ELU

#Artificial Neuron #Neural Network #Basics #Activation Function

Both ReLu and Leaky ReLu have discontinuous derivatives. ELU is smooth for first order derivative, i.e., ELU is class $C^1$.

$$ \begin{cases} x, & \text{if }x>=0 \\ \exp(x) - 1, & \text{else.} \end{cases} $$

Visualizations

ELU

Derivative of ELU

Code

def elu(x, alpha):
    return torch.where(x > 0, x, torch.exp(x) -1)

Full code to generate the data used in this article

from torch import nn
import matplotlib.pyplot as plt
import torch
from typing import Union, Optional
from pathlib import Path
import json


def visualize_activation(
    x: torch.Tensor, acti: torch.nn.Module,
    save_path: Optional[Union[str, Path]] = None
) -> dict:
    """Visualize activation function on the domain of x"""

    y = acti(x)

    # Calculate the grad of the activation function
    x = x.clone().requires_grad_()
    acti(x).sum().backward()
    yp = x.grad

    activation_dict = {
        "x": x.detach().numpy().tolist(),
        "y": y.detach().numpy().tolist(),
        "yp": yp.detach().numpy().tolist()
    }

    if save_path is not None:
        if isinstance(save_path, str):
            save_path = Path(save_path)
        save_path.parent.mkdir(parents=True, exist_ok=True)
        with open(save_path, "w") as f:
            json.dump(activation_dict, f, indent=4)

    return activation_dict

class ELU(nn.Module):

    def __init__(self) -> None:
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return torch.where(x > 0, x, torch.exp(x) -1)

    def __str__(self) -> str:
        return f"Activation Function: {super().__str__()}"


if __name__ == "__main__":

    elu = ELU()

    print(elu)

    save_path = "data/activations/elu.json"
    x = torch.linspace(-2, 2, 1000)
    data = visualize_activation(x, elu, save_path=save_path)

    fig, ax = plt.subplots()
    ax.plot(data["x"], data["y"])
    ax.plot(data["x"], data["yp"])
    ax.set_title("ELU")
    plt.show()

    pass