mayurak47/reproducibility_challenge

Code for reproducing the paper "Neural Networks Fail to Learn Periodic Functions and How to Fix It" as part of the ML Reproducibility Challenge

[Re] Neural Networks Fail to Learn Periodic Functions and How to Fix It

Code for reproducing the paper "Neural Networks Fail to Learn Periodic Functions and How to Fix It" as part of the ML Reproducibility Challenge

Due to the interactive nature of the experiments, most implementations are provided as Jupyter notebooks.

Description

Extrapolation_experiments.ipynb contains the basic extrapolation experiments on analytical functions with neural networks having different nonlinearities.

In Snake_simple_experiments.ipynb, the snake activation function is visualized, it is shown that snake can regress sin(x) and an MLP is trained on MNIST demonstrating the optimization capability of snake.

Snake_applications.ipynb contains the main experiments of the paper - training a ResNet18 with snake on CIFAR-10, the various regression experiments, and a comparison of a feedforward snake network and a ReLU RNN.

Sinusoid_a_comparison.ipynb demonstrates how different a influences learning.

dcgan.py implements a DCGAN on the MNIST dataset, using the specified nonlinearity in the generator and discriminator networks.

Sentiment_Analysis.ipynb is an attempt at using an LSTM network with the snake activation for sentiment analysis on the IMDB Movie Reviews Dataset.

Usage

Clone the repository with git clone https://github.com/mayurak47/Reproducibility_Challenge.

Preferably create a Python virtual environment (e.g. conda create -n <env_name> python=3.6.9) and activate it. Install the necessary libraries with pip install -r requirements.txt. If you are using a GPU, make sure that PyTorch is installed properly with torch.cuda.is_available() and install the right version from https://pytorch.org/get-started/locally/ otherwise. In case of any inconsistencies or errors, please install the appropriate version of the following packages manually, using pip or conda:

numpy
torch
torchvision
matplotlib
scikit-learn
tqdm
jupyter
pandas
nltk

Run the notebooks, if necessary modifying any (hyper)parameters in the relevant files in data, models, utils.py, or in the notebook itself.

For the GAN experiment, run the commands python dcgan.py --nonlinearity=snake and python dcgan.py --nonlinearity=leakyrelu.