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Repository: clab/rnng
Branch: master
Commit: 439950aa30ce
Files: 176
Total size: 11.5 MB
Directory structure:
gitextract_eii7yk68/
├── .gitignore
├── CMakeLists.txt
├── README.md
├── clusters-train-berk.txt
├── cmake/
│ └── FindEigen3.cmake
├── cnn/
│ ├── .gitignore
│ ├── .gitmodules
│ ├── .travis.yml
│ ├── CMakeLists.txt
│ ├── INSTALL.md
│ ├── LICENSE
│ ├── MEM.notes
│ ├── README.md
│ ├── TODO.cnn
│ ├── cmake/
│ │ ├── FindCNN.cmake
│ │ └── FindEigen3.cmake
│ ├── cnn/
│ │ ├── CMakeLists.txt
│ │ ├── aligned-mem-pool.cc
│ │ ├── aligned-mem-pool.h
│ │ ├── c2w.h
│ │ ├── cfsm-builder.cc
│ │ ├── cfsm-builder.h
│ │ ├── cnn-helper.h
│ │ ├── cnn.cc
│ │ ├── cnn.h
│ │ ├── conv.cc
│ │ ├── conv.h
│ │ ├── cuda.cc
│ │ ├── cuda.h
│ │ ├── deep-lstm.cc
│ │ ├── deep-lstm.h
│ │ ├── devices.cc
│ │ ├── devices.h
│ │ ├── dict.cc
│ │ ├── dict.h
│ │ ├── dim.cc
│ │ ├── dim.h
│ │ ├── except.h
│ │ ├── exec.cc
│ │ ├── exec.h
│ │ ├── expr.cc
│ │ ├── expr.h
│ │ ├── fast-lstm.cc
│ │ ├── fast-lstm.h
│ │ ├── functors.h
│ │ ├── gpu-kernels.h
│ │ ├── gpu-ops.cu
│ │ ├── gpu-ops.h
│ │ ├── grad-check.cc
│ │ ├── grad-check.h
│ │ ├── graph.cc
│ │ ├── graph.h
│ │ ├── gru.cc
│ │ ├── gru.h
│ │ ├── hsm-builder.cc
│ │ ├── hsm-builder.h
│ │ ├── init.cc
│ │ ├── init.h
│ │ ├── lstm.cc
│ │ ├── lstm.h
│ │ ├── mem.cc
│ │ ├── mem.h
│ │ ├── model.cc
│ │ ├── model.h
│ │ ├── mp.cc
│ │ ├── mp.h
│ │ ├── nodes-common.cc
│ │ ├── nodes.cc
│ │ ├── nodes.h
│ │ ├── param-nodes.cc
│ │ ├── param-nodes.h
│ │ ├── random.h
│ │ ├── rnn-state-machine.cc
│ │ ├── rnn-state-machine.h
│ │ ├── rnn.cc
│ │ ├── rnn.h
│ │ ├── saxe-init.cc
│ │ ├── saxe-init.h
│ │ ├── shadow-params.cc
│ │ ├── shadow-params.h
│ │ ├── simd-functors.h
│ │ ├── tensor.cc
│ │ ├── tensor.h
│ │ ├── tests/
│ │ │ ├── test_edges.cc
│ │ │ ├── test_init.cc
│ │ │ └── test_utils.h
│ │ ├── timing.h
│ │ ├── training.cc
│ │ └── training.h
│ ├── config.h.cmake
│ ├── examples/
│ │ ├── CMakeLists.txt
│ │ ├── embed-cl.cc
│ │ ├── encdec.cc
│ │ ├── example-data/
│ │ │ ├── clusters-hsm.txt
│ │ │ ├── dev-hsm.txt
│ │ │ ├── dev-poi.txt
│ │ │ ├── fin-dev.txt
│ │ │ ├── fin-toy.txt
│ │ │ ├── fin-words-dev.txt
│ │ │ ├── fin-words.txt
│ │ │ ├── fin.txt
│ │ │ ├── seg-sup.dev.txt
│ │ │ ├── textcat.txt
│ │ │ ├── train-hsm.txt
│ │ │ └── train-poi.txt
│ │ ├── mlc.cc
│ │ ├── nlm.cc
│ │ ├── poisson-regression.cc
│ │ ├── read-write.cc
│ │ ├── rnnlm-aevb.cc
│ │ ├── rnnlm-batch.cc
│ │ ├── rnnlm-cfsm.cc
│ │ ├── rnnlm-givenbag.cc
│ │ ├── rnnlm-mp.cc
│ │ ├── rnnlm.cc
│ │ ├── rnnlm.h
│ │ ├── rnnlm2.cc
│ │ ├── segrnn-sup.cc
│ │ ├── skiprnnlm.cc
│ │ ├── tag-bilstm.cc
│ │ ├── textcat.cc
│ │ ├── tok-embed.cc
│ │ ├── xor-batch-lookup.cc
│ │ ├── xor-batch.cc
│ │ ├── xor-xent.cc
│ │ └── xor.cc
│ ├── pycnn/
│ │ ├── INSTALL
│ │ ├── makefile
│ │ ├── pycnn.pxd
│ │ ├── pycnn.pyx
│ │ ├── pycnn_viz.py
│ │ ├── setup.py
│ │ └── setup_gpu.py
│ ├── pyexamples/
│ │ ├── attention.py
│ │ ├── bilstmtagger.py
│ │ ├── cpu_vs_gpu.py
│ │ ├── minibatch.py
│ │ ├── rnnlm.py
│ │ ├── rnnlm_transduce.py
│ │ ├── tutorials/
│ │ │ ├── API.ipynb
│ │ │ ├── RNNs.ipynb
│ │ │ └── tutorial-1-xor.ipynb
│ │ ├── util.py
│ │ └── xor.py
│ ├── rnnlm/
│ │ ├── CMakeLists.txt
│ │ ├── README.md
│ │ ├── install-examples.sh
│ │ └── lm.cc
│ └── tests/
│ ├── CMakeLists.txt
│ ├── README.md
│ ├── test-cnn.cc
│ └── test-nodes.cc
├── get_dictionary.py
├── get_oracle.py
├── get_oracle_gen.py
├── get_unkified_terminals.py
├── interpreting-rnng/
│ ├── README.md
│ └── nt-parser-gen-attention-gated-stack-only.cc
├── nt-parser/
│ ├── CMakeLists.txt
│ ├── compressed-fstream.h
│ ├── eval.cc
│ ├── eval.h
│ ├── nt-parser-gen.cc
│ ├── nt-parser.cc
│ ├── oracle.cc
│ ├── oracle.h
│ ├── pretrained.cc
│ └── pretrained.h
├── remove_dev_unk.py
├── sample_input_chinese.txt
├── sample_input_english.txt
└── utils/
├── add-fake-preterms-for-eval.pl
├── cut-corpus.pl
├── is-estimate-marginal-llh.pl
├── remove_dev_unk.py
└── replace-unks-in-trees.pl
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Compiled Object files
*.slo
*.lo
*.o
*.obj
# Precompiled Headers
*.gch
*.pch
# Compiled Dynamic libraries
*.so
*.dylib
*.dll
# Fortran module files
*.mod
# Compiled Static libraries
*.lai
*.la
*.a
*.lib
# Executables
*.exe
*.out
*.app
================================================
FILE: CMakeLists.txt
================================================
project(cnn)
cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
set(CMAKE_CXX_FLAGS "-Wall -std=c++11 -O3 -g")
enable_testing()
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/cnn)
set(WITH_EIGEN_BACKEND 1)
# look for Boost
set(Boost_REALPATH ON)
find_package(Boost COMPONENTS program_options iostreams serialization REQUIRED)
include_directories(${Boost_INCLUDE_DIR})
set(LIBS ${LIBS} ${Boost_LIBRARIES})
# look for Eigen
find_package(Eigen3 REQUIRED)
include_directories(${EIGEN3_INCLUDE_DIR})
#configure_file(${CMAKE_CURRENT_SOURCE_DIR}/config.h.cmake ${CMAKE_CURRENT_BINARY_DIR}/config.h)
add_subdirectory(cnn/cnn)
add_subdirectory(nt-parser)
# add_subdirectory(cnn/examples)
================================================
FILE: README.md
================================================
# Recurrent Neural Network Grammars
Code for the [Recurrent Neural Network Grammars](https://arxiv.org/abs/1602.07776) paper (NAACL 2016), by Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith, after the Corrigendum (last two pages on the ArXiv version of the paper). The code is written in C++.
