A neural network modelling requires an extensive use of activation functions and the correct activation function can help control and model the output of the problem statement.
This repository has a wide collection of various acivation functions and their python implementations in the script activation_functions.py.
pip install -r requirements.txt
The activation functions covered here are:
- Linear
- Tanh
- Sigmoid
- ReLU
- Leaky ReLU
- PReLU
- Softplus
- Binary Step
- Swish
- Elu
- SiLU
- Mish
- Bent Identity
- Gelu
- Arctan
- Le Cun's Tanh
- Biplar Sigmoid
- Logit
This list is non exhaustive.
This repository also contains a script called create_function_plots.py which creates plots for each and every activation function. The plots are saved in the plots directory in the <activation>.png format.
python create_function_plots.py
A few visualizations of the plots of the activation functions are shown below:
Sigmoid ⬇️
Relu ⬇️
Binary Step ⬇️
