Gradient access (.grad) in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to access the gradient of tensor x after backward pass.

PyTorch

import torch
x = torch.tensor(2.0, requires_grad=True)
y = x * x
loss = y
loss.backward()
print(x[1])

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

A.grad

B.data

C.item()

D.requires_grad

Attempts:

3 left

💡 Hint

Common Mistakes

Using .data instead of .grad to access gradients.

Trying to access gradient before calling backward().

The .grad attribute holds the gradient of the tensor after backward() is called.

2fill in blank

medium

Complete the code to zero out the gradients of tensor x.

PyTorch

import torch
x = torch.tensor(3.0, requires_grad=True)
y = x * 3
loss = y
loss.backward()
x.grad[1]()

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

Aclear

Bzero_

Creset

Dzero

Attempts:

3 left

💡 Hint

Common Mistakes

Using zero() instead of zero_(), which does not modify in-place.

Trying to assign None to x.grad instead of zeroing.

The zero_() method resets the gradient tensor to zero in-place.

3fill in blank

hard

Fix the error in accessing the gradient of tensor z after backward pass.

PyTorch

import torch
z = torch.tensor(4.0, requires_grad=True)
out = z ** 3
out.backward()
print(z[1])

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

A.data

B.grad_fn

C.grad.data

D.grad

Attempts:

3 left

💡 Hint

Common Mistakes

Using .grad_fn which gives the function that created the tensor, not the gradient.

Trying to access .data which is the raw tensor data, not the gradient.

The correct way to access the gradient is z.grad after backward().

4fill in blank

hard

Fill both blanks to compute the gradient of y = x^2 and print it.

PyTorch

import torch
x = torch.tensor(5.0, requires_grad=True)
y = x[1]2
loss = y
loss.backward()
print(x[2])

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

A**

B.grad

C.data

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of ** for power operation.

Accessing .data instead of .grad for gradients.

Use ** for power and .grad to access the gradient after backward().

5fill in blank

hard

Fill all three blanks to compute gradients for z = a * b and print gradients of a and b.

PyTorch

import torch
a = torch.tensor(2.0, requires_grad=True)
b = torch.tensor(3.0, requires_grad=True)
z = a[1]b
z.backward()
print(a[2])
print(b[3])

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

A*

B.grad

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using + instead of * for multiplication.

Trying to access .data instead of .grad for gradients.

Use * for multiplication and .grad to access gradients of both tensors.

Practice

1. What does the .grad attribute of a PyTorch tensor represent?

easy

A. The shape of the tensor

B. The value of the tensor itself

C. The gradient of the tensor after backpropagation

D. The data type of the tensor

Solution

1. Step 1: Understand the role of .grad in PyTorch

   The .grad attribute stores the gradient of the tensor computed during backpropagation.

2. Step 2: Differentiate from other tensor properties

   The tensor's value, shape, and data type are stored in other attributes, not .grad.

3. Final Answer:

   The gradient of the tensor after backpropagation -> Option C

4. Quick Check:

   .grad = gradient after backward() [OK]

Hint: Remember: .grad holds gradients after backward() [OK]

Common Mistakes:

• Confusing .grad with the tensor's value
• Expecting .grad before calling backward()
• Mixing .grad with tensor shape or dtype

2. Which of the following code snippets correctly accesses the gradient of a tensor x after backpropagation?

easy

A. grad = x.grad

B. grad = x.gradient()

C. grad = x.get_grad()

D. grad = x.gradient

Solution

1. Step 1: Recall the correct attribute for gradients

   PyTorch stores gradients in the .grad attribute, accessed as x.grad.

2. Step 2: Identify incorrect syntax

   Methods like gradient() or get_grad() do not exist for tensors.

3. Final Answer:

   grad = x.grad -> Option A

4. Quick Check:

   Use .grad attribute, not methods [OK]

Hint: Use .grad attribute, not functions [OK]

Common Mistakes:

• Trying to call .grad as a function
• Using incorrect method names
• Accessing .gradient instead of .grad

3. Consider the following PyTorch code:

import torch
x = torch.tensor(2.0, requires_grad=True)
y = x ** 3
z = y + 4
z.backward()
print(x.grad)

What will be the output of print(x.grad)?

medium

A. None

B. tensor(12.)

C. tensor(6.)

D. tensor(8.)

Solution

1. Step 1: Compute the derivative of z with respect to x

   Given y = x^3 and z = y + 4, the derivative is dz/dx = dy/dx = 3x^2.

2. Step 2: Calculate the gradient at x=2.0

   Plugging in x=2, 3 * (2)^2 = 3 * 4 = 12. So, x.grad will be tensor(12.).

3. Final Answer:

   tensor(12.) -> Option B

4. Quick Check:

   Derivative 3x^2 at x=2 = 12 [OK]

Hint: Calculate derivative manually, then plug in x [OK]

Common Mistakes:

• Forgetting to call backward() before accessing .grad
• Calculating derivative incorrectly
• Confusing y and z derivatives

4. You run this code:

import torch
x = torch.tensor(3.0, requires_grad=True)
y = x ** 2
print(x.grad)

But the output is None. Why?

medium

A. You forgot to call y.backward() to compute gradients

B. The tensor x does not require gradients

C. You cannot access gradients of scalar tensors

D. The tensor x is not a float

Solution

1. Step 1: Check if backward() was called

   Gradients are computed only after calling backward() on the output tensor.

2. Step 2: Confirm requires_grad=True and missing backward()

   Since x has requires_grad=True, the missing step is calling y.backward().

3. Final Answer:

   You forgot to call y.backward() to compute gradients -> Option A

4. Quick Check:

   Call backward() before accessing .grad [OK]

Hint: Always call backward() before checking .grad [OK]

Common Mistakes:

• Expecting .grad before backward()
• Confusing requires_grad flag
• Thinking .grad auto-updates without backward()

5. Given this code:

import torch
x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
y = (x ** 2).sum()
y.backward()
print(x.grad)

What will be the output of print(x.grad)?

hard

A. None

B. tensor([1., 2., 3.])

C. tensor([1., 4., 9.])

D. tensor([2., 4., 6.])

Solution

1. Step 1: Understand the function and gradient

   The function is y = sum(x_i^2). The gradient of y with respect to x_i is dy/dx_i = 2x_i.

2. Step 2: Calculate gradients for each element

   For x = [1, 2, 3], gradients are [2*1, 2*2, 2*3] = [2, 4, 6].

3. Final Answer:

   tensor([2., 4., 6.]) -> Option D

4. Quick Check:

   Gradient of x^2 is 2x element-wise [OK]

Hint: Gradient of x² is 2x for each element [OK]

Common Mistakes:

• Confusing sum of squares with squares of sums
• Forgetting element-wise gradient
• Expecting None without backward()