# Citation
@inproceedings{dyer-rnng:16,
author = {Chris Dyer and Adhiguna Kuncoro and Miguel Ballesteros and Noah A. Smith},
title = {Recurrent Neural Network Grammars},
booktitle = {Proc. of NAACL},
year = {2016},
}
# Prerequisites
* A C++ compiler supporting the [C++11 language standard](https://en.wikipedia.org/wiki/C%2B%2B11)
* [Boost](http://www.boost.org/) libraries
* [Eigen](http://eigen.tuxfamily.org) (latest development release)
* [CMake](http://www.cmake.org/)
* [EVALB](http://nlp.cs.nyu.edu/evalb/) (latest version. IMPORTANT: please put the EVALB folder on the same directory as `get_oracle.py` and `sample_input_chinese.txt` to ensure compatibility)
# Build instructions
Assuming the latest development version of Eigen is stored at: /opt/tools/eigen-dev
mkdir build
cd build
cmake -DEIGEN3_INCLUDE_DIR=/opt/tools/eigen-dev ..
make -j2
# Sample input format:
`sample_input_english.txt` (English PTB) and `sample_input_chinese.txt` (Chinese CTB)
# Oracles
The oracle converts the bracketed phrase-structure tree into a sequence of actions.
The script to obtain the oracle also converts singletons in the training set and unknown words in the dev and test set into a fine-grained set of 'UNK' symbols
### Obtaining the oracle for the discriminative model
python get_oracle.py [training file] [training file] > train.oracle
python get_oracle.py [training file] [dev file] > dev.oracle
python get_oracle.py [training file] [test file] > test.oracle
### Obtaining the oracle for the generative model
python get_oracle_gen.py [training file] [training file] > train_gen.oracle
python get_oracle_gen.py [training file] [dev file] > dev_gen.oracle
python get_oracle_gen.py [training file] [test file] > test_gen.oracle
### Removing sentences that start with '#' from the oracle
In the training file of the PTB, there is one sentence that starts with '#'. This breaks the oracle format. To fix this, remove lines 2251062-2251105 from train.oracle and lines 2183922-2183963 from `train_gen.oracle` (the exact lines are applicable when using the same PTB version and when using Sections 02-21 for training, which is the standard training split). The corresponding tree entry that needs to be removed is the following: `(NP (NP (QP (# #) (CD 200) (CD million))) (PP (IN of) (NP (NP (JJ undated) (JJ variable-rate) (NNS notes)) (VP (VBN priced) (PP (IN at) (NP (JJ par))) (PP (IN via) (NP (NNP Merill) (NNP Lynch) (NNP International) (NNP Ltd)))))) (. .))`, which is on line 33571 of the PTB training file.
# Discriminative Model
The discriminative variant of the RNNG is used as a proposal distribution for decoding the generative model (although it can also be used for decoding on its own). To save time we recommend training both models in parallel.
On the English PTB dataset the discriminative model typically converges after about 3 days on a single-core CPU device.
### Training the discriminative model
nohup build/nt-parser/nt-parser --cnn-mem 1700 -x -T [training_oracle_file] -d [dev_oracle_file] -C [original_dev_file (PTB bracketed format, see sample_input_english.txt)] -P -t --pretrained_dim [dimension of pre-trained word embedding] -w [pre-trained word embedding] --lstm_input_dim 128 --hidden_dim 128 -D 0.2 > log.txt
IMPORTANT: please run the command at the same folder where `remove_dev_unk.py` is located.
If not using pre-trained word embedding, then remove the `--pretrained_dim` and `-w` flags.
The training log is printed to `log.txt` (including information on where the parameter file for the model is saved to, which is used for decoding under the -m option below)
### Decoding with discriminative model
build/nt-parser/nt-parser --cnn-mem 1700 -x -T [training_oracle_file] -p [test_oracle_file] -C [original_test_file (PTB bracketed format, see sample_input_english.txt)] -P --pretrained_dim [dimension of pre-trained word embedding] -w [pre-trained word embedding] --lstm_input_dim 128 --hidden_dim 128 -m [parameter file] > output.txt
Note: the output will be stored in `/tmp/parser_test_eval.xxxx.txt` and the parser will output F1 score calculated with EVALB with COLLINS.prm option. The `xxxx` in `/tmp/parser_test_eval.xxxx.txt` can be obtained from the process ID of the decoding process (this is NOT the process ID of the training process).
In contrast, the `parameter file` (following the -m in the decoding command above) can be obtained from `log.txt` file that logs the training process (e.g. there will be a line near the top of `log.txt` that for instance will say `PARAMETER FILE: ntparse_0_2_32_128_16_128-pid138938.params`. In that case, the decoding option should specify `-m ntparse_0_2_32_128_16_128-pid138938.params`)
If training was done using pre-trained word embedding (by specifying the -w and --pretrained\_dim options) or POS tags (-P option), then decoding must alo use the exact same options used for training.
# Generative Model
The generative model achieved state of the art results, and decoding is done using sampled trees from the trained discriminative model
For the best results the generative model takes about 7 days to converge.
### Obtain the word clusters file
The class-factored softmax requires word clusters. This is obtained using Brown clustering. The file `clusters-train-berk.txt` can be used here, although this can also be obtained from scratch using [Percy Liang's Brown clustering code] (https://github.com/percyliang/brown-cluster). The following command was used to generate `clusters-train-berk.txt`:
python get_unkified_terminals.py -p train_gen.oracle > train-terms.txt
After that, using the Brown clustering code:
./wcluster --text ~/rnng_public_test_newest/rnng/train-terms.txt --c 140
The resulting file is under `train-terms-c140-p1.out/paths`, which can be renamed to `clusters-train-berk.txt`.
### Training the generative model
nohup build/nt-parser/nt-parser-gen -x -T [training_oracle_generative] -d [dev_oracle_generative] -t --clusters clusters-train-berk.txt --input_dim 256 --lstm_input_dim 256 --hidden_dim 256 -D 0.3 > log_gen.txt
The training log is printed to `log_gen.txt`, including information on where the parameters of the model is saved to, which is used for decoding later.
# Decoding with the generative model
Decoding with the generative model requires sample trees from the trained discriminative model
### Sampling trees from the discriminative model
build/nt-parser/nt-parser --cnn-mem 1700 -x -T [training_oracle_file] -p [test_oracle_file] -C [original_test_file (PTB bracketed format, see sample_input_english.txt)] -P --pretrained_dim [dimension of pre-trained word embedding] -w [pre-trained word embedding] --lstm_input_dim 128 --hidden_dim 128 -m [parameter file of trained discriminative model] --alpha 0.8 -s 100 > test-samples.props
important parameters
* s = # of samples (all reported results used 100)
* alpha = posterior scaling (since this is a proposal, a higher entropy distribution is probably good, so a value < 1 is sensible. All reported results used 0.8)
### Prepare samples for likelihood evaluation
utils/cut-corpus.pl 3 test-samples.props > test-samples.trees
### Evaluate joint likelihood under generative model
build/nt-parser/nt-parser-gen -x -T [training_oracle_generative] --clusters clusters-train-berk.txt --input_dim 256 --lstm_input_dim 256 --hidden_dim 256 -p test-samples.trees -m [parameters file from the trained generative model, see log_gen.txt] > test-samples.likelihoods
### Estimate marginal likelihood (final step to get language modeling ppl)
utils/is-estimate-marginal-llh.pl 2416 100 test-samples.props test-samples.likelihoods > llh.txt 2> rescored.trees
* 100 = # of samples
* 2416 = # of sentences in test set
* `rescored.trees` will contain the samples reranked by p(x,y)
The file `llh.txt` would contain the final language modeling perplexity after marginalization (see the last lines of the file)
### Compute generative model parsing accuracy (final step to get parsing accuracy from the generative model)
utils/add-fake-preterms-for-eval.pl rescored.trees > rescored.preterm.trees
utils/replace-unks-in-trees.pl [Discriminative oracle for the test file] rescored.preterm.trees > hyp.trees
utils/remove_dev_unk.py [gold trees on the test set (same format as sample_input_english.txt)] hyp.trees > hyp_final.trees
EVALB/evalb -p COLLINS.prm [gold trees on the test set (same format as sample_input_english.txt)] hyp_final.trees > parsing_result.txt
The file `parsing_result.txt` contains the final parsing accuracy using EVALB
# Pretrained Generative Model
As the generative model takes a while to train, a pretrained model is available here: https://drive.google.com/open?id=1E0lrR5sypgiYsYQmIdYN6TKVds22ApXa (version as of Apr 16 2018)
Since CNN/DyNet relies on Boost to serialize and save/load the model, using this pretrained model requires using boost version 1.60.0 to compile the system. It is important to specify the same training set and clusters in order to load the model (otherwise the model cannot be loaded). Here is the command for training the pretrained model:
build/nt-parser/nt-parser-gen -x -T /usr1/home/akuncoro/rnng-dataset/english/train-gen.oracle -d /usr1/home/akuncoro/rnng-dataset/english/dev-gen.oracle -t --clusters /usr1/home/akuncoro/rnng-dataset/english/clusters-train-berk.txt --input_dim 256 --lstm_input_dim 256 --hidden_dim 256 -D 0.3
The clusters that we used is the "clusters-train-berk.txt", and please contact us to get access to the oracle due to PTB licensing issues.
