Chatbot in 200 lines of code using TensorLayer

Overview

Seq2Seq Chatbot

This is a 200 lines implementation of Twitter/Cornell-Movie Chatbot, please read the following references before you read the code:

Prerequisites

Model


Training

python3 main.py

Results

Query > happy birthday have a nice day
 > thank you so much
 > thank babe
 > thank bro
 > thanks so much
 > thank babe i appreciate it
Query > donald trump won last nights presidential debate according to snap online polls
 > i dont know what the fuck is that
 > i think he was a racist
 > he is not a racist
 > he is a liar
 > trump needs to be president
Comments
  • [WIP] Fix error in PR #16 Inference mode and TF compatibility

    [WIP] Fix error in PR #16 Inference mode and TF compatibility

    Hey @pskrunner14

    Thanks for the great PR! We have rolled back the PR you recently submitted as the PR contains the error. Could you please have a look on it and we can merge it again? You can create a PR from your fork again or directly modify from here.

    Cheers, Luo

    opened by luomai 6
  • No module named 'tensorlayer.models.seq2seq'

    No module named 'tensorlayer.models.seq2seq'

    Can someone share with me how to resolve this error? Thanks.

    Traceback (most recent call last): File "D:\ChatBot\seq2seq-chatbot-master\main.py", line 11, in from tensorlayer.models.seq2seq import Seq2seq ModuleNotFoundError: No module named 'tensorlayer.models.seq2seq'

    opened by geongm 5
  • Change seq2seq import names

    Change seq2seq import names

    Had the #37 problem. It looks like on in current version of tensorlayer import names changed.

    These imports work with tensorflow 2.0.0-beta1 tensorlayer 2.1.0

    opened by egens 4
  • TL2.0

    TL2.0

    Update model compatible with TensorLayer2.0. Rewrite the loss. cross_entropy_seq_with_mask and cross_entropy_seq. Need to run to see if it converges and produce desirable results

    opened by ArnoldLIULJ 3
  • Inference mode and TF compatibility

    Inference mode and TF compatibility

    • Moved Inference code to a function.
    • Added optional arguments including running script in inference mode [usage python main.py --help].
    • Added tqdm progress bar for info while training.
    • Made the code compatible with TF v1.10.0 and TL v1.10.1.
    • Changed tf.contrib.rnn.BasicLSTMCell to tf.nn.rnn_cell.LSTMCell since the former is deprecated.
    • Moved session config to global scope.
    • Refactored code into relevant functions and reordered them so that the higher-level ones appear earlier in the code.
    • Renamed script to main.py for ease of use.
    • Updated README to add training and inference usage commands.
    • Added requirements.txt file.
    • Changed n.npz to model.npz since it is more standard.

    Note: Fixes #12 and #15

    opened by pskrunner14 3
  • Using the Chatbot

    Using the Chatbot

    Hi there,

    I trained the data for a few days and now the samples are returning good results to the predefined "Happy Birthday" and "Trump" requests.

    Great job by you. Thanks so far.

    Do you already have a small python program for using the chatbot? If I write a message, the chatbot should return a single answer.

    Thanks Chris

    opened by cpro90 3
  • Training is taking too much time

    Training is taking too much time

    Training on CPU is taking too much time, so do you have any estimate how much time it will take? I have executed this 12 hours ago and now i am on just "Epoch[2/50] step:[600/2852] loss:5.684645 took:9.62770s". Can you please help me to boost this training.

    opened by aqeellegalinc 3
  • Inference mode and TF compatibility (#16)

    Inference mode and TF compatibility (#16)

    @pskrunner14

    We have rolled back the PR you recently submitted as the PR contains the error. Could you please have a look on it and we can merge it again?

    opened by luomai 2
  • Fixes TL global variables initializer deprecated issue and Code readability

    Fixes TL global variables initializer deprecated issue and Code readability

    Fixed TensorLayer initialize global vars deprecated issue #13, changed learning rate to 0.001 for faster convergence, improved code readability and removed redundant comments and code

    opened by pskrunner14 2
  • Can't import data

    Can't import data

    ModuleNotFoundError Traceback (most recent call last) in () 8 9 ###============= prepare data ---> 10 from data.twitter import data 11 metadata, idx_q, idx_a = data.load_data(PATH='data/twitter/') # Twitter 12 # from data.cornell_corpus import data

    ModuleNotFoundError: No module named 'data.twitter'

    opened by georgexli 2
  • No module named twitter

    No module named twitter

    File "main_simple_seq2seq.py", line 18, in from data.twitter import data ImportError: No module named twitter

    Did I miss some files? Can you please help me?Many thanks^ o^

    opened by MProtoss 1
  • ModuleNotFoundError: No module named 'data.twitter'; 'data' is not a package

    ModuleNotFoundError: No module named 'data.twitter'; 'data' is not a package

    I am trying to write code for Chat Box, but encountering the error "ModuleNotFoundError: No module named 'data.twitter'; 'data' is not a package" when trying to execute "from data.twitter import data".

