Activation functions for CNN (ImageNet)

Activation functions for convolutional neural networks: proposals and experimental study

Algorithms included

This repo contains the code to run experiments for ImageNet with four activation functions that have been used recently for convolutional network models. These are:

• ReLU
• ELU
• ELUs₊₂
• ELUs₊₂L

Installation

Dependencies

This repo basically requires:

• Python (>= 3.6.8)
• munch (== 2.5.0)
• numpy (== 1.19.2)
• sacred (== 0.8.2)
• scikit-learn (== 0.24.1)
• tensorflow-gpu (== 2.3.0)
• scikit-learn (== 0.24.1)

Compilation

To install the requirements, use:

Install for GPU pip install -r requirements.txt

Development

Contributions are welcome. Pull requests are encouraged to be formatted according to PEP8, e.g., using yapf.

Usage

You can run the experiments with ImageNet dataset using:

python train_imagenet224.py

Citation

The paper titled "Activation functions for convolutional neural networks: proposals and experimental study" has been submitted to IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS).

Contributors

Activation functions for convolutional neural networks: proposals and experimental study

• Víctor Manuel Vargas (@victormvy)
• Pedro Antonio Gutiérrez (@pagutierrez)
• Javier Barbero Gómez (@javierbg)
• César Hervás-Martínez ([email protected])