seq2seq-chatbot

A sequence2sequence chatbot with TensorFlow.

Chatting with a trained model

To chat with a trained model from the model directory:

(Batch files are only available for windows as of now. For mac and linux users see instructions below for python console.)

For console chat:

1. Run chat_console_best_weights_training.bat or chat_console_best_weights_validation.bat

For web chat:

1. Run chat_web_best_weights_training.bat or chat_web_best_weights_validation.bat

2. Open a browser to the URL indicated by the server console, followed by /chat_ui.html. This is typically: http://localhost:8080/chat_ui.html

To chat with a trained model from a python console:

1. Set console working directory to the seq2seq-chatbot directory. This directory should have the models and datasets directories directly within it.

2. Run chat.py with the model checkpoint path:

run chat.py models\dataset_name\model_name\checkpoint.ckpt

For example, to chat with the trained cornell movie dialog model trained_model_v2:

1. Download and unzip trained_model_v2 into the seq2seq-chatbot/models/cornell_movie_dialog folder

2. Set console working directory to the seq2seq-chatbot directory

3. Run:

run chat.py models\cornell_movie_dialog\trained_model_v2\best_weights_training.ckpt

The result should look like this:

Training a model

To train a model from a python console:

1. Configure the hparams.json file to the desired training hyperparameters

2. Set console working directory to the seq2seq-chatbot directory. This directory should have the models and datasets directories directly within it.

3. To train a new model, run train.py with the dataset path:

run train.py --datasetdir=datasets\dataset_name

Or to resume training an existing model, run train.py with the model checkpoint path:

run train.py --checkpointfile=models\dataset_name\model_name\checkpoint.ckpt

For example, to train a new model on the cornell movie dialog dataset with default hyperparameters:

1. Set console working directory to the seq2seq-chatbot directory

2. Run:

run train.py --datasetdir=datasets\cornell_movie_dialog

The result should look like this:

Visualizing a model in TensorBoard

TensorBoard is a great tool for visualizing what is going on under the hood when a TensorFlow model is being trained.

To start TensorBoard from a terminal:

tensorboard --logdir=model_dir

Where model_dir is the path to the directory where the model checkpoint file is. For example, to view the trained cornell movie dialog model trained_model_v2:

tensorboard --logdir=models\cornell_movie_dialog\trained_model_v2

Dependencies

The following python packages are used in seq2seq-chatbot: (excluding packages that come with Anaconda)

• TensorFlow

  pip install --upgrade tensorflow

  For GPU support: (See here for full GPU install instructions including CUDA and cuDNN)

  pip install --upgrade tensorflow-gpu

• jsonpickle

  pip install --upgrade jsonpickle

• flask 0.12.4 and flask-restful (required to run the web interface)

  pip install flask==0.12.4
  pip install --upgrade flask-restful

Relevant papers

1. Sequence to Sequence Learning with Neural Networks