A simple Tensorflow based library for deep and/or denoising AutoEncoder.

Overview

libsdae - deep-Autoencoder & denoising autoencoder

A simple Tensorflow based library for Deep autoencoder and denoising AE. Library follows sklearn style.

Prerequisities & Support

  • Tensorflow 1.0 is needed.
  • Supports both Python 2.7 and 3.4+ . Inform if it doesn't.

Installing

pip install git+https://github.com/rajarsheem/libsdae.git

Usage and small doc

test.ipynb has small example where both a tiny and a large dataset is used.

from deepautoencoder import StackedAutoEncoder
model = StackedAutoEncoder(dims=[5,6], activations=['relu', 'relu'], noise='gaussian', epoch=[10000,500],
                            loss='rmse', lr=0.007, batch_size=50, print_step=2000)
# usage 1 - encoding same data                           
result = model.fit_transform(x)
# usage 2 - fitting on one dataset and transforming (encoding) on another data
model.fit(x)
result = model.transform(np.random.rand(5, x.shape[1]))

Alt text

Important points:

  • If noise is not given, it becomes an autoencoder instead of denoising autoencoder.
  • dims refers to the dimenstions of hidden layers. (3 layers in this case)
  • noise = (optional)['gaussian', 'mask-0.4']. mask-0.4 means 40% of bits will be masked for each example.
  • x_ is the encoded feature representation of x.
  • loss = (optional) reconstruction error. rmse or softmax with cross entropy are allowed. default is rmse.
  • print_step is the no. of steps to skip between two loss prints.
  • activations can be 'sigmoid', 'softmax', 'tanh' and 'relu'.
  • batch_size is the size of batch in every epoch
  • Note that while running, global loss means the loss on the total dataset and not on a specific batch.
  • epoch is a list denoting the no. of iterations for each layer.

Citing

  • Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion by P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P. Manzagol (Journal of Machine Learning Research 11 (2010) 3371-3408)

Contributing

You are free to contribute by starting a pull request. Some suggestions are:

  • Variational Autoencoders
  • Recurrent Autoencoders.
Owner
Rajarshee Mitra
I work at the intersection of NLU and Machine Learning. Currently, these are my primary areas of interest.
Rajarshee Mitra
Geometric Sensitivity Decomposition

Geometric Sensitivity Decomposition This repo is the official implementation of A Geometric Perspective towards Neural Calibration via Sensitivity Dec

16 Dec 26, 2022
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness This repository contains the code used for the exper

H.R. Oosterhuis 28 Nov 29, 2022
MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Documentation: https://mmgeneration.readthedocs.io/ Introduction English | 简体中文 MMGeneration is a powerful toolkit for generative models, especially f

OpenMMLab 1.3k Dec 29, 2022
PyTorch code for SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised DA

PyTorch Code for SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation Viraj Prabhu, Shivam Khare, Deeks

Viraj Prabhu 46 Dec 24, 2022
Code for NAACL 2021 full paper "Efficient Attentions for Long Document Summarization"

LongDocSum Code for NAACL 2021 paper "Efficient Attentions for Long Document Summarization" This repository contains data and models needed to reprodu

56 Jan 02, 2023
StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

StarGAN v2 - Official PyTorch Implementation StarGAN v2: Diverse Image Synthesis for Multiple Domains Yunjey Choi*, Youngjung Uh*, Jaejun Yoo*, Jung-W

Clova AI Research 3.1k Jan 09, 2023
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dea

MIC-DKFZ 1.2k Jan 04, 2023
Convolutional Neural Network to detect deforestation in the Amazon Rainforest

Convolutional Neural Network to detect deforestation in the Amazon Rainforest This project is part of my final work as an Aerospace Engineering studen

5 Feb 17, 2022
“英特尔创新大师杯”深度学习挑战赛 赛道3:CCKS2021中文NLP地址相关性任务

ccks2021-track3 CCKS2021中文NLP地址相关性任务-赛道三-冠军方案 团队:我的加菲鱼- wodejiafeiyu 初赛第二/复赛第一/决赛第一 前言 19年开始,陆陆续续参加了一些比赛,拿到过一些top,比较懒一直都没分享过,这次比较幸运又拿了top1,打算分享下 分类的任务

shaochenjie 131 Dec 31, 2022
Towards Boosting the Accuracy of Non-Latin Scene Text Recognition

Convolutional Recurrent Neural Network + CTCLoss | STAR-Net Code for paper "Towards Boosting the Accuracy of Non-Latin Scene Text Recognition" Depende

Sanjana Gunna 7 Aug 07, 2022
Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

Realtime Face Anti-Spoofing Detection 🤖 Realtime Face Anti Spoofing Detection with Face Detector to detect real and fake faces Please star this repo

Prem Kumar 86 Aug 03, 2022
Python implementation of O-OFDMNet, a deep learning-based optical OFDM system,

O-OFDMNet This includes Python implementation of O-OFDMNet, a deep learning-based optical OFDM system, which uses neural networks for signal processin

Thien Luong 4 Sep 09, 2022
Code for the published paper : Learning to recognize rare traffic sign

Improving traffic sign recognition by active search This repo contains code for the paper : "Learning to recognise rare traffic signs" How to use this

samsja 4 Jan 05, 2023
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image This repository is an implementation of the method described in the following pap

21 Dec 15, 2022
🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

Deep CORAL A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016' Deep CORAL can learn

Andy Hsu 200 Dec 25, 2022
A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

Layer-wise Relevance Propagation (LRP) in PyTorch Basic unsupervised implementation of Layer-wise Relevance Propagation (Bach et al., Montavon et al.)

Kai Fabi 28 Dec 26, 2022
Self-Supervised Speech Pre-training and Representation Learning Toolkit.

What's New Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site

s3prl 1.6k Jan 08, 2023
Neural Scene Graphs for Dynamic Scene (CVPR 2021)

Implementation of Neural Scene Graphs, that optimizes multiple radiance fields to represent different objects and a static scene background. Learned representations can be rendered with novel object

151 Dec 26, 2022
Detail-Preserving Transformer for Light Field Image Super-Resolution

DPT Official Pytorch implementation of the paper "Detail-Preserving Transformer for Light Field Image Super-Resolution" accepted by AAAI 2022 . Update

50 Jan 01, 2023
ML for NLP and Computer Vision.

Sparrow is our open-source ML product. It runs on Skipper MLOps infrastructure.

Katana ML 2 Nov 28, 2021