Learn what a neuron computes in a Convolutional Neural Network (CNN) and the critical role of linear functions and activation functions in deep learning models.
Question
What does a neuron compute?
A. A neuron computes an activation function followed by a linear function (z = Wx + b)
B. A neuron computes a linear function (z = Wx + b) followed by an activation function
C. A neuron computes a function g that scales the input x linearly (Wx + b)
D. A neuron computes the mean of all features before applying the output to an activation function
Answer
B. A neuron computes a linear function (z = Wx + b) followed by an activation function
Explanation
In the context of neural networks, including Convolutional Neural Networks (CNNs), a neuron performs two main operations:
Linear Combination: First, the neuron computes a weighted sum of its inputs (x) and adds a bias term (b). This is represented mathematically as:
z=W⋅x+b
Here:
- W represents the weights assigned to the inputs.
- b is the bias term that allows the model to shift the activation function.
Non-linear Activation: After calculating z, the neuron applies an activation function g(z), which introduces non-linearity into the model. Common activation functions include ReLU, Sigmoid, or Tanh. The purpose of this step is to enable the network to learn complex patterns and relationships in the data.
Why Option B is Correct
The process starts with a linear computation (z=W⋅x+b) and then applies an activation function (g(z)) to determine the neuron’s output.
This sequence ensures that the network can model both linear and non-linear relationships, which is essential for tasks like image recognition or object detection in CNNs168.
Why Other Options Are Incorrect
Option A: Incorrect because it reverses the order, implying that the activation function is applied first, which is not how neurons operate in neural networks.
Option C: Incorrect because it describes only a linear scaling operation without mentioning the crucial activation step.
Option D: Incorrect because neurons do not compute feature means before applying an activation function; this description does not align with standard neural network operations.
In CNNs and other neural networks, each neuron first computes a linear function (z=Wx+b) and then applies an activation function to introduce non-linearity. This two-step process enables deep learning models to capture complex patterns effectively.
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