nami-act 1.1.1

Official Repository for Nami: an adaptive self regulatory activation function

Nami

Official Repository for Nami — an adaptive activation function with built-in strong regularization capabilities.

---

Nami means "wave" in Japanese — the name comes from its wavy nature in the negative domain. Unlike typical activation functions that tend to a single value, Nami oscillates due to the sin function. It combines this oscillation with the smoothness of tanh. The behavior is controlled by three learnable parameters: w, a, and b.

---

Install

pip install nami-act

Usage

# for torch
from nami import Torch.Nami
nami_torch = Torch.Nami()

# for tensorflow
from nami import TF.Nami
nami_tf = TF.Nami()

# for jax there are two modules 
# one is nami function another is Nami object for Haiku
# if someone uses flax they can simply wrap the functional
# module with flax.nn.Module

from nami.JAX import nami, Nami 

Compatible:

PyTorch
TensorFlow
Jax (needs some fixes)

---

Plots:

Nami vs Others:

• Nami consistently leads in high-impact learning phases (e.g., around LR drops), emphasizing its adaptability.
• This resilience is not observed in traditional activations that either saturate early or destabilize under aggressive scheduling.

Derivative of Nami:

• Nami’s derivative exhibits a non-monotonic, softly saturating profile—a design that diversifies gradient propagation and avoids neuron inactivation.
• Its flexible form enables localized feature modulation, especially beneficial in deep architectures where gradient dynamics are critical.

---

Here is a quick comparison of Nami, Swish and Mish with the same weight initialization on ResNet-18, on CIFAR-10, More Detailed and complex tests are here benchmarks

def seed_everything(seed=42):
    import random, os
    import numpy as np
    import torch

    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    torch.use_deterministic_algorithms(True, warn_only=True)

seed_everything(42)

---

Ran the training for 200 epochs and Nami showed very stable losses troughout the training and especially in the later epochs and that is because of tanh(x * a) in the positive and a * sin(x * w) / b in the negative domain. It has learnable parameters w, a, b, which demand longer runs to learn deeper and complex information from the data.

• w is responsible for maintaining the wave-length, the smaller it is the smoother the gradients are.

• a regulates the spikes of the waves, high waves can capture deeper information, but if it rises too much, it may cause overfitting — that’s where b comes into the picture.

• b tackles overfitting by supressing a's dominance, and increases generalization.

---

Here is a sample benchmark on Resnet18:

Nami: Validation Accuracy 94.81, Training Loss 0.0015, Validation Loss 0.1963

---

Mish: Validation Accuracy 94.09, Training Loss 0.0032, Validation Loss 0.2424

---

Swish/SiLU: Validation Accuracy 94.06, Training Loss 0.0024, Validation Loss 0.2347

---

In conclusion, Nami beats Mish and Swish in both generalization and accuracy! Nami is suitable for Larger and Deeper Networks. And also; for shallow Neural Networks it captures more complex information than any other activation. Despite having three trainable parameters, its computational cost remains minimal — and the convergence is outstanding. For more detailed experiments visit benchmarks.

---

"I'd love to see ML folks fine-tune LLMs or train deep models using Nami, you can share the stats with me here: Gmail, X

---

Thanks for your support :)