Activation functions (ReLU, Sigmoid, Softmax) in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply the ReLU activation function to the tensor.

PyTorch

import torch
import torch.nn.functional as F

x = torch.tensor([-1.0, 0.0, 1.0, 2.0])
output = F.[1](x)
print(output)

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

Arelu

Bsigmoid

Csoftmax

Dtanh

Attempts:

3 left

💡 Hint

Common Mistakes

Using sigmoid or softmax instead of relu for this task.

Forgetting to import torch.nn.functional as F.

The ReLU function replaces negative values with zero and keeps positive values unchanged. Using F.relu applies this activation.

2fill in blank

medium

Complete the code to apply the Sigmoid activation function to the tensor.

PyTorch

import torch
import torch.nn.functional as F

x = torch.tensor([-2.0, 0.0, 2.0])
output = F.[1](x)
print(output)

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

Asoftmax

Brelu

Csigmoid

Dlog_softmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using ReLU instead of Sigmoid for probability outputs.

Using softmax when only one dimension is present.

The Sigmoid function outputs values between 0 and 1, useful for probabilities. Using F.sigmoid applies this activation.

3fill in blank

hard

Fix the error in applying Softmax activation along the correct dimension.

PyTorch

import torch
import torch.nn.functional as F

x = torch.tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
output = F.softmax(x, dim=[1])
print(output)

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

A0

B1

C2

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using dim=0 applies softmax across batches, which is usually incorrect.

Using dim=2 causes an index error for 2D tensors.

Softmax is usually applied along the feature dimension (dim=1) to convert scores into probabilities per row.

4fill in blank

hard

Fill both blanks to create a tensor and apply ReLU activation.

PyTorch

import torch
import torch.nn.functional as F

x = torch.tensor([1])
output = F.[2](x)
print(output)

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

A[[-1.0, 0.0, 1.0], [2.0, -2.0, 3.0]]

B[1, 2, 3]

Crelu

Dsigmoid

Attempts:

3 left

💡 Hint

Common Mistakes

Using sigmoid instead of relu changes output range.

Using a 1D tensor when a 2D tensor is expected.

The tensor contains positive and negative values. Applying relu sets negatives to zero and keeps positives.

5fill in blank

hard

Fill all three blanks to create a tensor, apply Softmax along the correct dimension, and print the result.

PyTorch

import torch
import torch.nn.functional as F

x = torch.tensor([1])
output = F.[2](x, dim=[3])
print(output)

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

A[[2.0, 1.0, 0.1], [1.0, 3.0, 0.2]]

Brelu

C1

Dsoftmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using relu instead of softmax changes output meaning.

Using dim=0 applies softmax across batches, not features.

The tensor has two rows of scores. Applying softmax along dim=1 converts each row into probabilities.

Practice

1. Which activation function is best suited for the output layer of a binary classification model in PyTorch?

easy

A. Sigmoid

B. ReLU

C. Softmax

D. Tanh

Solution

1. Step 1: Understand the output requirements for binary classification

   Binary classification outputs a probability between 0 and 1 for one class, so the activation must squash values into this range.

2. Step 2: Match activation functions to output needs

   Sigmoid outputs values between 0 and 1, making it ideal for binary classification outputs. ReLU outputs zero or positive values without upper bound, Softmax is for multi-class, and Tanh outputs between -1 and 1.

3. Final Answer:

   Sigmoid -> Option A

4. Quick Check:

   Binary output needs probability = Sigmoid [OK]

Hint: Binary output? Use Sigmoid for probability between 0 and 1 [OK]

Common Mistakes:

• Choosing ReLU which doesn't limit output to 0-1
• Using Softmax for binary instead of multi-class
• Confusing Tanh with Sigmoid output range

2. Which of the following is the correct way to apply the ReLU activation function to a tensor x in PyTorch?

easy

A. torch.activation.relu(x)

B. torch.nn.functional.relu(x)

C. torch.nn.ReLU(x)

D. torch.relu(x)

Solution

1. Step 1: Recall PyTorch ReLU usage

   ReLU can be applied using the functional API or as a module. The functional API call is torch.nn.functional.relu(x).

