Tensor shapes and dimensions in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a tensor in PyTorch?

A tensor is a multi-dimensional array used to store data. It can have any number of dimensions, like a list (1D), matrix (2D), or higher.

beginner

What does the shape of a tensor represent?

The shape shows the size of the tensor in each dimension. For example, shape (3, 4) means 3 rows and 4 columns in a 2D tensor.

beginner

How do you get the number of dimensions of a tensor in PyTorch?

Use the `.dim()` method. It returns how many dimensions the tensor has.

beginner

What is the difference between a 1D and 2D tensor?

A 1D tensor is like a list of numbers (shape like [5]). A 2D tensor is like a table or matrix with rows and columns (shape like [3, 4]).

intermediate

How can you change the shape of a tensor without changing its data?

You can use the `.view()` or `.reshape()` methods to change the shape while keeping the same data.

What does the shape (2, 3, 4) of a tensor mean?

A 2 rows, 3 columns, and 4 layers

B 2 matrices, each with 3 rows and 4 columns

C A 2D tensor with 3 rows and 4 columns

D A 1D tensor with 24 elements

Shape (2, 3, 4) means 2 blocks (matrices), each having 3 rows and 4 columns.

Which PyTorch method returns the number of dimensions of a tensor?

A .dim()

B .shape

C .size()

D .length()

The `.dim()` method returns the number of dimensions of a tensor.

If a tensor has shape (5,), what kind of tensor is it?

A 1D tensor

B 3D tensor

C 2D tensor

D Scalar

Shape (5,) means a 1D tensor with 5 elements.

Which method can you use to change the shape of a tensor without changing its data?

A .change_shape()

B .convert()

C .reshape()

D .resize()

The `.reshape()` method changes the shape without altering the data.

What will `tensor.shape` return for a tensor with 3 rows and 4 columns?

A (4, 3)

B 7

C 12

D (3, 4)

The shape is (3, 4) meaning 3 rows and 4 columns.

Explain what tensor shape and dimensions mean in PyTorch and why they matter.

Think about how data is organized in rows, columns, and layers.

Describe how to check and change the shape of a tensor in PyTorch with code examples.

Show how to get shape and dimension, then reshape a tensor.

Practice

1. What does the shape of a PyTorch tensor represent?

easy

A. The number of elements in each dimension of the tensor

B. The data type of the tensor elements

C. The device where the tensor is stored

D. The memory size of the tensor in bytes

Solution

1. Step 1: Understand tensor shape meaning

   The shape of a tensor tells how many elements are in each dimension, like rows and columns in a matrix.

2. Step 2: Differentiate from other properties

   Data type, device, and memory size are different properties and not related to shape.

3. Final Answer:

   The number of elements in each dimension of the tensor -> Option A

4. Quick Check:

   Tensor shape = elements per dimension [OK]

Hint: Shape = counts of elements per dimension [OK]

Common Mistakes:

• Confusing shape with data type
• Thinking shape shows memory size
• Mixing shape with device location

2. Which of the following is the correct way to get the shape of a tensor t in PyTorch?

easy

A. shape(t)

B. t.shape()

C. t.size()

D. t.get_shape()

Solution

1. Step 1: Recall PyTorch shape methods

   In PyTorch, t.size() returns the shape of the tensor as a tuple-like object.

2. Step 2: Check other options

   t.shape is an attribute, not a method, so t.shape() is incorrect. shape(t) and t.get_shape() are not valid PyTorch syntax.

3. Final Answer:

   t.size() -> Option C

4. Quick Check:

   Use t.size() to get shape [OK]

Hint: Use t.size() method to get shape [OK]

Common Mistakes:

• Calling t.shape() as a method
• Using non-existent functions like shape(t)
• Trying t.get_shape() which is invalid

3. What is the shape of the tensor created by torch.randn(3, 4, 5)?

medium

A. (3, 5, 4)

B. (3, 4, 5)

C. (5, 3, 4)

D. (4, 5, 3)

Solution

1. Step 1: Understand torch.randn arguments

   The arguments (3, 4, 5) specify the size of each dimension in order: first dimension 3, second 4, third 5.

2. Step 2: Confirm shape matches arguments

   The tensor shape matches exactly the arguments given, so the shape is (3, 4, 5).

3. Final Answer:

   (3, 4, 5) -> Option B

4. Quick Check:

   Shape matches input sizes (3,4,5) [OK]

Hint: Shape matches torch.randn arguments order [OK]

Common Mistakes:

• Mixing up dimension order
• Assuming shape is reversed
• Confusing shape with total elements

4. Consider this code snippet:

import torch
t = torch.randn(2, 3)
print(t.shape())

What error will this code produce?

medium

A. TypeError: 'torch.Size' object is not callable

B. AttributeError: 'Tensor' object has no attribute 'shape'

C. SyntaxError: invalid syntax

D. No error, prints (2, 3)

Solution

1. Step 1: Identify usage of t.shape()

   In PyTorch, t.shape is an attribute, not a method, so calling t.shape() tries to call a non-callable object.

2. Step 2: Understand error type

   This causes a TypeError saying the 'torch.Size' object is not callable.

3. Final Answer:

   TypeError: 'torch.Size' object is not callable -> Option A

4. Quick Check:

   t.shape is attribute, not method [OK]

Hint: t.shape is attribute, don't add () [OK]

Common Mistakes:

• Calling t.shape() instead of t.shape
• Expecting no error from t.shape()
• Confusing AttributeError with TypeError

5. You have a tensor x with shape (10, 5, 3). You want to reshape it to a 2D tensor with 5 rows and keep the total number of elements the same. Which of the following shapes is valid for the reshaped tensor?

hard

A. (5, 15)

B. (5, 10, 3)

C. (5, 5, 6)

D. (5, 30)

Solution

1. Step 1: Calculate total elements in original tensor

   Original shape is (10, 5, 3), so total elements = 10 * 5 * 3 = 150.

2. Step 2: Check which new shape keeps total elements and has 5 rows

   We want 5 rows, so first dimension = 5. The second dimension must satisfy 5 * ? = 150, so ? = 30. Check options:
   - A: (5, 15) -> 5*15=75 invalid total elements
   - B: (5, 10, 3) -> 5*10*3=150 valid but has 3 dims, not 2
   - C: (5, 30) -> 5*30=150 valid
   - D: (5, 5, 6) -> 5*5*6=150 valid but 3 dims

3. Step 3: Confirm valid shape with 5 rows and total elements

   (5, 30) is (5, 30) which matches 5 rows and total elements 150. (5, 15) is incorrect because 5*15=75, less than 150.

4. Final Answer:

   (5, 30) -> Option D

5. Quick Check:

   5 * 30 = 150 elements [OK]

Hint: Total elements must stay same; multiply dims to check [OK]

Common Mistakes:

• Ignoring total element count when reshaping
• Choosing shape with wrong total elements
• Confusing number of dimensions required