Thesis/pytorch-dnc
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love thy neighbour

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ixaxaar 2017-12-20 03:05:01 +05:30
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@ -456,7 +456,7 @@ python ./tasks/argmax_task.py -cuda 0 -lr 0.0001 -rnn_type lstm -memory_type dnc
## General noteworthy stuff ## General noteworthy stuff
1. SDNCs use the [FLANN approximate nearest library](https://www.cs.ubc.ca/research/flann/), with its python binding [pyflann3](https://github.com/primetang/pyflann). 1. SDNCs use the [FLANN approximate nearest neigbhour library](https://www.cs.ubc.ca/research/flann/), with its python binding [pyflann3](https://github.com/primetang/pyflann) and [FAISS](https://github.com/facebookresearch/faiss).
FLANN can be installed either from pip (automatically as a dependency), or from source (e.g. for multithreading via OpenMP): FLANN can be installed either from pip (automatically as a dependency), or from source (e.g. for multithreading via OpenMP):
@ -480,8 +480,7 @@ conda install faiss-gpu -c pytorch
FAISS is much faster, has a GPU implementation and is interoperable with pytorch tensors. FAISS is much faster, has a GPU implementation and is interoperable with pytorch tensors.
We try to use FAISS by default, in absence of which we fall back to FLANN. We try to use FAISS by default, in absence of which we fall back to FLANN.
2. An alternative to FLANN is [FAISS](https://github.com/facebookresearch/faiss), which is much faster and interoperable with torch cuda tensors (but is difficult to distribute, see [dnc/faiss_index.py](dnc/faiss_index.py)). 2. `nan`s in the gradients are common, try with different batch sizes
3. `nan`s in the gradients are common, try with different batch sizes
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