Count parameters across modules and architecture layers accurately. Use presets, notes, and validation for confidence. Download clean reports as CSV or PDF anytime instantly.
| Layer / Module | Type | Inputs | Outputs | Parameter count |
|---|---|---|---|---|
| Dense_1 | dense | 256 | 128 | 32,896 |
| Conv_3x3 | conv2d | 3x3, inC=32 | outC=64 | 18,496 |
| BatchNorm_64 | batchnorm | channels=64 | - | 128 |
| Custom_Module | manual | Engineering estimate | 12,000 | |
Parameter counts translate architecture choices into measurable complexity for engineering teams. In control, vision, and telemetry pipelines, larger counts increase calibration workload, verification time, and compute demand. A dense stage grows as in_units × out_units, and optional bias adds one parameter per output. Comparing counts across modules helps select efficient designs, set acceptance thresholds, and avoid late redesign when hardware memory, bandwidth, or power budgets are exceeded. Guides tradeoffs.
Separating trainable and non-trainable totals improves auditability. Trainable parameters are values you identify, tune, or fit, and they drive sensitivity to data quality and regularization. Non-trainable elements can store running statistics, masks, or fixed coefficients that still affect reproducibility. By toggling buffer inclusion, you can match summaries that count only weights, or compute full state to serialize for deterministic inference across devices and sessions. This distinction shows what can change.
Reliable counts depend on disciplined dimensions and consistent conventions. Dense layers require correct input and output units; convolution layers require kernel height, kernel width, input channels, and output channels. Swapping channels or forgetting a bias flag can change totals by orders of magnitude. The live per-layer table allows rapid validation, highlights outliers, and links formulas to design documents through meaningful names, so teams can review changes with confidence. Repeat checks.
Operational planning benefits from converting parameters into storage estimates. The calculator multiplies total parameters by bytes per parameter for formats like float32, bfloat16, float16, or int8. For example, 10,000,000 parameters occupy about 40 MB at float32 and 20 MB at 16-bit. A memory multiplier approximates extra state used by optimizers or runtime caches, supporting capacity planning for GPUs, edge accelerators, and firmware partitions. Use this to size hardware.
Treat parameter counting as a governance control, not a one-off check. Export CSV or PDF reports at design gates, attach them to minutes, and baseline totals before merges. When a component changes, the table shows exactly where growth occurred, enabling targeted optimization instead of broad cuts. Over time, a history of counts helps correlate complexity with defect rates, power draw, and maintenance cost across product revisions. Track deltas quarterly, consistently.
A parameter is a stored numeric value that defines a module’s behavior, such as weights, biases, embedding tables, or recurrent matrices. The tool totals trainable values and, optionally, non-trainable buffers for a clearer engineering inventory.
Bias adds one extra value per output unit or output channel. In dense layers it adds out_units, and in Conv2D it adds out_ch. If a design omits bias due to normalization or symmetry, disable it for accurate counts.
Include buffers when you need the full serialized state, such as deployments that store running statistics or fixed state across sessions. Exclude buffers when you want a summary aligned with trainable weights only.
Yes. Use formula rows for standard building blocks and manual rows for custom DSP blocks, lookup tables, or hardware-specific stages. Manual entries keep the report complete when exact internal counts are not available yet.
They use standard gate-based formulas that approximate common implementations. Differences can occur with projection layers, peepholes, fused kernels, or framework-specific packing. If your implementation differs, enter that block as a manual module.
Parameter count is structural and depends on dimensions. Data type only changes bytes per parameter, which drives storage and memory footprint estimates. Changing float32 to int8 reduces size, not the number of parameters.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.