Calculate the number of trainable parameters in a convolutional layer.
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.
Conv2D Parameters
params = kernel_size^2 × in_channels × out_channels + out_channels
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.
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.