Random Seed Generator

Generate a random seed value for reproducible machine learning experiments.

About Random Seed Generator

Random seeds control the initialization and stochastic behavior of ML training runs (weight init, data shuffling, dropout masks), so results can vary meaningfully between seeds even with identical hyperparameters. Running the same experiment across multiple seeds and averaging results reduces the standard error of your performance estimate by a factor of √n, which is why reporting mean ± std across 3-5 seeds is considered best practice for credible benchmarking.

Formula

seed = floor(random() × (max - min + 1)) + min

min: Minimum seed value
max: Maximum seed value
random(): Uniform random number in [0, 1)

Worked example

With the default values below, Random Seed Generator returns Generated Seeds of 1552924742, 390791056, 31490708, 125606179, 92938758. Change any input above and every figure here is recalculated in your browser.

Inputs used

Number of Seeds to Generateaccepts 1–1,000 seeds: 5 seeds
Minimum Valueaccepts 0–1,000,000,000: 0
Maximum Valueaccepts 1–2,147,483,647: 2,147,483,647

Results

Generated Seeds: 1552924742, 390791056, 31490708, 125606179, 92938758
Seed Count: 5
Variance Reduction Factor (√n): 2.24

Step by step

1Seeds generated= 1552924742, 390791056, 31490708, 125606179, 92938758
2Std-error reduction from averaging runs= 2.24×

Assumptions and limits

The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

How many seeds should I use for a reliable benchmark?

3-5 seeds is common for standard benchmarks; for small or noisy datasets, or when claiming a small improvement, use more seeds (5-10) to ensure the effect is not just noise.

Does setting a seed guarantee full reproducibility?

Not always — GPU non-determinism (e.g. cuDNN algorithms), multi-threading, and floating-point summation order can still introduce tiny variations even with a fixed seed unless you enable deterministic modes.

Why not just always use seed 42?

Using a single fixed seed risks cherry-picking a lucky (or unlucky) run; testing across multiple seeds reveals the true variance of your method rather than one sample of it.