by Graham Neubig, Yoav Goldberg, Chaitanya Malaviya, Austin Matthews, Yusuke Oda, and Pengcheng Yin
These are benchmarks to compare DyNet against several other neural network toolkits: TensorFlow, Theano, and Chainer. It covers four different natural language processing tasks, some of which are only implemented in a subset of the toolkits as they wouldn't be straightforward to implement in the others:
- rnnlm-batch: A recurrent neural network language model with mini-batched training.
- bilstm-tagger: A tagger that runs a bi-directional LSTM and selects a tag for each word.
- bilstm-tagger-withchar: Similar to bilstm-tagger, but uses characer-based embeddings for unknown words.
- treelstm: A text tagger based on tree-structured LSTMs.
The benchmarks can be run by first compiling the
dynet-cpp examples, then running run-tests.sh.
dynet-cpp needs the sequence-ops branch of DyNet to compile.