Implementation of Learning End-to-End Goal-Oriented Dialog with sklearn-like interface using Tensorflow. Tasks are from the bAbl dataset.
git clone [email protected]/rndabzooba/chatbot_memory_network.git
mkdir ./chatbot_memory_network/data/
cd ./chatbot_memory_network/data/
wget https://scontent.xx.fbcdn.net/t39.2365-6/13437784_1766606076905967_221214138_n.tgz
tar xzvf ./13437784_1766606076905967_221214138_n.tgz
cd ../
python single_dialog.py
Train the model
python single_dialog.py --train True --task_id 1 --interactive False
Running a single bAbI task Demo
python single_dialog.py --train False --task_id 1 --interactive True
These files are also a good example of usage.
- tensorflow 0.8
- scikit-learn 0.17.1
- six 1.10.0
- scipy
Unless specified, the Adam optimizer was used.
The following params were used:
- epochs: 200
- learning_rate: 0.01
- epsilon: 1e-8
- embedding_size: 20
| Task | Training Accuracy | Validation Accuracy | Testing Accuracy | Testing Accuracy(OOV) |
|---|---|---|---|---|
| 1 | 99.9 | 99.1 | 99.3 | 76.3 |
| 2 | 100 | 100 | 99.9 | 78.9 |
| 3 | 96.1 | 71.0 | 71.1 | 64.8 |
| 4 | 99.9 | 56.7 | 57.2 | 57.0 |
| 5 | 99.9 | 98.4 | 98.5 | 64.9 |
| 6 | 73.1 | 49.3 | 40.6 | --- |
I didn't play around with the epsilon param in Adam until after my initial results but values of 1.0 and 0.1 seem to help convergence and overfitting.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent