Discover how to calculate the number of parameters in a convolutional layer of a CNN with 100 filters for a 300×300 RGB image. Understand the role of filters and biases in deep learning models.
Table of Contents
Question
Suppose your input is a 300 by 300 color (RGB) image, and you use a convolutional layer with 100 filters that are each 5×5. How many parameters does this hidden layer have (including the bias parameters)?
A. 2501
B. 2600
C. 7500
D. 7600
Answer
D. 7600
Explanation
You have 25×3=75 weights and 1 bias per filter. Given that you have 100 filters, you get 7,600 parameters for this layer.
When dealing with Convolutional Neural Networks (CNNs), calculating the number of parameters in a convolutional layer is crucial for understanding model complexity and computational requirements. In this scenario, you have a 300×300 color (RGB) image input and a convolutional layer with 100 filters, each sized 5×5. Here’s how to compute the total number of parameters:
Filter Parameters
Each filter in a convolutional layer processes all three color channels (RGB), so it requires parameters for each channel.
For a single filter, the number of parameters is calculated as:
Parameters per filter=Filter height×Filter width×Number of channels=5×5×3=75
Total Filter Parameters
With 100 filters, the total number of parameters for all filters is:
Total filter parameters=Parameters per filter×Number of filters=75×100=7500
Bias Parameters
Each filter has an associated bias parameter, adding one more parameter per filter.
Thus, for 100 filters, there are 100 bias parameters.
Total Parameters Including Bias
The total number of parameters in the convolutional layer, including biases, is:
Total parameters=Total filter parameters+Bias parameters=7500+100=7600
Therefore, the correct answer is D. 7600. This calculation helps ensure you understand how each component contributes to the overall parameter count in CNNs, which is vital for designing efficient neural network architectures.
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