tensors x and y with values [1, 2, 3] and [4, 5, 6].

Added the tensors using the + operator.

Multiplied the sum tensor by 2 using the * operator.

Printed both intermediate and final results.

Results Interpretation

Before: No computation done, no output.

After: Successfully created tensors, added them, and multiplied the result. Output shows correct tensor values.

This experiment shows how to create tensors and perform basic arithmetic operations in PyTorch, which is the foundation for building machine learning models.

Bonus Experiment

Try creating two 2D tensors (matrices), perform matrix multiplication, and print the result.

💡 Hint

Use torch.matmul() or the @ operator for matrix multiplication.

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