This repository is the official implementation of the paper entitled "Path Development Network with Finite dimensional Lie Group"

The code has been tested successfully using Python 3.7; thus we suggest using this version or a later version of Python. A typical process for installing the package dependencies involves creating a new Python virtual environment.

To install the required packages, run the following:

pip install .

The soruce code of the development network is in the development subdirectory. We currently provide the implementations for five Lie groups: Special Othorgonal, Real Symplectic, Special Euclidean, Unitary and group of orientation-preserving isometries of the hyperbolic space.

The development layer can be used like standard RNN type models on the time series. For demonstrations, we provide a self contained short example:

from development.nn import development_layer
from development.sp import sp
from development.hyperbolic import hyperbolic
from development.se import se
from development.so import so
import torch

# Create some data
batch, length, input_size = 1, 10, 2
hidden_size = 3
x = torch.rand(batch, length, input_size)

# specify the Lie algebra
param = so  # sp, hyperbolic, se,


# Development layer can directly apply on x like standard RNN model
# Specify development layer with the weigths

dev = development_layer(
    input_size=input_size, hidden_size=hidden_size, param=param, return_sequence=False)
out = dev(x)

The code for reproducing the experiments of the paper can be found in the subdirectories: SpeechCommands, CharTrajectories,sMNIST,CIFAR10,BM_2Sphere and Nbody. Each experiment contains a simple run script to generate data, train and evaluate models. For the first time user, it will takes some time to generate the dataset automatically. See the following instructions for each specific task.

To train individual model with our hyperparameter using the SpeechCommands/run.py script. This script takes one argument to specify the model, i.e.

python3 SpeechCommands/run.py --model MODEL_NAME

where MODEL_NAME is either DEV, signature, LSTM, LSTM_DEV.

To run all experiments in the paper at once, where each model is ran over five iterations:

python3 SpeechCommands/run_all.py

To train a individual model with our hyperparameter using the CharTrajectories/run.py script. This script takes three arguments, i.e.

python3 CharTrajectories/run.py --model MODEL_NAME --param LIE_GROUP --drop_rate DROP_RATE --train_sr TRAIN_SAMPLE_RATE -- --test_sr TEST_SAMPLE_RATE

where MODEL_NAME is either DEV, signature, LSTM, LSTM_DEV,EXPRNN.LIE_GROUP is either SO or Sp. DROP_RATE can take value in [0.3,0.5,0.7],SAMPLE_RATE can take value in [1,0.5] .

To run all experiments in the paper at once, where each model is ran over five iterations:

python3 CharTrajectories/run_all.py

The plots for this experiment in the paper can be found in the notebook CharTrajectories/notebooks/plot_loss.ipynb

To train individual model with our hyperparameter using the sMNIST/run.py script. This script takes two auguments, i.e.

python3 sMNIST/run.py --model MODEL_NAME --permuted PERMUTED

where MODEL_NAME is either LSTM, LSTM_DEV.PERMUTED is either True or False.

To train individual model with our hyperparameter using the CIFAR10/run.py script. This script takes one model augument, i.e.

python3 CIFAT10/run.py --model MODEL_NAME

where MODEL_NAME is either LSTM, LSTM_DEV,EXPRNN.

To train individual model with our hyperparameter using the BM_2Sphere/run.py script. This script takes three auguments, i.e.

python3 BM_2Sphere/run.py --model MODEL_NAME  

where MODEL_NAME is either LSTM_DEV, EXPRNN, LSTM

The visualization for this experiment in the paper can be found in the notebook BM_2Sphere/notebooks/2sphere.ipynb

To train individual model with our hyperparameter using the Nbody/run.py script. This script takes three auguments, i.e.

python3 Nbody/run.py --model MODEL_NAME --param LIE_GROUP --p PREDICTION_STEPs 

where MODEL_NAME is either LSTM, LSTM_DEV.LIE_GROUP is either SO or SE. PREDICTION_STEPs can take value in [10,30,50].

To run all experiments in the paper at once, where each model is ran over five iterations:

python3 Nbody/run_all.py

The visualization for this experiment in the paper can be found in the notebook Nbody/notebooks/visualization.ipynb