Tensor shapes and dimensions in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a tensor of shape (3, 4).

PyTorch

import torch
x = torch.zeros([1])
print(x.shape)

Drag options to blanks, or click blank then click option'

A(4, 3)

B[3, 4, 5]

C(3, 4)

D[4, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using square brackets instead of parentheses for shape.

Swapping the dimensions (4, 3) instead of (3, 4).

The shape (3, 4) creates a tensor with 3 rows and 4 columns.

2fill in blank

medium

Complete the code to get the number of dimensions of tensor x.

PyTorch

import torch
x = torch.randn(2, 5, 7)
dimensions = x.[1]()
print(dimensions)

Drag options to blanks, or click blank then click option'

Ashape

Bndim

Csize

Ddim

Attempts:

3 left

💡 Hint

Common Mistakes

Using shape which returns the size tuple, not the number of dimensions.

Using ndim which is not a PyTorch method.

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

3fill in blank

hard

Fix the error in the code to reshape tensor x to shape (6, 10).

PyTorch

import torch
x = torch.randn(3, 20)
x = x.[1]((6, 10))
print(x.shape)

Drag options to blanks, or click blank then click option'

Aview

Bresize

Creshape

Dresize_

Attempts:

3 left

💡 Hint

Common Mistakes

Using reshape() which can sometimes cause errors if tensor is not contiguous.

Using resize() which changes the tensor size in-place and can cause unexpected behavior.

The view() method reshapes a tensor without copying data, but requires the tensor to be contiguous.

4fill in blank

hard

Fill both blanks to create a tensor of shape (2, 3) and then flatten it to 1D.

PyTorch

import torch
x = torch.randn([1])
x_flat = x.[2]()
print(x_flat.shape)

Drag options to blanks, or click blank then click option'

A(2, 3)

B(3, 2)

Cflatten

Dview

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the shape dimensions.

Using view() without specifying the new shape.

First, create a tensor with shape (2, 3). Then use flatten() to convert it to 1D.

5fill in blank

hard

Fill all three blanks to create a tensor, get its shape, and access the size of the second dimension.

PyTorch

import torch
x = torch.randn([1])
shape = x.[2]
second_dim = shape[[3]]
print(second_dim)

Drag options to blanks, or click blank then click option'

A(4, 5, 6)

Bshape

C1

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Using size() method instead of shape attribute.

Accessing index 0 or 2 instead of 1 for the second dimension.

Create a tensor with shape (4, 5, 6). Use shape to get its size tuple. Access index 1 for the second dimension.

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