How sifat192/activation-function-analysis works
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 Activation functions
Components 15
Activation implementation
- Activation functions
Activation_Function_Analysis.ipynb - Derivatives
Activation_Function_Analysis.ipynb - Modern activations
Activation_Function_Analysis.ipynb - Custom activation
Activation_Function_Analysis.ipynb
Behavior analysis
- Curve visualization
Activation_Function_Analysis.ipynb - Derivative analysis
Activation_Function_Analysis.ipynb - Behavior observations
Activation_Function_Analysis.ipynb
Experiments
- Vanishing gradients
Activation_Function_Analysis.ipynb - Dead neurons
Activation_Function_Analysis.ipynb - Output distributions
Activation_Function_Analysis.ipynb - Efficiency benchmark
Activation_Function_Analysis.ipynb - Analysis results
Activation_Function_Analysis.ipynb
Other components
- Researcher
- NumPy
- Matplotlib
Connections 19
- 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