CNN Parameter Count Calculator
Calculate the number of trainable parameters in a convolutional layer.
Inputs
Total Parameters
73,856
Weight Parameters
73,728
Step by step
Weights: K² × C_in × C_out
3² × 64 × 128
= 73,728
Biases
128
= 128
Total
73,728 + 128
= 73,856
How it works
A convolutional layer has K×K×C_in×C_out weight parameters (kernel size squared times input channels times output filters) plus C_out bias terms. Conv layers are far more parameter-efficient than dense layers due to weight sharing across spatial positions.
Formula
Conv2D Parameters
params = kernel_size^2 × in_channels × out_channels + out_channels
- kernel_size
- Height/width of the convolution kernel
Frequently Asked Questions
Why are conv layers more efficient than dense layers?
A conv layer reuses the same small kernel across all spatial positions (weight sharing), requiring far fewer parameters than connecting every input pixel to every output neuron.
Does kernel size affect parameter count?
Yes — parameters scale with K². A 5×5 kernel has 2.78× more parameters than a 3×3 kernel. This is why modern architectures prefer stacking multiple 3×3 layers over using larger kernels.
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