Skip to content

Navigation Menu

Explore
By company size
By use case
By industry
View all solutions
Topics
- AI
- DevOps
- Security
- Software Development
- View all
Explore
- GitHub Sponsors
  Fund open source developers
- The ReadME Project
  GitHub community articles
Repositories
- Enterprise platform
  AI-powered developer platform
Available add-ons
Pricing

Search code, repositories, users, issues, pull requests...

Search

Clear

Search syntax tips

Provide feedback

We read every piece of feedback, and take your input very seriously.

Include my email address so I can be contacted

Saved searches

Use saved searches to filter your results more quickly

Name

Query

To see all available qualifiers, see our documentation.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

Dismiss alert

higepon / tensorflow_seq2seq_chatbot Public

Notifications You must be signed in to change notification settings
Fork 19
Star 80

Code
Issues 1
Pull requests
Actions
Projects
Wiki
Security
Insights

Additional navigation options

Code
Issues
Pull requests
Actions
Projects
Wiki
Security
Insights

Logs25: Print detailed debug information

Jump to bottom

Higepon Taro Minowa edited this page Jun 4, 2018 · 1 revision

whole reward
sampled reply
inferred reply

Toggle table of contents Pages 69

Home
A Deep Reinforcement Learning Chatbot
Algorithm for Reinforcement Learning
attention_decoder
Beam Search
Beam Search 1.Read the paper
Beam Search 10.Debug beam_search=True
Beam Search 11.Debug beam 2
Beam Search 2.LSTM decode detail
Beam Search 3.Understand Beam Search
Beam Search 4.Visit seq2seq implmentation
Beam Search 5.Revisit Beam Search Discussion
Beam Search 6.How to get probablities
Beam Search 7.Implement Beam Search on Python side
Beam Search 8.Revisit Beam Search Discussion Again
Beam Search 9.Debug attention_decoder
beam_attention_decoder
Deep Reinforcement Learning for Dialogue Generation
embedding_attention_decoder
embedding_attention_seq2seq
Install tensorflow FreeBSD
Log Template
logs0:old
logs10:See if RL works with medium model
logs11:See if RL works with medium model2
logs12:Train seq2seq with Large Data
logs13:Understand Policy Gradient
logs14:Normalize Reward
logs15:See if new RL is working
logs16:RL test shorter reply is better
logs17:RL test len equals 2 is the best
logs18:RL test train seq2seq first
logs19: Random negative reward to avoid 0 loss
logs1:Use easy tf log
logs20: Make a list of how we implement RL for seq2seq
logs21: Steps to make small RL work
logs22:Refactoring ideas
Logs23:Train large seq2seq
Logs24: Run RL on large seq2seq
Logs25: Print detailed debug information
Logs25:Construct real dull response RL
Logs25:Fix sample helper
Logs26: Confirm dull response work for small tweets
Logs27: Mutual Information
Logs28: Mutual Information Observe
Logs29: Wrap up Mutual Information Beam
logs2:Revisit auth on PyDrive
logs30:Show sampled reply for large data
logs31:Get logits and log_prob
logs32:Values to watch for RL
logs33:Observe RL values
logs34:KL penalty
logs35:RL finally works
logs3:How to save model
logs4:See if RL framework is working
logs5:Consolidate logs
logs6:Make RL model work with small model
logs7:Test RL with small model
logs8:Understand policy entropy TO REVISIT
logs9:Fix msec data issue
logs:33 Long RL run
loop_function
Make old chatbot compatible with tensorflow 1.0
Make your chatbot remember information
misc notes
model_with_buckets
Seq2SeqModel
Understand how CS20SI chatbot works
Understand seq2seq lib with attention

Clone this wiki locally

Footer

© 2025 GitHub, Inc.

Footer navigation

Terms
Privacy
Security
Status
Docs
Contact

You can’t perform that action at this time.