Convolution Feature Map Calculator

Formula Used

The effective kernel includes spacing created by dilation. Each output uses weighted input values, bias, and activation. True convolution flips both kernel axes before multiplication.

How to Use This Calculator

Enter the input matrix using one row per line. Add a rectangular kernel with matching row formatting. Choose movement, padding, dilation, activation, and display settings.

Press the calculation button to generate the feature map. Review dimensions, statistics, table values, and heatmap colors. Change one setting between runs for clearer comparisons.

Understanding Feature Maps

Convolution feature maps reveal where learned filters detect useful visual patterns. Each output cell summarizes one local matrix neighborhood. Strong responses often highlight edges, textures, corners, or repeated structures.

Core Convolution Process

A kernel slides across the input using selected movement steps. Every overlap multiplies aligned values before summing their combined contribution. Bias and activation then shape the final response at each location.

Kernel Design Choices

Small kernels capture local details while preserving efficient computation. Larger kernels examine broader context but require more parameters and operations. Asymmetric kernels can emphasize horizontal, vertical, or directional patterns effectively.

Stride Effects

Stride controls how far the kernel moves between calculations. A larger stride reduces output size and computational cost considerably. However, aggressive movement may skip subtle signals within neighboring regions.

Padding Behavior

Padding adds artificial values around the original matrix boundaries. Valid padding uses only positions fully covered by the kernel. Same padding usually preserves dimensions when stride equals one exactly.

Dilation Controls

Dilation spaces kernel elements apart without increasing parameter counts. This expands the receptive field while retaining compact kernel dimensions. Larger dilation can capture distant relationships within structured feature patterns.

Cross Correlation Versus Convolution

Many neural libraries apply cross correlation but call it convolution. Cross correlation keeps kernel orientation unchanged during sliding calculations. True convolution flips the kernel horizontally and vertically before processing.

Activation Options

Activation functions transform raw responses into more useful representations. ReLU removes negative values and promotes sparse positive feature signals. Sigmoid and tanh compress values into bounded nonlinear output ranges.

Interpreting Heatmaps

Heatmap colors show relative response strength across output locations. Bright regions usually indicate stronger agreement with the selected kernel. Always compare colors with displayed numbers before drawing final conclusions.

Choosing Inputs

Use meaningful matrices that represent images, signals, or learned features. Scale differences can dominate multiplication and distort comparisons between regions. Normalized inputs often produce more stable and interpretable feature responses.

Diagnosing Output Shape

Output dimensions depend on input size, effective kernel, and padding. Stride and dilation further change how many valid placements remain. Review calculated dimensions before comparing feature maps across experiments carefully.

Practical Experimentation

Begin with simple edge kernels and small integer matrices. Change one option at a time to isolate its effect. Record output changes to understand convolution behavior with confidence.

Advanced Testing

Compare several kernels using identical inputs for fairness. Review summary statistics beside each generated heatmap. Combined evidence exposes unstable or unexpected response patterns.

Model Relevance

Feature maps connect formulas with practical learning behavior. Early layers detect simple patterns before deeper combinations. Visualization strengthens debugging and architecture decisions across complex projects.