Implementation of Sequence Generative Adversarial Nets with Policy Gradient

Related tags

Deep LearningSeqGAN
Overview

SeqGAN

Requirements:

  • Tensorflow r1.0.1
  • Python 2.7
  • CUDA 7.5+ (For GPU)

Introduction

Apply Generative Adversarial Nets to generating sequences of discrete tokens.

The illustration of SeqGAN. Left: D is trained over the real data and the generated data by G. Right: G is trained by policy gradient where the final reward signal is provided by D and is passed back to the intermediate action value via Monte Carlo search.

The research paper SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient has been accepted at the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).

We provide example codes to repeat the synthetic data experiments with oracle evaluation mechanisms. To run the experiment with default parameters:

$ python sequence_gan.py

You can change the all the parameters in sequence_gan.py.

The experiment has two stages. In the first stage, use the positive data provided by the oracle model and Maximum Likelihood Estimation to perform supervise learning. In the second stage, use adversarial training to improve the generator.

After running the experiments, you could get the negative log-likelihodd performance saved in save/experiment-log.txt like:

pre-training...
epoch:	0	nll:	10.1716
epoch:	5	nll:	9.42939
epoch:	10	nll:	9.2388
epoch:	15	nll:	9.11899
epoch:	20	nll:	9.13099
epoch:	25	nll:	9.14474
epoch:	30	nll:	9.12539
epoch:	35	nll:	9.13982
epoch:	40	nll:	9.135
epoch:	45	nll:	9.13081
epoch:	50	nll:	9.10678
epoch:	55	nll:	9.10694
epoch:	60	nll:	9.10349
epoch:	65	nll:	9.10403
epoch:	70	nll:	9.07613
epoch:	75	nll:	9.091
epoch:	80	nll:	9.08909
epoch:	85	nll:	9.0807
epoch:	90	nll:	9.08434
epoch:	95	nll:	9.08936
epoch:	100	nll:	9.07443
epoch:	105	nll:	9.08305
epoch:	110	nll:	9.06973
epoch:	115	nll:	9.07058
adversarial training...
epoch:	0	nll:	9.08457
epoch:	5	nll:	9.04511
epoch:	10	nll:	9.03079
epoch:	15	nll:	8.99239
epoch:	20	nll:	8.96401
epoch:	25	nll:	8.93864
epoch:	30	nll:	8.91642
epoch:	35	nll:	8.87761
epoch:	40	nll:	8.88582
epoch:	45	nll:	8.8592
epoch:	50	nll:	8.83388
epoch:	55	nll:	8.81342
epoch:	60	nll:	8.80247
epoch:	65	nll:	8.77778
epoch:	70	nll:	8.7567
epoch:	75	nll:	8.73002
epoch:	80	nll:	8.72488
epoch:	85	nll:	8.72233
epoch:	90	nll:	8.71473
epoch:	95	nll:	8.71163
epoch:	100	nll:	8.70113
epoch:	105	nll:	8.69879
epoch:	110	nll:	8.69208
epoch:	115	nll:	8.69291
epoch:	120	nll:	8.68371
epoch:	125	nll:	8.689
epoch:	130	nll:	8.68989
epoch:	135	nll:	8.68269
epoch:	140	nll:	8.68647
epoch:	145	nll:	8.68066
epoch:	150	nll:	8.6832

Note: this code is based on the previous work by ofirnachum. Many thanks to ofirnachum.

Owner
Lantao Yu
Ph.D. Student at Stanford CS Department
Lantao Yu
GitHub Repository
2021 CCF BDCI 全国信息检索挑战杯（CCIR-Cup）智能人机交互自然语言理解赛道第二名参赛解决方案

2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup) 智能人机交互自然语言理解赛道第二名解决方案 比赛网址: CCIR-Cup-智能人机交互自然语言理解 1.依赖环境： python==3.8 torch==1.7.1+cu110 numpy==1.19.2 transformers=

JinXiang 22 Oct 29, 2022
Submission to Twitter's algorithmic bias bounty challenge

Twitter Ethics Challenge: Pixel Perfect Submission to Twitter's algorithmic bias bounty challenge, by Travis Hoppe (@metasemantic). Abstract We build

Travis Hoppe 4 Aug 19, 2022
Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

Lbl2Vec Lbl2Vec is an algorithm for unsupervised document classification and unsupervised document retrieval. It automatically generates jointly embed

sebis - TUM - Germany 61 Dec 20, 2022
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models arXiv | BibTeX High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach*, Andreas Blattmann*, Dominik Lorenz

CompVis Heidelberg 5.6k Dec 30, 2022
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

dm_control: DeepMind Infrastructure for Physics-Based Simulation. DeepMind's software stack for physics-based simulation and Reinforcement Learning en

DeepMind 3k Dec 31, 2022
This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

Intro This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales Vehicle Sam

39 Jul 21, 2022
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN

Timo Sämann 561 Jan 04, 2023
Official Implementation of SWAD (NeurIPS 2021)

SWAD: Domain Generalization by Seeking Flat Minima (NeurIPS'21) Official PyTorch implementation of SWAD: Domain Generalization by Seeking Flat Minima.

Junbum Cha 97 Dec 20, 2022
Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN Official PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. Prerequisites Python 2.7

SK T-Brain 754 Dec 29, 2022
A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

P-tuning A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''. How to use our code We have released the code

THUDM 562 Dec 27, 2022
Code repo for "Transformer on a Diet" paper

Transformer on a Diet Reference: C Wang, Z Ye, A Zhang, Z Zhang, A Smola. "Transformer on a Diet". arXiv preprint arXiv (2020). Installation pip insta

cgraywang 31 Sep 26, 2021
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our rece

Jaehyeon Kim 1.7k Jan 08, 2023
PyTorch Implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedding (ORAL, MICCAIW 2021)

Small Lesion Segmentation in Brain MRIs with Subpixel Embedding PyTorch implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedd

22 Oct 21, 2022
This is a yolo3 implemented via tensorflow 2.7

YoloV3 - an object detection algorithm implemented via TF 2.x source code In this article I assume you've already familiar with basic computer vision

2 Jan 17, 2022
Post-Training Quantization for Vision transformers.

PTQ4ViT Post-Training Quantization Framework for Vision Transformers. We use the twin uniform quantization method to reduce the quantization error on

Zhihang Yuan 61 Dec 28, 2022
Object Detection with YOLOv3

Object Detection with YOLOv3 Bu projede YOLOv3-608 modeli kullanılmıştır. Requirements Python 3.8 OpenCV Numpy Documentation Yolo ile ilgili detaylı b

Ayşe Konuş 0 Mar 27, 2022
On-device speech-to-index engine powered by deep learning.

On-device speech-to-index engine powered by deep learning.

Picovoice 30 Nov 24, 2022
nfelo: a power ranking, prediction, and betting model for the NFL

nfelo nfelo is a power ranking, prediction, and betting model for the NFL. Nfelo take's 538's Elo framework and further adapts it for the NFL, hence t

6 Nov 22, 2022
This package contains a PyTorch Implementation of IB-GAN of the submitted paper in AAAI 2021

The PyTorch implementation of IB-GAN model of AAAI 2021 This package contains a PyTorch implementation of IB-GAN presented in the submitted paper (IB-

Insu Jeon 9 Mar 30, 2022
Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation (NeurIPS 2021) by Qiming Hu, Xiaojie Guo. Dependencies P

Qiming Hu 31 Dec 20, 2022