# Pretrained Discriminative Model Tree Samples For Reranking
To get tree samples from the discriminative models (in order to use the generative model to rescore these samples), please find "test-samples.props" here: https://drive.google.com/open?id=0Bz1ZN2dBHG1dTTZXc2FBOXZObm8
# Acknowledgments
We thank Daniel Fried for discovering a bug in the discriminative model.
# Contact
If there are any issues, please let us know at adhiguna.kuncoro [ AT SYMBOL ] gmail.com, miguel.ballesteros [AT SYMBOL] ibm.com, and cdyer [AT SYMBOL] cs.cmu.edu
# License
This software is released under the terms of the [Apache License, Version 2.0] (http://www.apache.org/licenses/LICENSE-2.0)
================================================
FILE: clusters-train-berk.txt
================================================
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01110110
gitextract_eii7yk68/
├── .gitignore
├── CMakeLists.txt
├── README.md
├── clusters-train-berk.txt
├── cmake/
│ └── FindEigen3.cmake
├── cnn/
│ ├── .gitignore
│ ├── .gitmodules
│ ├── .travis.yml
│ ├── CMakeLists.txt
│ ├── INSTALL.md
│ ├── LICENSE
│ ├── MEM.notes
│ ├── README.md
│ ├── TODO.cnn
│ ├── cmake/
│ │ ├── FindCNN.cmake
│ │ └── FindEigen3.cmake
│ ├── cnn/
│ │ ├── CMakeLists.txt
│ │ ├── aligned-mem-pool.cc
│ │ ├── aligned-mem-pool.h
│ │ ├── c2w.h
│ │ ├── cfsm-builder.cc
│ │ ├── cfsm-builder.h
│ │ ├── cnn-helper.h
│ │ ├── cnn.cc
│ │ ├── cnn.h
│ │ ├── conv.cc
│ │ ├── conv.h
│ │ ├── cuda.cc
│ │ ├── cuda.h
│ │ ├── deep-lstm.cc
│ │ ├── deep-lstm.h
│ │ ├── devices.cc
│ │ ├── devices.h
│ │ ├── dict.cc
│ │ ├── dict.h
│ │ ├── dim.cc
│ │ ├── dim.h
│ │ ├── except.h
│ │ ├── exec.cc
│ │ ├── exec.h
│ │ ├── expr.cc
│ │ ├── expr.h
│ │ ├── fast-lstm.cc
│ │ ├── fast-lstm.h
│ │ ├── functors.h
│ │ ├── gpu-kernels.h
│ │ ├── gpu-ops.cu
│ │ ├── gpu-ops.h
│ │ ├── grad-check.cc
│ │ ├── grad-check.h
│ │ ├── graph.cc
│ │ ├── graph.h
│ │ ├── gru.cc
│ │ ├── gru.h
│ │ ├── hsm-builder.cc
│ │ ├── hsm-builder.h
│ │ ├── init.cc
│ │ ├── init.h
│ │ ├── lstm.cc
│ │ ├── lstm.h
│ │ ├── mem.cc
│ │ ├── mem.h
│ │ ├── model.cc
│ │ ├── model.h
│ │ ├── mp.cc
│ │ ├── mp.h
│ │ ├── nodes-common.cc
│ │ ├── nodes.cc
│ │ ├── nodes.h
│ │ ├── param-nodes.cc
│ │ ├── param-nodes.h
│ │ ├── random.h
│ │ ├── rnn-state-machine.cc
│ │ ├── rnn-state-machine.h
│ │ ├── rnn.cc
│ │ ├── rnn.h
│ │ ├── saxe-init.cc
│ │ ├── saxe-init.h
│ │ ├── shadow-params.cc
│ │ ├── shadow-params.h
│ │ ├── simd-functors.h
│ │ ├── tensor.cc
│ │ ├── tensor.h
│ │ ├── tests/
│ │ │ ├── test_edges.cc
│ │ │ ├── test_init.cc
│ │ │ └── test_utils.h
│ │ ├── timing.h
│ │ ├── training.cc
│ │ └── training.h
│ ├── config.h.cmake
│ ├── examples/
│ │ ├── CMakeLists.txt
│ │ ├── embed-cl.cc
│ │ ├── encdec.cc
│ │ ├── example-data/
│ │ │ ├── clusters-hsm.txt
│ │ │ ├── dev-hsm.txt
│ │ │ ├── dev-poi.txt
│ │ │ ├── fin-dev.txt
│ │ │ ├── fin-toy.txt
│ │ │ ├── fin-words-dev.txt
│ │ │ ├── fin-words.txt
│ │ │ ├── fin.txt
│ │ │ ├── seg-sup.dev.txt
│ │ │ ├── textcat.txt
│ │ │ ├── train-hsm.txt
│ │ │ └── train-poi.txt
│ │ ├── mlc.cc
│ │ ├── nlm.cc
│ │ ├── poisson-regression.cc
│ │ ├── read-write.cc
│ │ ├── rnnlm-aevb.cc
│ │ ├── rnnlm-batch.cc
│ │ ├── rnnlm-cfsm.cc
│ │ ├── rnnlm-givenbag.cc
│ │ ├── rnnlm-mp.cc
│ │ ├── rnnlm.cc
│ │ ├── rnnlm.h
│ │ ├── rnnlm2.cc
│ │ ├── segrnn-sup.cc
│ │ ├── skiprnnlm.cc
│ │ ├── tag-bilstm.cc
│ │ ├── textcat.cc
│ │ ├── tok-embed.cc
│ │ ├── xor-batch-lookup.cc
│ │ ├── xor-batch.cc
│ │ ├── xor-xent.cc
│ │ └── xor.cc
│ ├── pycnn/
│ │ ├── INSTALL
│ │ ├── makefile
│ │ ├── pycnn.pxd
│ │ ├── pycnn.pyx
│ │ ├── pycnn_viz.py
│ │ ├── setup.py
│ │ └── setup_gpu.py
│ ├── pyexamples/
│ │ ├── attention.py
│ │ ├── bilstmtagger.py
│ │ ├── cpu_vs_gpu.py
│ │ ├── minibatch.py
│ │ ├── rnnlm.py
│ │ ├── rnnlm_transduce.py
│ │ ├── tutorials/
│ │ │ ├── API.ipynb
│ │ │ ├── RNNs.ipynb
│ │ │ └── tutorial-1-xor.ipynb
│ │ ├── util.py
│ │ └── xor.py
│ ├── rnnlm/
│ │ ├── CMakeLists.txt
│ │ ├── README.md
│ │ ├── install-examples.sh
│ │ └── lm.cc
│ └── tests/
│ ├── CMakeLists.txt
│ ├── README.md
│ ├── test-cnn.cc
│ └── test-nodes.cc
├── get_dictionary.py
├── get_oracle.py
├── get_oracle_gen.py
├── get_unkified_terminals.py
├── interpreting-rnng/
│ ├── README.md
│ └── nt-parser-gen-attention-gated-stack-only.cc
├── nt-parser/
│ ├── CMakeLists.txt
│ ├── compressed-fstream.h
│ ├── eval.cc
│ ├── eval.h
│ ├── nt-parser-gen.cc
│ ├── nt-parser.cc
│ ├── oracle.cc
│ ├── oracle.h
│ ├── pretrained.cc
│ └── pretrained.h
├── remove_dev_unk.py
├── sample_input_chinese.txt
├── sample_input_english.txt
└── utils/
├── add-fake-preterms-for-eval.pl
├── cut-corpus.pl
├── is-estimate-marginal-llh.pl
├── remove_dev_unk.py
└── replace-unks-in-trees.pl
SYMBOL INDEX (1036 symbols across 118 files)
FILE: cnn/cnn/aligned-mem-pool.h
function namespace (line 7) | namespace cnn {
function zero_all (line 47) | void zero_all() {
FILE: cnn/cnn/c2w.h
function namespace (line 11) | namespace cnn {
FILE: cnn/cnn/cfsm-builder.cc
type cnn (line 8) | namespace cnn {
function is_ws (line 12) | inline bool is_ws(char x) { return (x == ' ' || x == '\t'); }
function not_ws (line 13) | inline bool not_ws(char x) { return (x != ' ' && x != '\t'); }
function Expression (line 26) | Expression NonFactoredSoftmaxBuilder::neg_log_softmax(const Expression...
function Expression (line 78) | Expression ClassFactoredSoftmaxBuilder::neg_log_softmax(const Expressi...
