Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
End-To-End Memory Network using Tensorflow
| Date | Stars |
|---|---|
| 2026-07-24 | 340 |
| 2026-07-25 | 340 |
| 2026-07-28 | 340 |
| 2026-07-30 | 340 |
| 2026-08-06 | 340 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# MemN2N Implementation of [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895) with sklearn-like interface using Tensorflow. Tasks are from the [bAbl](http://arxiv.org/abs/1502.05698) dataset.  ### Get Started ``` git clone [email protected]:domluna/memn2n.git mkdir ./memn2n/data/ cd ./memn2n/data/ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz tar xzvf ./tasks_1-20_v1-2.tar.gz cd ../ python single.py ``` ### Examples Running a [single bAbI task](./single.py) Running a [joint model on all bAbI tasks](./joint.py) These files are also a good example of usage. ### Requirements * tensorflow 1.0 * scikit-learn 0.17.1 * six 1.10.0 ### Single Task Results 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. Stochastic gradient descent optimizer was used with an annealed learning rate schedule as specified in Section 4.2 of [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895) The following params were used: * epochs: 100 * hops: 3 * embedding_size: 20 Task | Training Accuracy | Validation Accuracy | Testing Accuracy ------|---------------------|-----------------------|------------------ 1 | 1.0 | 1.0 | 1.0 2 | 1.0 | 0.86 | 0.83 3 | 1.0 | 0.64 | 0.54 4 | 1.0 | 0.99 | 0.98 5 | 1.0 | 0.94 | 0.87 6 | 1.0 | 0.97 | 0.92 7 | 1.0 | 0.89 | 0.84 8 | 1.0 | 0.93 | 0.86 9 | 1.0 | 0.86 | 0.90 10 | 1.0 | 0.80 | 0.78 11 | 1.0 | 0.92 | 0.84 12 | 1.0 | 1.0 | 1.0 13 | 0.99 | 0.94 | 0.90 14 | 1.0 | 0.97 | 0.93 15 | 1.0 | 1.0 | 1.0 16 | 0.81 | 0.47 | 0.44 17 | 0.76 | 0.65 | 0.52 18 | 0.97 | 0.96 | 0.88 19 | 0.40 | 0.17 | 0.13 20 | 1.0 | 1.0 | 1.0 ### Joint Training Results Pass: 1,6,9,10,12,13,15,20 Again stochastic gradient descent optimizer was used with an annealed learning rate schedule as specified in Section 4.2 of [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895) The following params were used: * epochs: 60 * hops: 3 * embedding_size: 40 Task | Training Accuracy | Validation Accuracy | Testing Accuracy ------|-------------------|---------------------|------------------- 1 | 1.0 | 0.99 | 0.999 2 | 1.0 | 0.84 | 0.849 3 | 0.99 | 0.72 | 0.715 4 | 0.96 | 0.86 | 0.851 5 | 1.0 | 0.92 | 0.865 6 | 1.0 | 0.97 | 0.964 7 | 0.96 | 0.87 | 0.851 8 | 0.99 | 0.89 | 0.898 9 | 0.99 | 0.96 | 0.96 10 | 1.0 | 0.96 | 0.928 11 | 1.0 | 0.98 | 0.93 12 | 1.0 | 0.98 | 0.982 13 | 0.99 | 0.98 | 0.976 14 | 1.0 | 0.81 | 0.877 15 | 1.0 | 1.0 | 0.983 16 | 0.64 | 0.45 | 0.44 17 | 0.77 | 0.64 | 0.547 18
Excerpt of 4,723 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:48c8320da23f50ae, topic:tensorflow
matched fp:48c8320da23f50ae, topic:nlp