    Please suggest , how to resolve the issue?

    note: I am working on following environment: Python is 3.6 V Tensorflow : 2.0 Tensorlayer: 2.2 python-twitter

    opened by mhmitalihalder 0
  • How could I get the

    How could I get the "thought vector" using TensorLayer?

    I am using the seq2seq model as an autoencoder. Given a test paragraph, I'd like to get the thought vector (using the terminology in the figure of README.md).

    opened by munichong 0
Releases(0.1)
Owner
TensorLayer Community
A neutral open community to promote AI technology.
TensorLayer Community
End-To-End Optimization of LiDAR Beam Configuration

End-To-End Optimization of LiDAR Beam Configuration arXiv | IEEE Xplore This repository is the official implementation of the paper: End-To-End Optimi

Niclas 30 Nov 28, 2022
ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

ALFRED A Benchmark for Interpreting Grounded Instructions for Everyday Tasks Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han,

ALFRED 204 Dec 15, 2022
Motion planning environment for Sampling-based Planners

Sampling-Based Motion Planners' Testing Environment Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quick

Soraxas 23 Aug 23, 2022
PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA

Soft DTW Loss Function for PyTorch in CUDA This is a Pytorch Implementation of Soft-DTW: a Differentiable Loss Function for Time-Series which is batch

Keon Lee 76 Dec 20, 2022
This is the code for CVPR 2021 oral paper: Jigsaw Clustering for Unsupervised Visual Representation Learning

JigsawClustering Jigsaw Clustering for Unsupervised Visual Representation Learning Pengguang Chen, Shu Liu, Jiaya Jia Introduction This project provid

DV Lab 73 Sep 18, 2022
A semismooth Newton method for elliptic PDE-constrained optimization

sNewton4PDEOpt The Python module implements a semismooth Newton method for solving finite-element discretizations of the strongly convex, linear ellip

2 Dec 08, 2022
AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation A pytorch-version implementation codes of paper:

11 Dec 13, 2022
Group-Free 3D Object Detection via Transformers

Group-Free 3D Object Detection via Transformers By Ze Liu, Zheng Zhang, Yue Cao, Han Hu, Xin Tong. This repo is the official implementation of "Group-

Ze Liu 213 Dec 07, 2022
use machine learning to recognize gesture on raspberrypi

Raspberrypi_Gesture-Recognition use machine learning to recognize gesture on raspberrypi 說明 利用 tensorflow lite 訓練手部辨識模型 分辨 "剪刀"、"石頭"、"布" 之手勢 再將訓練模型匯入

1 Dec 10, 2021
Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency (ICCV2021) Paper Link: https://arxiv.org/abs/2107.11355 This implementation bui

32 Nov 17, 2022
CVPR 2022 "Online Convolutional Re-parameterization"

OREPA: Online Convolutional Re-parameterization This repo is the PyTorch implementation of our paper to appear in CVPR2022 on "Online Convolutional Re

Mu Hu 121 Dec 21, 2022
Python Environment for Bayesian Learning

Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl in

Abhik Shah 103 Jul 14, 2022
Style transfer between images was performed using the VGG19 model

Style transfer between images was performed using the VGG19 model. The necessary codes, libraries and all other information of this project are available below

Onur yılmaz 2 May 09, 2022
Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection

fpn.pytorch Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection Introduction This project inherits the property of our pytorc

Jianwei Yang 912 Dec 21, 2022
Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

Utkarsh Agiwal 1 Feb 03, 2022
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

Meta Research 141 Jan 07, 2023
PyTorch implementation for STIN

STIN This repository contains PyTorch implementation for STIN. Abstract: In single-photon LiDAR, photon-efficient imaging captures the 3D structure of

Yiweins 2 Nov 22, 2022
Open source hardware and software platform to build a small scale self driving car.

Donkeycar is minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions.

Autorope 2.4k Jan 04, 2023
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Facebook Research 338 Dec 29, 2022
Mscp jamf - Build compliance in jamf

mscp_jamf Build compliance in Jamf. This will build the following xml pieces to

Bob Gendler 3 Jul 25, 2022