FILE: cnn/cnn/cfsm-builder.h
function namespace (line 10) | namespace cnn {
FILE: cnn/cnn/cnn-helper.h
function namespace (line 8) | namespace cnn {
FILE: cnn/cnn/cnn.cc
type cnn (line 11) | namespace cnn {
function VariableIndex (line 80) | VariableIndex ComputationGraph::add_input(real s) {
function VariableIndex (line 87) | VariableIndex ComputationGraph::add_input(const real* ps) {
function VariableIndex (line 94) | VariableIndex ComputationGraph::add_input(const Dim& d, const vector<f...
function VariableIndex (line 101) | VariableIndex ComputationGraph::add_input(const Dim& d, const vector<f...
function VariableIndex (line 108) | VariableIndex ComputationGraph::add_parameters(Parameters* p) {
function VariableIndex (line 117) | VariableIndex ComputationGraph::add_const_parameters(Parameters* p) {
function VariableIndex (line 125) | VariableIndex ComputationGraph::add_lookup(LookupParameters* p, const ...
function VariableIndex (line 134) | VariableIndex ComputationGraph::add_lookup(LookupParameters* p, unsign...
function VariableIndex (line 143) | VariableIndex ComputationGraph::add_lookup(LookupParameters* p, const ...
function VariableIndex (line 152) | VariableIndex ComputationGraph::add_lookup(LookupParameters* p, const ...
function VariableIndex (line 162) | VariableIndex ComputationGraph::add_const_lookup(LookupParameters* p, ...
function VariableIndex (line 172) | VariableIndex ComputationGraph::add_const_lookup(LookupParameters* p, ...
function VariableIndex (line 180) | VariableIndex ComputationGraph::add_const_lookup(LookupParameters* p, ...
function VariableIndex (line 188) | VariableIndex ComputationGraph::add_const_lookup(LookupParameters* p, ...
function Tensor (line 209) | const Tensor& ComputationGraph::incremental_forward() { return ee->inc...
function Tensor (line 210) | const Tensor& ComputationGraph::forward() { return ee->forward(); }
function Tensor (line 211) | const Tensor& ComputationGraph::get_value(VariableIndex i) { return ee...
function Tensor (line 212) | const Tensor& ComputationGraph::get_value(const expr::Expression& e) {...
FILE: cnn/cnn/cnn.h
function namespace (line 26) | namespace cnn {
function VariableIndex (line 197) | VariableIndex ComputationGraph::add_function(const std::initializer_list...
function VariableIndex (line 206) | VariableIndex ComputationGraph::add_function(const std::initializer_list...
function VariableIndex (line 215) | VariableIndex ComputationGraph::add_function(const T& arguments) {
FILE: cnn/cnn/conv.cc
type cnn (line 16) | namespace cnn {
function string (line 18) | string AddVectorToAllColumns::as_string(const vector<string>& arg_name...
function Dim (line 24) | Dim AddVectorToAllColumns::dim_forward(const vector<Dim>& xs) const {
function string (line 56) | string FoldRows::as_string(const vector<string>& arg_names) const {
function Dim (line 62) | Dim FoldRows::dim_forward(const vector<Dim>& xs) const {
function string (line 106) | string Conv1DNarrow::as_string(const vector<string>& arg_names) const {
function Dim (line 112) | Dim Conv1DNarrow::dim_forward(const vector<Dim>& xs) const {
function string (line 184) | string Conv1DWide::as_string(const vector<string>& arg_names) const {
function Dim (line 190) | Dim Conv1DWide::dim_forward(const vector<Dim>& xs) const {
function string (line 260) | string KMaxPooling::as_string(const vector<string>& arg_names) const {
function Dim (line 266) | Dim KMaxPooling::dim_forward(const vector<Dim>& xs) const {
FILE: cnn/cnn/conv.h
function Node (line 8) | struct AddVectorToAllColumns : public Node {
function Node (line 20) | struct KMaxPooling : public Node {
FILE: cnn/cnn/cuda.cc
type cnn (line 10) | namespace cnn {
function RemoveArgs (line 14) | static void RemoveArgs(int& argc, char**& argv, int& argi, int n) {
function Initialize_GPU (line 23) | vector<Device*> Initialize_GPU(int& argc, char**& argv) {
FILE: cnn/cnn/cuda.h
function namespace (line 33) | namespace cnn {
FILE: cnn/cnn/deep-lstm.cc
type cnn (line 13) | namespace cnn {
function Expression (line 91) | Expression DeepLSTMBuilder::add_input_impl(int prev, const Expression&...
FILE: cnn/cnn/deep-lstm.h
function namespace (line 10) | namespace cnn {
FILE: cnn/cnn/devices.cc
type cnn (line 9) | namespace cnn {
FILE: cnn/cnn/devices.h
function DeviceType (line 10) | enum class DeviceType {CPU, GPU};
FILE: cnn/cnn/dict.cc
type cnn (line 9) | namespace cnn {
function ReadSentence (line 11) | std::vector<int> ReadSentence(const std::string& line, Dict* sd) {
function ReadSentencePair (line 23) | void ReadSentencePair(const std::string& line, std::vector<int>* s, Di...
FILE: cnn/cnn/dict.h
function namespace (line 19) | namespace cnn {
FILE: cnn/cnn/dim.cc
type cnn (line 7) | namespace cnn {
function ostream (line 9) | ostream& operator<<(ostream& os, const Dim& d) {
function ostream (line 19) | ostream& operator<<(ostream& os, const vector<Dim>& ds) {
FILE: cnn/cnn/dim.h
function namespace (line 14) | namespace boost { namespace serialization { class access; } }
function namespace (line 16) | namespace cnn {
FILE: cnn/cnn/except.h
function namespace (line 6) | namespace cnn {
FILE: cnn/cnn/exec.cc
type cnn (line 7) | namespace cnn {
function Tensor (line 15) | const Tensor& SimpleExecutionEngine::forward() {
function Tensor (line 20) | const Tensor& SimpleExecutionEngine::forward(VariableIndex i) {
function Tensor (line 25) | const Tensor& SimpleExecutionEngine::get_value(VariableIndex i) {
function Tensor (line 33) | const Tensor& SimpleExecutionEngine::incremental_forward() {
function Tensor (line 38) | const Tensor& SimpleExecutionEngine::incremental_forward(VariableIndex...
FILE: cnn/cnn/exec.h
function namespace (line 6) | namespace cnn {
FILE: cnn/cnn/expr.cc
type cnn (line 8) | namespace cnn { namespace expr {
type expr (line 8) | namespace expr {
function Expression (line 12) | Expression input(ComputationGraph& g, real s) { return Expression(&g...
function Expression (line 13) | Expression input(ComputationGraph& g, const real *ps) { return Expre...
function Expression (line 14) | Expression input(ComputationGraph& g, const Dim& d, const vector<flo...
function Expression (line 15) | Expression input(ComputationGraph& g, const Dim& d, const vector<flo...
function Expression (line 16) | Expression const_parameter(ComputationGraph& g, Parameters* p) { ret...
function Expression (line 17) | Expression parameter(ComputationGraph& g, Parameters* p) { return Ex...
function Expression (line 18) | Expression lookup(ComputationGraph& g, LookupParameters* p, unsigned...
function Expression (line 19) | Expression lookup(ComputationGraph& g, LookupParameters* p, const un...
function Expression (line 20) | Expression lookup(ComputationGraph& g, LookupParameters* p, const ve...
function Expression (line 21) | Expression lookup(ComputationGraph& g, LookupParameters* p, const ve...
function Expression (line 22) | Expression const_lookup(ComputationGraph& g, LookupParameters* p, un...
function Expression (line 23) | Expression const_lookup(ComputationGraph& g, LookupParameters* p, co...
function Expression (line 24) | Expression const_lookup(ComputationGraph& g, LookupParameters* p, co...
function Expression (line 25) | Expression const_lookup(ComputationGraph& g, LookupParameters* p, co...
function Expression (line 26) | Expression zeroes(ComputationGraph& g, const Dim& d) { return Expres...
function Expression (line 29) | Expression nobackprop(const Expression& x) { return Expression(x.pg,...
function Expression (line 31) | Expression operator-(const Expression& x) { return Expression(x.pg, ...
function Expression (line 32) | Expression operator+(const Expression& x, const Expression& y) { ret...
function Expression (line 33) | Expression operator+(real x, const Expression& y) { return Expressio...
function Expression (line 34) | Expression operator+(const Expression& x, real y) { return y+x; }
function Expression (line 35) | Expression operator-(const Expression& x, const Expression& y) { ret...
function Expression (line 36) | Expression operator-(real x, const Expression& y) { return Expressio...
function Expression (line 37) | Expression operator-(const Expression& x, real y) { return -(y-x); }
function Expression (line 38) | Expression operator*(const Expression& x, const Expression& y) { ret...
function Expression (line 39) | Expression operator*(const Expression& x, float y) { return Expressi...
function Expression (line 40) | Expression addmv(const Expression& x, const Expression& y) { return ...
function Expression (line 41) | Expression cdiv(const Expression& x, const Expression& y) { return E...
function Expression (line 42) | Expression colwise_add(const Expression& x, const Expression& bias) ...
function Expression (line 43) | Expression contract3d_1d_1d(const Expression& x, const Expression& y...
function Expression (line 44) | Expression contract3d_1d_1d(const Expression& x, const Expression& y...
