Implementation of End-To-End Memory Networks with sklearn-like interface using Tensorflow. Tasks are from the bAbl dataset.
git clone git@github.com:domluna/memn2n.git
cd memn2n
python single.py
Running a single bAbI task
Running a joint model on all bAbI tasks
These files are also a good example of usage.
- tensorflow 0.8
- scikit-learn 0.17.1
- six 1.10.0
For a task to pass it has to meet 95%+ testing accuracy. Measured on single tasks on the 1k data.
pass: 1,4,12,15,20
Several other tasks have 80%+ testing accuracy.
Unless specified, the Adam optimizer was used.
The following params were used:
- epochs: 200
- learning_rate: 0.01
- epsilon: 1e-8
- embedding_size: 20
A joint model was also run with the following params:
- epochs: 100
- learning_rate: 0.01
- epsilon: 1.0
- embedding_size: 40
| Task | Training Accuracy | Validation Accuracy | Testing Accuracy |
|---|---|---|---|
| 1 | 0.97 | 0.95 | 0.96 |
| 2 | 0.84 | 0.68 | 0.61 |
| 3 | 0.5 | 0.4 | 0.3 |
| 4 | 0.93 | 0.94 | 0.93 |
| 5 | 0.91 | 0.76 | 0.81 |
| 6 | 0.78 | 0.8 | 0.72 |
| 7 | 0.87 | 0.86 | 0.8 |
| 8 | 0.82 | 0.8 | 0.77 |
| 9 | 0.76 | 0.73 | 0.72 |
| 10 | 0.71 | 0.66 | 0.63 |
| 11 | 0.9 | 0.87 | 0.89 |
| 12 | 0.93 | 0.92 | 0.92 |
| 13 | 0.93 | 0.88 | 0.93 |
| 14 | 0.88 | 0.87 | 0.76 |
| 15 | 1.0 | 1.0 | 1.0 |
| 16 | 0.57 | 0.42 | 0.46 |
| 17 | 0.7 | 0.61 | 0.57 |
| 18 | 0.93 | 0.96 | 0.9 |
| 19 | 0.12 | 0.07 | 0.09 |
| 20 | 1.0 | 1.0 | 1.0 |
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.