Learn what an activation value is in neural networks, specifically in CNNs. Understand its role in transforming the weighted sum of inputs and how it impacts deep learning models.
Table of Contents
Question
Classify and select from the given option, what is an activation value?
A. weighted sum of inputs
B. threshold value
C. main input to neuron
D. none of the mentioned
Answer
A. weighted sum of inputs
Explanation
It is definition of activation value & is basic Q&A.
An activation value in the context of neural networks refers to the result of applying an activation function to the weighted sum of inputs for a neuron. This process is fundamental to how neural networks, including Convolutional Neural Networks (CNNs), operate.
Breaking Down the Components
Weighted Sum of Inputs:
- Each neuron in a neural network receives multiple inputs, each multiplied by a corresponding weight.
- A bias term is added to this weighted sum to adjust the output.
Activation Function:
- After calculating the weighted sum (z), an activation function is applied to introduce non-linearity.
- This step determines whether a neuron “fires” or not, effectively deciding if its output should be passed to the next layer.
- Common activation functions include ReLU, Sigmoid, and Tanh.
Role in CNNs:
- In CNNs, activation values are crucial for extracting meaningful patterns from data.
- For example, after convolutional and pooling operations, activation functions like ReLU help retain significant features while discarding irrelevant ones.
Why “A. Weighted Sum of Inputs” Is Correct
The question asks about the definition of an activation value. While activation functions transform the weighted sum into a final output, the term “activation value” generally refers to the raw weighted sum before transformation. This aligns with option A.
Why Other Options Are Incorrect
B. Threshold value: This refers to specific activation functions like binary step, which use thresholds but does not define activation values universally.
C. Main input to neuron: Inputs are just raw data or features; they are not synonymous with activation values.
D. None of the mentioned: Incorrect because option A accurately defines an activation value.
The correct answer is A. Weighted sum of inputs, as it represents the fundamental calculation that occurs before applying an activation function in neural networks. Understanding this concept is essential for grasping how CNNs and other deep learning architectures process data efficiently.
Convolutional Neural Network CNN certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Convolutional Neural Network CNN exam and earn Convolutional Neural Network CNN certification.
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Explanation
Convolutional Neural Network CNN certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Convolutional Neural Network CNN exam and earn Convolutional Neural Network CNN certification.