Indexing and slicing in PyTorch

Introduction

Indexing and slicing help you pick parts of data from tensors easily, like choosing pieces from a big cake.

When you want to get a single value from a tensor.

When you need a smaller part of a tensor for calculations.

When you want to change some values inside a tensor.

When you want to loop over parts of data in a tensor.

When you want to prepare data batches for training a model.

Syntax

PyTorch

tensor[start:stop:step]
tensor[index]
tensor[start:stop]
tensor[:, column_index]
tensor[row_index, :]

Use ':' to select ranges, like start to stop (stop not included).

You can use commas to index multiple dimensions, like tensor[row, column].

Examples

Gets the second element (index 1) from the 1D tensor.

PyTorch

x = torch.tensor([10, 20, 30, 40, 50])
print(x[1])

Gets elements from index 1 up to but not including 4.

PyTorch

x = torch.tensor([10, 20, 30, 40, 50])
print(x[1:4])

Gets all rows but only the second column.

PyTorch

x = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(x[:, 1])

Gets the first row and first two columns.

PyTorch

x = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(x[0, :2])

Sample Model

This program shows how to pick single elements, rows, columns, and smaller parts from a 2D tensor using indexing and slicing.

PyTorch

import torch

# Create a 2D tensor
x = torch.tensor([[10, 20, 30], [40, 50, 60], [70, 80, 90]])

# Get the element at row 1, column 2
elem = x[1, 2]

# Get the first row
first_row = x[0, :]

# Get the last column
last_col = x[:, 2]

# Get a sub-tensor of first two rows and first two columns
sub_tensor = x[:2, :2]

print(f"Element at (1, 2): {elem}")
print(f"First row: {first_row}")
print(f"Last column: {last_col}")
print(f"Sub-tensor (2x2):\n{sub_tensor}")

Important Notes

Remember that indexing starts at 0, so the first element is at index 0.

Negative indices count from the end, so -1 is the last element.

Slicing does not include the stop index, so x[0:2] gets elements 0 and 1.

Summary

Indexing picks single elements by position.

Slicing picks ranges of elements using start:stop:step.

Use commas to index multiple dimensions in tensors.

Practice

1. What does the expression tensor[2] do in PyTorch when tensor is a 1D tensor?

easy

A. Selects every second element starting at index 2

B. Selects elements from index 0 to 2

C. Selects the element at index 2

D. Raises an error because indexing starts at 1

Solution

1. Step 1: Understand 1D tensor indexing

   In PyTorch, indexing a 1D tensor with [2] picks the element at position 2 (0-based index).

2. Step 2: Confirm indexing behavior

   Since indexing starts at 0, tensor[2] returns the third element.

3. Final Answer:

   Selects the element at index 2 -> Option C

4. Quick Check:

   Indexing with a single integer picks one element [OK]

Hint: Single index picks one element by position [OK]

Common Mistakes:

• Thinking indexing starts at 1
• Confusing slicing with indexing
• Expecting a range instead of one element

2. Which of the following is the correct syntax to slice a 1D tensor t to get elements from index 1 to 4 (excluding 4)?

easy

A. t(1:4)

B. t[1:4]

C. t[1-4]

D. t[1,4]

Solution

1. Step 1: Recall slicing syntax

   Slicing uses start:stop inside square brackets to select elements from start index up to but not including stop index.

2. Step 2: Check each option

   t[1:4] uses correct slicing syntax t[1:4]. Options A, B, and C use invalid syntax for slicing.

3. Final Answer:

   t[1:4] -> Option B

4. Quick Check:

   Slicing uses colon inside brackets [OK]

Hint: Use colon inside brackets for slicing [OK]

Common Mistakes:

• Using comma instead of colon for slicing
• Using parentheses instead of brackets
• Using minus sign instead of colon

3. Given the tensor t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), what is the output of t[1:, :2]?

medium

A. [[5, 6], [8, 9]]

B. [[2, 3], [5, 6]]

C. [[1, 2], [4, 5]]

D. [[4, 5], [7, 8]]

Solution

1. Step 1: Understand slicing t[1:, :2]

   1: means rows from index 1 to end (rows 1 and 2). :2 means columns from start to index 2 (columns 0 and 1).

2. Step 2: Extract the sliced elements

   Rows 1 and 2 are [[4,5,6],[7,8,9]]. Taking columns 0 and 1 gives [[4,5],[7,8]].

3. Final Answer:

   [[4, 5], [7, 8]] -> Option D

4. Quick Check:

   Row slice 1: and col slice :2 gives [[4,5],[7,8]] [OK]

Hint: Rows after 1, columns before 2 slice tensor [OK]

Common Mistakes:

• Mixing row and column indices
• Including column index 2 mistakenly
• Confusing slicing ranges

4. What is wrong with this code snippet?

import torch
t = torch.tensor([1, 2, 3, 4, 5])
print(t[::2, 1])

medium

A. Trying to index 2D on a 1D tensor

B. Slicing syntax is incorrect

C. Tensor is not created properly

D. Print statement missing parentheses

Solution

1. Step 1: Analyze tensor shape

   The tensor t is 1D with shape (5,). It has only one dimension.

2. Step 2: Check indexing used

   The code uses t[::2, 1] which tries to index two dimensions (rows and columns). This causes an error because t is 1D.

3. Final Answer:

   Trying to index 2D on a 1D tensor -> Option A

4. Quick Check:

   1D tensor cannot be indexed with two indices [OK]

Hint: Match number of indices to tensor dimensions [OK]

Common Mistakes:

• Assuming 1D tensor can be indexed like 2D
• Confusing slicing syntax with indexing
• Ignoring tensor shape before indexing

5. You have a 3D tensor t with shape (4, 5, 6). Which indexing expression extracts all elements from the second dimension at index 3, across all batches and features?

hard

A. t[:, 3, :]

B. t[3, :, :]

C. t[:, :, 3]

D. t[3, 3, :]

Solution

1. Step 1: Understand tensor dimensions

   Tensor shape is (4, 5, 6): first dim = batches, second dim = 5 elements, third dim = 6 features.

2. Step 2: Select all batches, index 3 in second dim, all features

   Use : to select all in first and third dims, and 3 to select index 3 in second dim, so t[:, 3, :].

3. Final Answer:

   t[:, 3, :] -> Option A

4. Quick Check:

   Colon means all, index 3 picks second dim element [OK]

Hint: Use colon for all dims, index for specific dim [OK]

Common Mistakes:

• Mixing dimension order
• Using wrong index for dimension
• Using single index for multiple dims