function Expression (line 45) | Expression contract3d_1d(const Expression& x, const Expression& y) {...
function Expression (line 46) | Expression contract3d_1d(const Expression& x, const Expression& y, c...
function Expression (line 48) | Expression sqrt(const Expression& x) { return Expression(x.pg, x.pg-...
function Expression (line 49) | Expression erf(const Expression& x) { return Expression(x.pg, x.pg->...
function Expression (line 50) | Expression tanh(const Expression& x) { return Expression(x.pg, x.pg-...
function Expression (line 51) | Expression lgamma(const Expression& x) { return Expression(x.pg, x.p...
function Expression (line 52) | Expression log(const Expression& x) { return Expression(x.pg, x.pg->...
function Expression (line 53) | Expression exp(const Expression& x) { return Expression(x.pg, x.pg->...
function Expression (line 54) | Expression square(const Expression& x) { return Expression(x.pg, x.p...
function Expression (line 55) | Expression cube(const Expression& x) { return Expression(x.pg, x.pg-...
function Expression (line 56) | Expression logistic(const Expression& x) { return Expression(x.pg, x...
function Expression (line 57) | Expression rectify(const Expression& x) { return Expression(x.pg, x....
function Expression (line 58) | Expression hinge(const Expression& x, unsigned index, float m) { ret...
function Expression (line 59) | Expression hinge(const Expression& x, const unsigned* pindex, float ...
function Expression (line 60) | Expression log_softmax(const Expression& x) { return Expression(x.pg...
function Expression (line 61) | Expression log_softmax(const Expression& x, const vector<unsigned>& ...
function Expression (line 62) | Expression sparsemax(const Expression& x) { return Expression(x.pg, ...
function Expression (line 63) | Expression sparsemax_loss(const Expression& x, const vector<unsigned...
function Expression (line 64) | Expression sparsemax_loss(const Expression& x, const vector<unsigned...
function Expression (line 65) | Expression softmax(const Expression& x) { return Expression(x.pg, x....
function Expression (line 66) | Expression softsign(const Expression& x) { return Expression(x.pg, x...
function Expression (line 67) | Expression pow(const Expression& x, const Expression& y) { return Ex...
function Expression (line 68) | Expression min(const Expression& x, const Expression& y) { return Ex...
function Expression (line 69) | Expression max(const Expression& x, const Expression& y) { return Ex...
function Expression (line 70) | Expression noise(const Expression& x, real stddev) { return Expressi...
function Expression (line 71) | Expression dropout(const Expression& x, real p) { return Expression(...
function Expression (line 72) | Expression block_dropout(const Expression& x, real p) { return Expre...
function Expression (line 74) | Expression reshape(const Expression& x, const Dim& d) { return Expre...
function Expression (line 75) | Expression transpose(const Expression& x) { return Expression(x.pg, ...
function Expression (line 76) | Expression select_rows(const Expression& x, const vector<unsigned>& ...
function Expression (line 77) | Expression select_rows(const Expression& x, const vector<unsigned>* ...
function Expression (line 78) | Expression select_cols(const Expression& x, const vector<unsigned>& ...
function Expression (line 79) | Expression select_cols(const Expression& x, const vector<unsigned>* ...
function Expression (line 80) | Expression inverse(const Expression& x) { return Expression(x.pg, x....
function Expression (line 81) | Expression logdet(const Expression& x) { return Expression(x.pg, x.p...
function Expression (line 83) | Expression trace_of_product(const Expression& x, const Expression& y...
function Expression (line 84) | Expression cwise_multiply(const Expression& x, const Expression& y) ...
function Expression (line 86) | Expression squared_norm(const Expression& x) { return Expression(x.p...
function Expression (line 88) | Expression dot_product(const Expression& x, const Expression& y) { r...
function Expression (line 89) | Expression squared_distance(const Expression& x, const Expression& y...
function Expression (line 90) | Expression huber_distance(const Expression& x, const Expression& y, ...
function Expression (line 91) | Expression l1_distance(const Expression& x, const Expression& y) { r...
function Expression (line 92) | Expression binary_log_loss(const Expression& x, const Expression& y)...
function Expression (line 93) | Expression pairwise_rank_loss(const Expression& x, const Expression&...
function Expression (line 94) | Expression poisson_loss(const Expression& x, unsigned y) { return Ex...
function Expression (line 95) | Expression poisson_loss(const Expression& x, const unsigned* py) { r...
function Expression (line 97) | Expression conv1d_narrow(const Expression& x, const Expression& f) {...
function Expression (line 98) | Expression conv1d_wide(const Expression& x, const Expression& f) { r...
function Expression (line 99) | Expression kmax_pooling(const Expression& x, unsigned k) { return Ex...
function Expression (line 100) | Expression fold_rows(const Expression& x, unsigned nrows) { return E...
function Expression (line 102) | Expression pick(const Expression& x, unsigned v) { return Expression...
function Expression (line 103) | Expression pick(const Expression& x, const vector<unsigned> & v) { r...
function Expression (line 104) | Expression pick(const Expression& x, unsigned* pv) { return Expressi...
function Expression (line 105) | Expression pick(const Expression& x, const vector<unsigned> * pv) { ...
function Expression (line 107) | Expression pickrange(const Expression& x, unsigned v, unsigned u) { ...
function Expression (line 109) | Expression pickneglogsoftmax(const Expression& x, unsigned v) { retu...
function Expression (line 110) | Expression pickneglogsoftmax(const Expression& x, const vector<unsig...
function Expression (line 111) | Expression pickneglogsoftmax(const Expression& x, unsigned* pv) { re...
function Expression (line 112) | Expression pickneglogsoftmax(const Expression& x, const vector<unsig...
function Expression (line 114) | Expression sum_cols(const Expression& x) { return Expression(x.pg, x...
function Expression (line 116) | Expression sum_batches(const Expression& x) { return Expression(x.pg...
function Expression (line 118) | Expression kmh_ngram(const Expression& x, unsigned n) { return Expre...
FILE: cnn/cnn/expr.h
function namespace (line 7) | namespace cnn { namespace expr {
FILE: cnn/cnn/fast-lstm.cc
type cnn (line 13) | namespace cnn {
function Expression (line 96) | Expression FastLSTMBuilder::add_input_impl(int prev, const Expression&...
FILE: cnn/cnn/fast-lstm.h
function namespace (line 10) | namespace cnn {
FILE: cnn/cnn/functors.h
function namespace (line 25) | namespace cnn {
type FL1Backward (line 40) | struct FL1Backward {
type FHuberBackward (line 48) | struct FHuberBackward {
type FProduct (line 58) | struct FProduct {
type FQuotient (line 64) | struct FQuotient {
type FConstantPlus (line 70) | struct FConstantPlus {
type FConstantMinus (line 78) | struct FConstantMinus {
type FNegate (line 86) | struct FNegate {
type FErf (line 92) | struct FErf {
type FTanh (line 98) | struct FTanh {
type FLog (line 111) | struct FLog {
type FMaxBackwardInv (line 117) | struct FMaxBackwardInv {
type FSqrtBackward (line 123) | struct FSqrtBackward {
type FErfBackward (line 129) | struct FErfBackward {
type FTanhBackward (line 135) | struct FTanhBackward {
type FLogBackward (line 141) | struct FLogBackward {
type FPairwiseRankLoss (line 147) | struct FPairwiseRankLoss {
type FRectifyBackward (line 156) | struct FRectifyBackward {
type FRectifyNegateBackward (line 162) | struct FRectifyNegateBackward {
type FSoftmaxNormalize (line 168) | struct FSoftmaxNormalize {
type FSoftmaxBackward (line 176) | struct FSoftmaxBackward {
type FLogGammaBackward (line 184) | struct FLogGammaBackward {
type FNegLogSoftmaxBackward (line 195) | struct FNegLogSoftmaxBackward {
type FPtrNegLogSoftmaxBackward (line 204) | struct FPtrNegLogSoftmaxBackward {
type FLogSoftmaxNormalize (line 213) | struct FLogSoftmaxNormalize {
type FWeightedError (line 221) | struct FWeightedError {
type FLogSoftmaxBackward (line 227) | struct FLogSoftmaxBackward {
type FRectify (line 236) | struct FRectify {
type FSoftSign (line 242) | struct FSoftSign {
type FSoftSignBackward (line 248) | struct FSoftSignBackward {
type FLogisticSigmoid (line 255) | struct FLogisticSigmoid {
type FLogisticSigmoidBackward (line 261) | struct FLogisticSigmoidBackward {
type FSqDist (line 267) | struct FSqDist {
type FEuclideanBackward (line 274) | struct FEuclideanBackward {
type FL2SGDUpdate (line 283) | struct FL2SGDUpdate {
type FBinaryLogLoss (line 292) | struct FBinaryLogLoss {
type FBinaryLogLossBackward (line 310) | struct FBinaryLogLossBackward {
FILE: cnn/cnn/gpu-kernels.h
function namespace (line 6) | namespace cnn {
FILE: cnn/cnn/gpu-ops.h
function namespace (line 4) | namespace cnn {
FILE: cnn/cnn/grad-check.cc
type cnn (line 12) | namespace cnn {
function CheckGrad (line 14) | bool CheckGrad(Model& m, ComputationGraph& g, int verbosity) {
FILE: cnn/cnn/grad-check.h
function namespace (line 4) | namespace cnn {
FILE: cnn/cnn/graph.cc
type cnn (line 8) | namespace cnn {
function GraphOptimize (line 10) | void GraphOptimize(ComputationGraph* cg) {
FILE: cnn/cnn/graph.h
function namespace (line 4) | namespace cnn {
FILE: cnn/cnn/gru.cc
type cnn (line 13) | namespace cnn {
function Expression (line 77) | Expression GRUBuilder::add_input_impl(int prev, const Expression& x) {
FILE: cnn/cnn/gru.h
function namespace (line 7) | namespace cnn {
FILE: cnn/cnn/hsm-builder.cc
type cnn (line 13) | namespace cnn {
function Cluster (line 26) | Cluster* Cluster::add_child(unsigned sym) {
function Cluster (line 80) | const Cluster* Cluster::get_child(unsigned i) const {
function Expression (line 90) | Expression Cluster::predict(Expression h, ComputationGraph& cg) const {
function Expression (line 101) | Expression Cluster::neg_log_softmax(Expression h, unsigned r, Computat...
