# sifat192/activation-function-analysis architecture

> Architecture diagram of the GitHub repository sifat192/activation-function-analysis, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/sifat192/activation-function-analysis
- Repository: https://github.com/sifat192/activation-function-analysis

## Overview

This is an educational Jupyter notebook for implementing and comparing neural-network activation functions. A researcher follows its experiments from function and derivative analysis through gradient problems, output distributions, and timing benchmarks, ending with modern and custom activations. The README documents these phases and the NumPy/Matplotlib stack, but the notebook source was not sampled, so internal call order and specific library interactions cannot be verified; the graph reflects documented experimental relationships only.

Main entry points: [`Activation_Function_Analysis.ipynb`](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb) (Activation functions)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_implement["Activation implementation"]
  node_implementations["Activation functions"]
  node_derivatives["Derivatives"]
  node_modern["Modern activations"]
  node_custom["Custom activation"]
end

subgraph group_analysis["Behavior analysis"]
  node_curves["Curve visualization"]
  node_derivative_analysis["Derivative analysis"]
  node_observations["Behavior observations"]
end

subgraph group_experiments["Experiments"]
  node_gradient["Vanishing gradients"]
  node_dead_neurons["Dead neurons"]
  node_distributions["Output distributions"]
  node_benchmark["Efficiency benchmark"]
  node_results["Analysis results"]
end

node_researcher(("Researcher"))
node_numpy["NumPy"]
node_matplotlib["Matplotlib"]

node_researcher -->|"runs notebook"| node_implementations
node_implementations -->|"uses"| node_numpy
node_implementations -->|"derives"| node_derivatives
node_implementations -->|"includes"| node_modern
node_implementations -->|"extends"| node_custom
node_implementations -->|"compares"| node_curves
node_derivatives -->|"supports"| node_derivative_analysis
node_curves -->|"renders with"| node_matplotlib
node_derivative_analysis -->|"informs"| node_observations
node_observations -->|"examines"| node_gradient
node_observations -->|"examines"| node_dead_neurons
node_implementations -->|"analyzes"| node_distributions
node_implementations -->|"times"| node_benchmark
node_gradient -->|"contributes to"| node_results
node_dead_neurons -->|"contributes to"| node_results
node_distributions -->|"contributes to"| node_results
node_benchmark -->|"contributes to"| node_results
node_modern -->|"informs"| node_results
node_custom -->|"evaluates"| node_results

click node_implementations "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_derivatives "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_modern "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_custom "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_curves "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_derivative_analysis "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_observations "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_gradient "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_dead_neurons "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_distributions "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_benchmark "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"
click node_results "https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_implementations,node_derivatives,node_modern,node_custom toneBlue
class node_curves,node_derivative_analysis,node_observations toneAmber
class node_gradient,node_dead_neurons,node_distributions,node_benchmark,node_results toneMint
class node_researcher,node_numpy,node_matplotlib toneIndigo
```

## Components

### Activation implementation

- [Activation functions](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Derivatives](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Modern activations](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Custom activation](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`

### Behavior analysis

- [Curve visualization](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Derivative analysis](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Behavior observations](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`

### Experiments

- [Vanishing gradients](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Dead neurons](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Output distributions](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Efficiency benchmark](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`
- [Analysis results](https://github.com/sifat192/activation-function-analysis/blob/main/Activation_Function_Analysis.ipynb): `Activation_Function_Analysis.ipynb`

### Other components

- **Researcher**
- **NumPy**
- **Matplotlib**

## Connections

- Researcher → Activation functions: runs notebook
- Activation functions → NumPy: uses
- Activation functions → Derivatives: derives
- Activation functions → Modern activations: includes
- Activation functions → Custom activation: extends
- Activation functions → Curve visualization: compares
- Derivatives → Derivative analysis: supports
- Curve visualization → Matplotlib: renders with
- Derivative analysis → Behavior observations: informs
- Behavior observations → Vanishing gradients: examines
- Behavior observations → Dead neurons: examines
- Activation functions → Output distributions: analyzes
- Activation functions → Efficiency benchmark: times
- Vanishing gradients → Analysis results: contributes to
- Dead neurons → Analysis results: contributes to
- Output distributions → Analysis results: contributes to
- Efficiency benchmark → Analysis results: contributes to
- Modern activations → Analysis results: informs
- Custom activation → Analysis results: evaluates