TO BE FIXED:
- This repository is a Chapter for 简单粗暴TensorFlow | A Concise Handbook of TensorFlow
- Book Chapter: Concise Chit Chat - https://huan.github.io/python-concise-chitchat/
make install # install python dependencies
make train # train the model(dataset will be downloaded automatically)
make board # monitor & analyse train process
make chat # chat with it!make docker # this will get into the tensorflow/tensorflow:latest-py3-gpu docker container
make board & # open tensorboard at http://localhost:6006
make train
make chatmake codemake download # download original dataset
make dataset # generate the formated dataset- 简单粗暴TensorFlow | A Concise Handbook of TensorFlow
- Quick guide to run TensorBoard in Google Colab
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
- Neural Machine Translation (seq2seq) Tutorial
- Understand the Difference Between Return Sequences and Return States for LSTMs in Keras
- Practical Guide of RNN in Tensorflow and Keras Introduction
- Sequence Tagging with Tensorflow
- Design and build a chatbot using data from the Cornell Movie Dialogues corpus, using Keras
- PyTorch Chatbot Tutorial
- A ten-minute introduction to sequence-to-sequence learning in Keras
- Use tf.clip_by_global_norm for gradient clipping
- Understanding Python's "with" statement
- How to Use the TimeDistributed Layer for Long Short-Term Memory Networks in Python
- Practical seq2seq - Revisiting sequence to sequence learning, with focus on implementation details
- [Chatbots with Seq2Seq - Learn to build a chatbot using TensorFlow])(http://complx.me/2016-06-28-easy-seq2seq/)
@huan Huan LI <zixia@zixia.net>
- Code & Docs © 2018 - now Huan LI <zixia@zixia.net>
- Code released under the Apache-2.0 License
- Docs released under Creative Commons