Indexing and slicing in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is indexing in PyTorch tensors?

Indexing means selecting a single element from a tensor using its position, like picking one item from a list by its number.

beginner

How does slicing work in PyTorch tensors?

Slicing means selecting a range of elements from a tensor, similar to cutting a piece from a loaf of bread by specifying start and end positions.

beginner

What does the colon ':' symbol mean in tensor slicing?

The ':' means 'take all elements' along that dimension, like saying 'give me everything in this row or column'.

intermediate

How do you select the first 3 rows and last 2 columns of a 2D tensor in PyTorch?

Use slicing like tensor[:3, -2:] which means 'rows from start to 3 (not including 3)' and 'last 2 columns'.

intermediate

What happens if you use a negative index in PyTorch tensor indexing?

Negative index counts from the end, so -1 means the last element, -2 means second last, just like counting backwards in a list.

In PyTorch, what does tensor[2] select?

A The second element along the first dimension

B The third element along the first dimension

C All elements except the third

D The last element

Indexing starts at 0, so tensor[2] selects the element at position 2, which is the third element.

What does tensor[:, 1:4] do?

A Selects all rows and columns 1 to 3

B Selects rows 1 to 4 and all columns

C Selects only the first row and columns 1 to 4

D Selects all rows and columns 1 to 4

The ':' means all rows, and '1:4' means columns from 1 up to but not including 4.

How do you select the last element of a tensor?

A tensor[-1]

B tensor[0]

C tensor[1]

D tensor[last]

Negative indexing with -1 selects the last element.

What does tensor[1:5:2] mean?

A Select elements from index 1 to 5, stepping by 2

B Select elements from index 1 to 5, stepping by 1

C Select elements from index 2 to 5, stepping by 2

D Select elements from index 1 to 4, stepping by 2

The slice 1:5:2 means start at 1, go up to but not including 5, stepping by 2.

If tensor is 3D, what does tensor[:, 0, :] select?

A All elements in the first slice of the first dimension

B All elements in the last slice of the third dimension

C All elements in the first slice of the second dimension

D Only the first element of the tensor

The ':' means all elements in that dimension, '0' selects the first element in the second dimension.

Explain how to use indexing and slicing to select parts of a PyTorch tensor.

Think about how you pick items from a list or a grid.

Describe how negative indices work in PyTorch tensor indexing and slicing.

Imagine counting backwards from the end of a line.

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