Prototype Trainer 1.0.0.1 <Chrome Quick>

Standard optimizers (Adam, SGD) are included, but the standout feature is the Adaptive Scheduler. It monitors validation loss plateaus and automatically adjusts learning rates—not just on predefined milestones, but in real-time. In version 1.0.0.1, the scheduler now includes a "patience decay" factor, preventing overfitting on noisy datasets.

Installation is straightforward:

pip install prototype-trainer==1.0.0.1

Here is a minimal example training a simple MNIST classifier: prototype trainer 1.0.0.1

from prototype_trainer import Trainer, Dataset
from prototype_trainer.models import MLP

Inspect results: open the generated plots and metrics summary; rerun with adjusted config or start a small sweep.
Iterate until a reproducible prototype meets acceptance criteria (target metric(s) and resource bounds).

In gaming terminology, a "trainer" is a program that runs in the background while a game is active. It intercepts and modifies the game's memory data to enable cheats that are not natively available in the game's menu. Standard optimizers (Adam, SGD) are included, but the