2. Step 2: Check each option

   torch.relu(x) is incorrect because torch.relu does not exist. torch.activation.relu(x) is invalid syntax. torch.nn.ReLU(x) tries to call the module directly without instantiation. torch.nn.functional.relu(x) correctly uses the functional API.

3. Final Answer:

   torch.nn.functional.relu(x) -> Option B

4. Quick Check:

   Use functional API for ReLU = torch.nn.functional.relu(x) [OK]

Hint: Use torch.nn.functional.relu(x) for direct ReLU call [OK]

Common Mistakes:

• Trying to call torch.relu which doesn't exist
• Using torch.nn.ReLU without creating an instance
• Confusing module and functional API usage

3. What is the output of the following PyTorch code?

import torch
import torch.nn.functional as F
x = torch.tensor([-1.0, 0.0, 2.0])
output = F.relu(x)
print(output.tolist())

medium

A. [-1.0, 0.0, 2.0]

B. [0.0, 1.0, 1.0]

C. [0.0, 0.0, 2.0]

D. [-1.0, 0.0, 0.0]

Solution

1. Step 1: Understand ReLU behavior on input tensor

   ReLU sets negative values to zero and keeps positive values unchanged.

2. Step 2: Apply ReLU to each element

   -1.0 becomes 0.0, 0.0 stays 0.0, 2.0 stays 2.0. So output is [0.0, 0.0, 2.0].

3. Final Answer:

   [0.0, 0.0, 2.0] -> Option C

4. Quick Check:

   ReLU zeroes negatives = [0.0, 0.0, 2.0] [OK]

Hint: ReLU zeroes negatives, keeps positives same [OK]

Common Mistakes:

• Leaving negative values unchanged
• Confusing ReLU with Sigmoid output
• Assuming ReLU changes zeros to ones

4. The following code aims to apply Softmax to a tensor x along dimension 1 but throws an error. What is the error and how to fix it?

import torch
import torch.nn.functional as F
x = torch.tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
output = F.softmax(x, dim=2)
print(output)

medium

A. Missing activation import; fix by importing torch.nn

B. Softmax requires 1D tensor; fix by flattening x

C. Softmax cannot be applied to float tensors; convert to int

D. Dimension 2 is out of range for tensor of shape (2,3); fix by using dim=1

Solution

1. Step 1: Check tensor shape and dimension argument

   Tensor x has shape (2, 3), so valid dims are 0 and 1. Using dim=2 is invalid and causes an error.

2. Step 2: Correct the dimension argument

   To apply Softmax across columns (features), use dim=1. This fixes the error.

3. Final Answer:

   Dimension 2 is out of range for tensor of shape (2,3); fix by using dim=1 -> Option D

4. Quick Check:

   Tensor shape (2,3) means max dim=1 [OK]

Hint: Check tensor shape before choosing dim for Softmax [OK]

Common Mistakes:

• Using invalid dimension index for Softmax
• Thinking Softmax needs 1D input only
• Ignoring error message about dimension out of range

5. You have a multi-class classification problem with 4 classes. Which activation function should you use in the output layer and why?

Choose the best option.

hard

A. Softmax, because it outputs probabilities that sum to 1 across all classes

B. Sigmoid, because it outputs probabilities for each class independently

C. Tanh, because it outputs values between -1 and 1 for better gradients

D. ReLU, because it outputs positive values for all classes

Solution

1. Step 1: Understand multi-class output requirements

   For multi-class classification, the output layer should produce probabilities for each class that sum to 1, representing a valid probability distribution.

2. Step 2: Match activation functions to multi-class needs

   Softmax converts raw scores into probabilities that sum to 1 across classes. ReLU does not produce probabilities, Sigmoid treats classes independently (not summing to 1), and Tanh outputs values between -1 and 1, not probabilities.

3. Final Answer:

   Softmax, because it outputs probabilities that sum to 1 across all classes -> Option A

4. Quick Check:

   Multi-class needs sum-to-1 probabilities = Softmax [OK]

Hint: Multi-class output? Use Softmax for normalized probabilities [OK]

Common Mistakes:

• Using Sigmoid which treats classes independently
• Choosing ReLU which is not probabilistic
• Confusing Tanh output range with probabilities