function Expression (line 150) | Expression Cluster::get_weights(ComputationGraph& cg) const {
function Expression (line 157) | Expression Cluster::get_bias(ComputationGraph& cg) const {
function string (line 164) | string Cluster::toString() const {
function Expression (line 191) | Expression HierarchicalSoftmaxBuilder::neg_log_softmax(const Expressio...
function is_ws (line 232) | inline bool is_ws(char x) { return (x == ' ' || x == '\t'); }
function not_ws (line 233) | inline bool not_ws(char x) { return (x != ' ' && x != '\t'); }
function Cluster (line 235) | Cluster* HierarchicalSoftmaxBuilder::ReadClusterFile(const std::string...
FILE: cnn/cnn/hsm-builder.h
function namespace (line 12) | namespace cnn {
FILE: cnn/cnn/init.cc
type cnn (line 16) | namespace cnn {
function RemoveArgs (line 26) | static void RemoveArgs(int& argc, char**& argv, int& argi, int n) {
function Initialize (line 33) | void Initialize(int& argc, char**& argv, unsigned random_seed, bool sh...
function Cleanup (line 93) | void Cleanup() {
FILE: cnn/cnn/init.h
function namespace (line 4) | namespace cnn {
FILE: cnn/cnn/lstm.cc
type cnn (line 13) | namespace cnn {
function Expression (line 92) | Expression LSTMBuilder::add_input_impl(int prev, const Expression& x) {
FILE: cnn/cnn/lstm.h
function namespace (line 10) | namespace cnn {
FILE: cnn/cnn/mem.cc
type cnn (line 19) | namespace cnn {
FILE: cnn/cnn/mem.h
function namespace (line 6) | namespace cnn {
function MemAllocator (line 26) | struct CPUAllocator : public MemAllocator {
function MemAllocator (line 33) | struct SharedAllocator : public MemAllocator {
function MemAllocator (line 41) | struct GPUAllocator : public MemAllocator {
FILE: cnn/cnn/model.cc
type cnn (line 21) | namespace cnn {
function Parameters (line 201) | Parameters* Model::add_parameters(const Dim& d, float scale) {
function LookupParameters (line 208) | LookupParameters* Model::add_lookup_parameters(unsigned n, const Dim& ...
function save_cnn_model (line 220) | void save_cnn_model(std::string filename, Model* model) {
function load_cnn_model (line 226) | void load_cnn_model(std::string filename, Model* model) {
FILE: cnn/cnn/model.h
function namespace (line 14) | namespace cnn {
function ParametersBase (line 58) | struct LookupParameters : public ParametersBase {
function gradient_l2_norm (line 107) | float gradient_l2_norm() const;
FILE: cnn/cnn/mp.cc
type cnn (line 5) | namespace cnn {
type mp (line 6) | namespace mp {
function GenerateQueueName (line 14) | std::string GenerateQueueName() {
function GenerateSharedMemoryName (line 21) | std::string GenerateSharedMemoryName() {
function SumValues (line 28) | cnn::real SumValues(const std::vector<cnn::real>& values) {
function Mean (line 32) | cnn::real Mean(const std::vector<cnn::real>& values) {
function ElapsedTimeString (line 36) | std::string ElapsedTimeString(const timespec& start, const timespec&...
function SpawnChildren (line 44) | unsigned SpawnChildren(std::vector<Workload>& workloads) {
function CreateWorkloads (line 64) | std::vector<Workload> CreateWorkloads(unsigned num_children) {
FILE: cnn/cnn/mp.h
function namespace (line 28) | namespace cnn {
FILE: cnn/cnn/nodes-common.cc
type cnn (line 9) | namespace cnn {
function LooksLikeVector (line 11) | inline bool LooksLikeVector(const Dim& d) {
function string (line 20) | string SparsemaxLoss::as_string(const vector<string>& arg_names) const {
function Dim (line 26) | Dim SparsemaxLoss::dim_forward(const vector<Dim>& xs) const {
function string (line 34) | string Sparsemax::as_string(const vector<string>& arg_names) const {
function Dim (line 40) | Dim Sparsemax::dim_forward(const vector<Dim>& xs) const {
function string (line 48) | string MatrixInverse::as_string(const vector<string>& arg_names) const {
function Dim (line 54) | Dim MatrixInverse::dim_forward(const vector<Dim>& xs) const {
function Dim (line 58) | Dim LogDet::dim_forward(const vector<Dim>& xs) const {
function string (line 66) | string LogDet::as_string(const vector<string>& arg_names) const {
function string (line 72) | string AddMv::as_string(const vector<string>& arg_names) const {
function Dim (line 78) | Dim AddMv::dim_forward(const vector<Dim>& xs) const {
function string (line 89) | string SelectRows::as_string(const vector<string>& arg_names) const {
function Dim (line 95) | Dim SelectRows::dim_forward(const vector<Dim>& xs) const {
function string (line 105) | string SelectCols::as_string(const vector<string>& arg_names) const {
function Dim (line 111) | Dim SelectCols::dim_forward(const vector<Dim>& xs) const {
function string (line 120) | string Min::as_string(const vector<string>& arg_names) const {
function Dim (line 126) | Dim Min::dim_forward(const vector<Dim>& xs) const {
function string (line 134) | string Max::as_string(const vector<string>& arg_names) const {
function Dim (line 140) | Dim Max::dim_forward(const vector<Dim>& xs) const {
function string (line 148) | string TraceOfProduct::as_string(const vector<string>& arg_names) const {
function Dim (line 154) | Dim TraceOfProduct::dim_forward(const vector<Dim>& xs) const {
function string (line 162) | string ConstScalarMultiply::as_string(const vector<string>& arg_names)...
function Dim (line 168) | Dim ConstScalarMultiply::dim_forward(const vector<Dim>& xs) const {
function string (line 176) | string DotProduct::as_string(const vector<string>& arg_names) const {
function Dim (line 182) | Dim DotProduct::dim_forward(const vector<Dim>& xs) const {
function string (line 193) | string Transpose::as_string(const vector<string>& arg_names) const {
function Dim (line 199) | Dim Transpose::dim_forward(const vector<Dim>& xs) const {
function string (line 207) | string Reshape::as_string(const vector<string>& arg_names) const {
function Dim (line 213) | Dim Reshape::dim_forward(const vector<Dim>& xs) const {
function string (line 219) | string SumColumns::as_string(const vector<string>& arg_names) const {
function Dim (line 227) | Dim SumColumns::dim_forward(const vector<Dim>& xs) const {
function string (line 233) | string KMHNGram::as_string(const vector<string>& arg_names) const {
function Dim (line 239) | Dim KMHNGram::dim_forward(const vector<Dim>& xs) const {
function string (line 249) | string InnerProduct3D_1D::as_string(const vector<string>& arg_names) c...
function Dim (line 256) | Dim InnerProduct3D_1D::dim_forward(const vector<Dim>& xs) const {
function string (line 274) | string InnerProduct3D_1D_1D::as_string(const vector<string>& arg_names...
