The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory

Overview

Simple-DMA

a simple Dual Memory Architecture for classifications.

based on the paper Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors written by Sang-Woo Lee1, Chung-Yeon Lee1, Dong Hyun Kwak2, Jiwon Kim3, Jeonghee Kim3, and Byoung-Tak Zhang1,2.

The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory

CNN architecture:

CNN Architecture

DMA architecture:

DMA

The Code:

main.py - the Dual Memory architecture model training and running.
CNN-full_dataset.py - running the CNN on the entire dataset inorder to see how well it works.
CNN_retrainning_model.py - running the CNN on 10 split datasets to see the catastrophic forgetting phenomenon.
cnn.py - the class for creating a cnn.
model.py - the class for creating the Dual Memory model.
Bqueue.py - the class for managing the long term memory.
Plotter.py - the class for plotting the graphs of the scores from the DMA snd CNN. 

The libraries we used:

Tensorflow.keras - for the nueral networks
sklearn - for the preprocessing of the data, shuffling of the data, splitting the data to train and test
  	  and for the linear model.
logging - for writing to the output window
numpy - for processing the data
copy - for copying the model
shutil - for getting the terminal columns.
tqdm - for printing the process bar in the output window.
matplotlib.pyplot - for plotting the graphs.

RUN

In order to run the DMA just open it in pycharm, make sure you have all the libraries installed.

with the scores of the CNN_retrainning_model.py that you recieve after running it you plot the graph of the catastrophic forgetting
that we have presented in class - ithe plot happenes automatically.

with the scores of the main.py that you recieve after running it you plot the graph of the final results that we have presented in class 
and the scores of the DMA vs the scores of the CNN - the plot happenes automatically.
FinRL­-Meta: A Universe for Data­-Driven Financial Reinforcement Learning. 🔥

FinRL-Meta: A Universe of Market Environments. FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users

AI4Finance Foundation 543 Jan 08, 2023
The mini-AlphaStar (mini-AS, or mAS) - mini-scale version (non-official) of the AlphaStar (AS)

A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II.

Ruo-Ze Liu 216 Jan 04, 2023
Identifying Stroke Indicators Using Rough Sets

Identifying Stroke Indicators Using Rough Sets With the spirit of reproducible research, this repository contains all the codes required to produce th

Muhammad Salman Pathan 0 Jun 09, 2022
基于深度强化学习的原神自动钓鱼AI

原神自动钓鱼AI由YOLOX, DQN两部分模型组成。使用迁移学习,半监督学习进行训练。 模型也包含一些使用opencv等传统数字图像处理方法实现的不可学习部分。

4.2k Jan 01, 2023
A Data Annotation Tool for Semantic Segmentation, Object Detection and Lane Line Detection.(In Development Stage)

Data-Annotation-Tool How to Run this Tool? To run this software, follow the steps: git clone https://github.com/Autonomous-Car-Project/Data-Annotation

TiVRA AI 13 Aug 18, 2022
Definition of a business problem according to Wilson Lower Bound Score and Time Based Average Rating

Wilson Lower Bound Score, Time Based Rating Average In this study I tried to calculate the product rating and sorting reviews more accurately. I have

3 Sep 30, 2021
A curated list of awesome deep long-tailed learning resources.

A curated list of awesome deep long-tailed learning resources.

vanint 210 Dec 25, 2022
Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Wonjong Jang 8 Nov 01, 2022
SPTAG: A library for fast approximate nearest neighbor search

SPTAG: A library for fast approximate nearest neighbor search SPTAG SPTAG (Space Partition Tree And Graph) is a library for large scale vector approxi

Microsoft 4.3k Jan 01, 2023
This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

0 Feb 02, 2022
Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

46 Nov 09, 2022
HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

Methods HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method) Dynamically selecting the best propagation method for each node

Yong 7 Dec 18, 2022
A Python module for parallel optimization of expensive black-box functions

blackbox: A Python module for parallel optimization of expensive black-box functions What is this? A minimalistic and easy-to-use Python module that e

Paul Knysh 426 Dec 08, 2022
[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Robot Action Primitives (RAPS) This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action

Murtaza Dalal 55 Dec 27, 2022
A python interface for training Reinforcement Learning bots to battle on pokemon showdown

The pokemon showdown Python environment A Python interface to create battling pokemon agents. poke-env offers an easy-to-use interface for creating ru

Haris Sahovic 184 Dec 30, 2022
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

Single-Image-Reflection-Removal-Beyond-Linearity Paper Single Image Reflection Removal Beyond Linearity. Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, G

Qiang Wen 51 Jun 24, 2022
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners This repository is built upon BEiT, thanks very much! Now, we on

Zhiliang Peng 2.3k Jan 04, 2023
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer

Multimedia Research 484 Dec 29, 2022
Pytorch implementation of CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation"

MUST-GAN Code | paper The Pytorch implementation of our CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generat

TianxiangMa 46 Dec 26, 2022