| commit | 4de888e167f0d81da6258e89df233e74c47a2351 | [log] [tgz] |
|---|---|---|
| author | Dmytro Dzhulgakov <dzhulgakov@fb.com> | Wed Jan 11 11:33:39 2017 -0800 |
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Wed Jan 11 11:44:38 2017 -0800 |
| tree | 4853002163dd533492a69671ae8db9a269a31a39 | |
| parent | 4ae5235ec951ad9370448f3703f45176db584424 [diff] |
Add optional gradient on weights for (Sparse)LengthsWeightedSum Summary: It ended up much messier than originally expected. Maybe we should have just hardcode it, but I've tried to be "generic" so far at expense of code readability. The main issue is that for weights computation we need access to original embedding matrix and in sparse case we need to relookup the embeddings to do the dot product with output grads. Thus I'm making weight grad computation optional, controlled by a flag and it triggers invocation of a different backward op that produces both grads at the same time. So far it's implemented only for 'Lengths' version. It'd be straightforward to implement (Un)SortedSegment versions but I haven't done that yet. Reviewed By: kennyhorror Differential Revision: D4388215 fbshipit-source-id: 23132ab7daa1f5eec49233f802af1fe75b469c2b
Caffe2 is a deep learning framework made with expression, speed, and modularity in mind. It is an experimental refactoring of Caffe, and allows a more flexible way to organize computation.
Caffe2 is released under the BSD 2-Clause license.
git clone --recursive https://github.com/bwasti/caffe2.git cd caffe2
brew install automake protobuf mkdir build && cd build cmake .. make
sudo apt-get install libprotobuf-dev protobuf-compiler libatlas-base-dev libgoogle-glog-dev libgtest-dev liblmdb-dev libleveldb-dev libsnappy-dev python-dev python-pip libiomp-dev libopencv-dev libpthread-stubs0-dev cmake
sudo pip install numpy
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
sudo apt-get install git
CUDNN_URL="http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/cudnn-8.0-linux-x64-v5.1.tgz" &&
curl -fsSL ${CUDNN_URL} -O &&
sudo tar -xzf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local &&
rm cudnn-8.0-linux-x64-v5.1.tgz &&
sudo ldconfig
mkdir build && cd build
cmake ..
make
To run the tutorials you'll need ipython-notebooks and matplotlib, which can be installed on OS X with:
brew install matplotlib --with-python3 pip install ipython notebook
Ubuntu 14.04 (GCC)
OS X (Clang)
Options (both Clang and GCC)
BLAS
Other