Efficient Neural Network Implementation of the Universemachine

Authors: Chun-Hao To, Ethan Nadler

Requirements

We recommend using python3 and a virtual env. See instructions here.

virtualenv -p python3 .env
source .env/bin/activate
pip install -r requirements.txt

When you're done working on the project, deactivate the virtual environment with deactivate.

Task

Given an n-body simulation, predict the stellar mass function of the same universe.

##Build dataset

python build_dataset.py --data_dir data/subdir --output_dir data/output

Start

3. Train your experiment. Simply run

python train.py --data_dir data/experiment1.1/datavector_original_split/ --model_dir ./experiments/test/ --restore_file best

It will instantiate a model and train it on the training set following the hyperparameters specified in params.json. It will also evaluate some metrics on the validation set.

4. Random forest. simply run

python bach_resnetsearch.py