Calculator
Overall layout remains single column. The calculator area uses three columns on large screens, two on smaller screens, and one on mobile.
Example Data Table
Use these example rows to compare common layer setups in image models.
| Layer | Input | Kernel | Stride | Padding | Dilation | Output Channels | Output |
|---|---|---|---|---|---|---|---|
| Conv2D | 224 × 224 × 3 | 7 × 7 | 2 × 2 | 3 × 3 | 1 × 1 | 64 | 112 × 112 × 64 |
| Conv2D | 32 × 32 × 16 | 3 × 3 | 1 × 1 | 1 × 1 | 1 × 1 | 32 | 32 × 32 × 32 |
| Pool2D | 32 × 32 × 32 | 2 × 2 | 2 × 2 | 0 × 0 | 1 × 1 | 32 | 16 × 16 × 32 |
| Transpose2D | 16 × 16 × 64 | 4 × 4 | 2 × 2 | 1 × 1 | 1 × 1 | 32 | 32 × 32 × 32 |
Formula Used
Output height = floor((Input height + 2 × Padding height − Effective kernel height) / Stride height) + 1
Output width = floor((Input width + 2 × Padding width − Effective kernel width) / Stride width) + 1
Effective kernel = Dilation × (Kernel − 1) + 1
Replace the floor operation with ceil when you want pooling windows to round upward.
Output height = (Input height − 1) × Stride height − 2 × Padding height + Effective kernel height + Output padding height
Output width = (Input width − 1) × Stride width − 2 × Padding width + Effective kernel width + Output padding width
Parameters = Kernel height × Kernel width × Input channels × Output channels / Groups, plus output channels when bias is enabled.
How to Use This Calculator
- Select the layer type you want to analyze.
- Enter input height, width, and channels.
- Provide kernel, stride, padding, and dilation values.
- Set output channels for convolution or transposed convolution.
- Enter groups if you are using grouped or depthwise patterns.
- Use output padding only for transposed convolution.
- Enable ceil mode only for pooling when required.
- Press the button to display the result above the form.
- Review the graph, shape summary, parameters, and ratios.
- Export the result or sample table in CSV or PDF format.
FAQs
1) What does this calculator measure?
It computes output height, output width, output channels, effective kernel size, parameter count, and approximate forward operations for common CNN layer types.
2) Why can output size become negative or zero?
That happens when the input is too small for the chosen kernel, dilation, and padding combination. Reduce the kernel, reduce dilation, or increase padding or input size.
3) Does pooling change channel count?
Typical 2D pooling changes spatial dimensions only. The channel count usually stays equal to the input channel count unless another operation follows it.
4) What is an effective kernel?
It is the size a dilated filter occupies across the input. A 3 × 3 kernel with dilation 2 behaves like a 5 × 5 footprint.
5) Why do grouped convolutions matter here?
Grouped convolutions reduce parameter count and compute because each filter only connects to part of the input channels. Input and output channels must remain divisible by groups.
6) When should I use output padding?
Use output padding only with transposed convolution when you need a precise upsampled dimension. It helps resolve size ambiguity but must stay smaller than stride.
7) Is the operation count exact?
No. It is a practical approximation for comparing layer cost. Actual runtime depends on library kernels, hardware, memory access, precision, and fusion.
8) Can I use this for model planning?
Yes. It is useful for checking shape consistency, estimating feature map growth, comparing architectural choices, and preventing dimension mismatches before implementation.