Learn how to calculate the total parameters in a fully connected neural network layer with 256 neurons and a 128×128 input. Detailed explanation for CNN certification exam success.
Question
Suppose your input is a 128 by 128 grayscale image, and you are not using a convolutional network. If the first hidden layer has 256 neurons, each one fully connected to the input, how many parameters does this hidden layer have (including the bias parameters)?
A. 4194304
B. 12583168
C. 12582912
D. 4194560
Answer
D. 4194560
Explanation
To calculate the total number of parameters in a fully connected (dense) layer, you need to account for both weights and biases:
Weights: Each neuron in the dense layer is connected to every input pixel. For an input image of size 128×128, the total number of input features is:
Input size=128×128=16,384
Since there are 256 neurons in the hidden layer, each neuron will have 16,384 weights. Therefore, the total number of weights is:
Total weights=16,384×256=4,194,304
Biases: Each neuron has one bias parameter. With 256 neurons in the hidden layer, the total number of biases is:
Total biases=256
Total Parameters: The sum of weights and biases gives the total number of parameters:
Total parameters=(Input size×Number of neurons)+Number of neurons
Substituting the values:
Total parameters=(16,384×256)+256=4,194,304+256=4,194,560
Thus, the total number of parameters in this fully connected layer is 4,194,560.
Why This Matters for CNN Certification Exams:
Understanding parameter calculations is essential for optimizing neural networks and preventing overfitting. This skill demonstrates your ability to analyze model complexity and resource requirements effectively.
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.