Predicting Pseudo Random Values Using Convolutional Neural Networks
Quick Links
Repository Overview (trimmed):
├───ContributionImage
│ └─── Contains Dockerfile with a tutorial on how to set up this project with Jupyter Lab.
├───DemoImage
│ └─── Contains Dockerfile with a tutorial on how to set up this project with cli.
└───WorkSpace
├───Milestones
│ └─── Contains milestones that provide updated progress on the project.
├───Paper
│ └─── Contains the final paper written describing everything from the proposal to the results of the research.
├───Project Proposal
│ └─── Contains the initial document for our proposed research.
└───ProjectCore
├─── main.py ... Entry point for executing experiments via CLI
├─── main.ipynb ... Entry point for executing experiments via Jupyter Notebook
├─── other folders containing PRNG results
├───Core
├─── Contains files that implement the core functionality of this project.
Read more about each one at the top of each file.
How to explore / run the code
Before choosing your option, read through the tutorial under option 1. This tutorial provides the best introduction for running the project in addition to more information about the code itself.
Option 1: The Demo Image (Reduced Docker Image with only the dependencies). Recommended to run the Demo.
Option 2: The Contribution Image (Jupyter Lab on Docker). Recommended for contributors.
We recommend you read: more information about the code itself
Option 3: 'simple' install
Note: platform-specific tooling issues might occur. With Windows, you will likely have to download the pre-compiled version of different packages (like SciPy and others) from here: Windows Binaries. For Windows, we recommend the Docker Demo Image or the larger Contribution Image. This will get you started down the right path if you insist on this option.
git clone https://github.com/CSCI4850/S20-team7-project.git
cd S20-team7-project
pip install -r requirements.txt
cd ./WorkSpace/ProjectCore/
python3 main.py
We recommend you read: more information about the code itself