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Compute Conv2D parameter counts

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of convolutional neural network parameterization, specifically how kernel dimensions, input/output channels and an optional bias term determine the number of learnable parameters.

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Compute Conv2D parameter counts

Tesla

Sep 6, 2025, 12:00 AM

easyMachine Learning EngineerTechnical ScreenMachine Learning

Parameter Count for a 2D Convolution Layer

You are given a standard 2D convolution layer with:

Input channels: C_in
Output channels: C_out
Kernel size: (k_h, k_w)
Stride: (s_h, s_w)
Padding: (p_h, p_w)
Optional bias term

Task:

Compute the total number of learnable parameters when the layer includes a bias term.
Compute the total number of learnable parameters when the layer has no bias term.

Assume a standard convolution (groups = 1).

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