function Dim (line 281) | Dim InnerProduct3D_1D_1D::dim_forward(const vector<Dim>& xs) const {
function string (line 300) | string GaussianNoise::as_string(const vector<string>& arg_names) const {
function Dim (line 306) | Dim GaussianNoise::dim_forward(const vector<Dim>& xs) const {
function string (line 311) | string Dropout::as_string(const vector<string>& arg_names) const {
function Dim (line 317) | Dim Dropout::dim_forward(const vector<Dim>& xs) const {
function string (line 322) | string BlockDropout::as_string(const vector<string>& arg_names) const {
function Dim (line 328) | Dim BlockDropout::dim_forward(const vector<Dim>& xs) const {
function string (line 333) | string ConstantPlusX::as_string(const vector<string>& arg_names) const {
function Dim (line 339) | Dim ConstantPlusX::dim_forward(const vector<Dim>& xs) const {
function string (line 344) | string ConstantMinusX::as_string(const vector<string>& arg_names) const {
function Dim (line 350) | Dim ConstantMinusX::dim_forward(const vector<Dim>& xs) const {
function string (line 355) | string LogSumExp::as_string(const vector<string>& arg_names) const {
function Dim (line 364) | Dim LogSumExp::dim_forward(const vector<Dim>& xs) const {
function string (line 375) | string Sum::as_string(const vector<string>& arg_names) const {
function Dim (line 383) | Dim Sum::dim_forward(const vector<Dim>& xs) const {
function string (line 395) | string SumBatches::as_string(const vector<string>& arg_names) const {
function Dim (line 401) | Dim SumBatches::dim_forward(const vector<Dim>& xs) const {
function string (line 406) | string Average::as_string(const vector<string>& arg_names) const {
function Dim (line 415) | Dim Average::dim_forward(const vector<Dim>& xs) const {
function string (line 427) | string Sqrt::as_string(const vector<string>& arg_names) const {
function Dim (line 433) | Dim Sqrt::dim_forward(const vector<Dim>& xs) const {
function string (line 438) | string Erf::as_string(const vector<string>& arg_names) const {
function Dim (line 444) | Dim Erf::dim_forward(const vector<Dim>& xs) const {
function string (line 449) | string Tanh::as_string(const vector<string>& arg_names) const {
function Dim (line 455) | Dim Tanh::dim_forward(const vector<Dim>& xs) const {
function string (line 460) | string Square::as_string(const vector<string>& arg_names) const {
function Dim (line 466) | Dim Square::dim_forward(const vector<Dim>& xs) const {
function string (line 471) | string Cube::as_string(const vector<string>& arg_names) const {
function Dim (line 477) | Dim Cube::dim_forward(const vector<Dim>& xs) const {
function string (line 482) | string Exp::as_string(const vector<string>& arg_names) const {
function Dim (line 488) | Dim Exp::dim_forward(const vector<Dim>& xs) const {
function string (line 493) | string LogGamma::as_string(const vector<string>& arg_names) const {
function Dim (line 499) | Dim LogGamma::dim_forward(const vector<Dim>& xs) const {
function string (line 504) | string Log::as_string(const vector<string>& arg_names) const {
function Dim (line 510) | Dim Log::dim_forward(const vector<Dim>& xs) const {
function string (line 515) | string Concatenate::as_string(const vector<string>& arg_names) const {
function Dim (line 525) | Dim Concatenate::dim_forward(const vector<Dim>& xs) const {
function string (line 543) | string ConcatenateColumns::as_string(const vector<string>& arg_names) ...
function Dim (line 553) | Dim ConcatenateColumns::dim_forward(const vector<Dim>& xs) const {
function string (line 569) | string PairwiseRankLoss::as_string(const vector<string>& arg_names) co...
function Dim (line 575) | Dim PairwiseRankLoss::dim_forward(const vector<Dim>& xs) const {
function string (line 586) | string Hinge::as_string(const vector<string>& arg_names) const {
function Dim (line 592) | Dim Hinge::dim_forward(const vector<Dim>& xs) const {
function string (line 600) | string Identity::as_string(const vector<string>& arg_names) const {
function Dim (line 604) | Dim Identity::dim_forward(const vector<Dim>& xs) const {
function string (line 609) | string NoBackprop::as_string(const vector<string>& arg_names) const {
function Dim (line 615) | Dim NoBackprop::dim_forward(const vector<Dim>& xs) const {
function string (line 620) | string Softmax::as_string(const vector<string>& arg_names) const {
function Dim (line 626) | Dim Softmax::dim_forward(const vector<Dim>& xs) const {
function string (line 635) | string SoftSign::as_string(const vector<string>& arg_names) const {
function Dim (line 641) | Dim SoftSign::dim_forward(const vector<Dim>& xs) const {
function string (line 650) | string PickNegLogSoftmax::as_string(const vector<string>& arg_names) c...
function Dim (line 663) | Dim PickNegLogSoftmax::dim_forward(const vector<Dim>& xs) const {
function string (line 672) | string LogSoftmax::as_string(const vector<string>& arg_names) const {
function Dim (line 678) | Dim LogSoftmax::dim_forward(const vector<Dim>& xs) const {
function string (line 687) | string RestrictedLogSoftmax::as_string(const vector<string>& arg_names...
function Dim (line 693) | Dim RestrictedLogSoftmax::dim_forward(const vector<Dim>& xs) const {
function string (line 702) | string PickElement::as_string(const vector<string>& arg_names) const {
function Dim (line 708) | Dim PickElement::dim_forward(const vector<Dim>& xs) const {
function string (line 719) | string PickRange::as_string(const vector<string>& arg_names) const {
function Dim (line 725) | Dim PickRange::dim_forward(const vector<Dim>& xs) const {
function string (line 735) | string MatrixMultiply::as_string(const vector<string>& arg_names) const {
function Dim (line 741) | Dim MatrixMultiply::dim_forward(const vector<Dim>& xs) const {
function string (line 751) | string CwiseMultiply::as_string(const vector<string>& arg_names) const {
function Dim (line 757) | Dim CwiseMultiply::dim_forward(const vector<Dim>& xs) const {
function string (line 768) | string Pow::as_string(const vector<string>& arg_names) const {
function Dim (line 774) | Dim Pow::dim_forward(const vector<Dim>& xs) const {
function string (line 784) | string CwiseQuotient::as_string(const vector<string>& arg_names) const {
function Dim (line 790) | Dim CwiseQuotient::dim_forward(const vector<Dim>& xs) const {
function string (line 801) | string AffineTransform::as_string(const vector<string>& arg_names) con...
function Dim (line 809) | Dim AffineTransform::dim_forward(const vector<Dim>& xs) const {
function string (line 827) | string Negate::as_string(const vector<string>& arg_names) const {
function Dim (line 833) | Dim Negate::dim_forward(const vector<Dim>& xs) const {
function string (line 838) | string Rectify::as_string(const vector<string>& arg_names) const {
function Dim (line 844) | Dim Rectify::dim_forward(const vector<Dim>& xs) const {
function string (line 849) | string HuberDistance::as_string(const vector<string>& arg_names) const {
function Dim (line 855) | Dim HuberDistance::dim_forward(const vector<Dim>& xs) const {
function string (line 864) | string L1Distance::as_string(const vector<string>& arg_names) const {
function Dim (line 870) | Dim L1Distance::dim_forward(const vector<Dim>& xs) const {
function string (line 879) | string PoissonRegressionLoss::as_string(const vector<string>& arg_name...
function Dim (line 885) | Dim PoissonRegressionLoss::dim_forward(const vector<Dim>& xs) const {
function string (line 893) | string SquaredNorm::as_string(const vector<string>& arg_names) const {
function Dim (line 899) | Dim SquaredNorm::dim_forward(const vector<Dim>& xs) const {
function string (line 904) | string SquaredEuclideanDistance::as_string(const vector<string>& arg_n...
function Dim (line 910) | Dim SquaredEuclideanDistance::dim_forward(const vector<Dim>& xs) const {
function string (line 919) | string LogisticSigmoid::as_string(const vector<string>& arg_names) con...
function Dim (line 925) | Dim LogisticSigmoid::dim_forward(const vector<Dim>& xs) const {
function string (line 930) | string BinaryLogLoss::as_string(const vector<string>& arg_names) const {
function Dim (line 936) | Dim BinaryLogLoss::dim_forward(const vector<Dim>& xs) const {
FILE: cnn/cnn/nodes.cc
type cnn (line 36) | namespace cnn {
function EIGEN_STRONG_INLINE (line 770) | EIGEN_STRONG_INLINE float logsumexp(const T& x) {
function logdet (line 788) | inline typename MatrixType::Scalar logdet(const MatrixType& M, bool us...
function EIGEN_STRONG_INLINE (line 1495) | EIGEN_STRONG_INLINE real logsumexp(const T& x, const vector<unsigned>&...
function CUDAMatrixMultiply (line 1622) | inline void CUDAMatrixMultiply(const Tensor& l, const Tensor& r, Tenso...
function string (line 2151) | string Zeroes::as_string(const vector<string>& arg_names) const {
function Dim (line 2157) | Dim Zeroes::dim_forward(const vector<Dim>& xs) const {
FILE: cnn/cnn/nodes.h
function Node (line 11) | struct SparsemaxLoss : public Node {
function Node (line 106) | struct Pow : public Node {
function Node (line 119) | struct Min : public Node {
function Node (line 268) | struct InnerProduct3D_1D_1D : public Node {
function Node (line 282) | struct GaussianNoise : public Node {
type BlockDropout (line 314) | struct BlockDropout
function explicit (line 315) | explicit BlockDropout(const std::initializer_list<VariableIndex>& a, rea...
