Struggling with CNN certification exams? Learn how to identify the number of input layers in a neural network. Detailed explanation provided for exam success.
Table of Contents
Question
How many input layers have the following Neural network?
A. 1
B. 2
C. 3
D. 4
Answer
A. 1
Explanation
In the context of neural networks, an input layer refers to the first layer of nodes (neurons) that receives data directly from the external environment. These nodes represent the features or dimensions of the input data (e.g., pixels in an image, numerical values in a dataset).
Looking at the provided images:
- The leftmost column of nodes in both diagrams represents the input layer. This is where raw data enters the network.
- The input layer is followed by one or more hidden layers and ends with an output layer.
Despite multiple layers being present in these diagrams, only one input layer exists, as there is a single point where data initially enters the network. The rest are hidden and output layers, which process and produce predictions based on the input data.
Key Points to Remember:
- Input Layer: The first layer where raw data enters.
- Hidden Layers: Intermediate layers that perform computations.
- Output Layer: The final layer that produces predictions or classifications.
Thus, for both diagrams, the neural network has exactly one input layer, making A. 1 the correct choice.
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.