function aux_storage_size (line 318) | size_t aux_storage_size() const override;
function Node (line 346) | struct ConstantMinusX : public Node {
function Node (line 361) | struct Sqrt : public Node {
function Node (line 375) | struct Erf : public Node {
function Node (line 389) | struct Tanh : public Node {
function Node (line 403) | struct Square : public Node {
function Node (line 417) | struct Cube : public Node {
function Node (line 431) | struct Exp : public Node {
function Node (line 445) | struct LogGamma : public Node {
function Node (line 459) | struct Log : public Node {
function Node (line 473) | struct Concatenate : public Node {
function Node (line 490) | struct ConcatenateColumns : public Node {
function Node (line 506) | struct PairwiseRankLoss : public Node {
function Node (line 522) | struct Hinge : public Node {
function Node (line 554) | struct Identity : public Node {
function Node (line 571) | struct MaxPooling1D : public Node {
function Node (line 586) | struct MatrixMultiply : public Node {
function Node (line 600) | struct CwiseMultiply : public Node {
function Node (line 614) | struct CwiseQuotient : public Node {
function Node (line 628) | struct AffineTransform : public Node {
function Node (line 642) | struct Negate : public Node {
function Node (line 656) | struct Rectify : public Node {
function Node (line 674) | struct BinaryLogLoss : public Node {
function Node (line 688) | struct LogSumExp : public Node {
FILE: cnn/cnn/param-nodes.cc
type cnn (line 8) | namespace cnn {
function string (line 10) | string ConstParameterNode::as_string(const vector<string>& arg_names) ...
function Dim (line 16) | Dim ConstParameterNode::dim_forward(const vector<Dim>& xs) const {
function string (line 35) | string ParameterNode::as_string(const vector<string>& arg_names) const {
function Dim (line 41) | Dim ParameterNode::dim_forward(const vector<Dim>& xs) const {
function string (line 64) | string InputNode::as_string(const vector<string>& arg_names) const {
function Dim (line 70) | Dim InputNode::dim_forward(const vector<Dim>& xs) const {
function string (line 99) | string ScalarInputNode::as_string(const vector<string>& arg_names) con...
function Dim (line 105) | Dim ScalarInputNode::dim_forward(const vector<Dim>& xs) const {
function string (line 127) | string LookupNode::as_string(const vector<string>& arg_names) const {
function Dim (line 133) | Dim LookupNode::dim_forward(const vector<Dim>& xs) const {
FILE: cnn/cnn/param-nodes.h
function namespace (line 7) | namespace cnn {
FILE: cnn/cnn/random.h
function namespace (line 6) | namespace cnn {
FILE: cnn/cnn/rnn-state-machine.cc
type cnn (line 8) | namespace cnn {
FILE: cnn/cnn/rnn-state-machine.h
function namespace (line 4) | namespace cnn {
FILE: cnn/cnn/rnn.cc
type cnn (line 15) | namespace cnn {
function Expression (line 66) | Expression SimpleRNNBuilder::add_input_impl(int prev, const Expression...
function Expression (line 90) | Expression SimpleRNNBuilder::add_auxiliary_input(const Expression &in,...
FILE: cnn/cnn/rnn.h
function namespace (line 10) | namespace cnn {
FILE: cnn/cnn/saxe-init.cc
type cnn (line 11) | namespace cnn {
function OrthonormalRandom (line 13) | void OrthonormalRandom(unsigned dd, float g, Tensor& x) {
FILE: cnn/cnn/saxe-init.h
function namespace (line 4) | namespace cnn {
FILE: cnn/cnn/shadow-params.cc
type cnn (line 9) | namespace cnn {
function AllocateShadowParameters (line 23) | vector<ShadowParameters> AllocateShadowParameters(const Model& m) {
function AllocateShadowLookupParameters (line 31) | vector<ShadowLookupParameters> AllocateShadowLookupParameters(const Mo...
FILE: cnn/cnn/shadow-params.h
function namespace (line 11) | namespace cnn {
FILE: cnn/cnn/simd-functors.h
function namespace (line 21) | namespace cnn {
function namespace (line 36) | namespace Eigen { namespace internal {
function namespace (line 46) | namespace cnn {
function namespace (line 61) | namespace Eigen { namespace internal {
function namespace (line 71) | namespace cnn {
function namespace (line 88) | namespace Eigen { namespace internal {
function namespace (line 99) | namespace cnn {
function namespace (line 116) | namespace Eigen { namespace internal {
function namespace (line 126) | namespace cnn {
function namespace (line 142) | namespace Eigen { namespace internal {
function namespace (line 152) | namespace cnn {
function namespace (line 161) | namespace Eigen { namespace internal {
function namespace (line 171) | namespace cnn {
function namespace (line 184) | namespace Eigen { namespace internal {
function namespace (line 194) | namespace cnn {
function namespace (line 215) | namespace Eigen { namespace internal {
FILE: cnn/cnn/tensor.cc
type cnn (line 13) | namespace cnn {
function ostream (line 15) | ostream& operator<<(ostream& os, const Tensor& t) {
function real (line 26) | real as_scalar(const Tensor& t) {
function as_vector (line 37) | vector<real> as_vector(const Tensor& v) {
function real (line 152) | real rand01() {
function rand0n (line 157) | int rand0n(int n) {
function real (line 164) | real rand_normal() {
FILE: cnn/cnn/tensor.h
function namespace (line 24) | namespace cnn {
type TensorTools (line 209) | struct TensorTools {
FILE: cnn/cnn/tests/test_edges.cc
function Dim (line 17) | Dim size(const Tensor& t) {
function BOOST_AUTO_TEST_CASE (line 23) | BOOST_AUTO_TEST_CASE(ESqrL2)
function BOOST_AUTO_TEST_CASE (line 45) | BOOST_AUTO_TEST_CASE(EMatrixMultiply) {
function BOOST_AUTO_TEST_CASE (line 76) | BOOST_AUTO_TEST_CASE(EColumnConcat)
function BOOST_AUTO_TEST_CASE (line 114) | BOOST_AUTO_TEST_CASE(ERowConcat)
function BOOST_AUTO_TEST_CASE (line 150) | BOOST_AUTO_TEST_CASE(EMultilinear) {
function BOOST_AUTO_TEST_CASE (line 198) | BOOST_AUTO_TEST_CASE(ELogisticSigmoid) {
function BOOST_AUTO_TEST_CASE (line 220) | BOOST_AUTO_TEST_CASE(ETanh) {
function BOOST_AUTO_TEST_CASE (line 240) | BOOST_AUTO_TEST_CASE(MatrixVector) {
function BOOST_AUTO_TEST_CASE (line 266) | BOOST_AUTO_TEST_CASE(EConstantMinus) {
function BOOST_AUTO_TEST_CASE (line 283) | BOOST_AUTO_TEST_CASE(ESoftmaxUnif) {
function BOOST_AUTO_TEST_CASE (line 315) | BOOST_AUTO_TEST_CASE(TensorInner3D_1D) {
FILE: cnn/cnn/tests/test_init.cc
function BOOST_AUTO_TEST_CASE (line 16) | BOOST_AUTO_TEST_CASE(EOrthonormalRandom)
function BOOST_AUTO_TEST_CASE (line 45) | BOOST_AUTO_TEST_CASE(BernoulliInit) {
function BOOST_AUTO_TEST_CASE (line 55) | BOOST_AUTO_TEST_CASE(Rand01) {
FILE: cnn/cnn/tests/test_utils.h
function namespace (line 6) | namespace cnn {
FILE: cnn/cnn/timing.h
function namespace (line 8) | namespace cnn {
FILE: cnn/cnn/training.cc
type cnn (line 5) | namespace cnn {
function is_valid (line 10) | bool is_valid(const Eigen::MatrixBase<Derived>& x) {
FILE: cnn/cnn/training.h
function namespace (line 8) | namespace cnn {
function Trainer (line 47) | struct SimpleSGDTrainer : public Trainer {
function Trainer (line 53) | struct MomentumSGDTrainer : public Trainer {
function Trainer (line 69) | struct AdagradTrainer : public Trainer {
function Trainer (line 80) | struct AdadeltaTrainer : public Trainer {
function Trainer (line 94) | struct RmsPropTrainer : public Trainer {
function Trainer (line 106) | struct AdamTrainer : public Trainer {
FILE: cnn/examples/embed-cl.cc
type Encoder (line 27) | struct Encoder {
method Encoder (line 31) | explicit Encoder(Model& model) {
method Expression (line 36) | Expression EmbedSource(const vector<int>& sent, ComputationGraph& cg) {
method Expression (line 51) | Expression EmbedTarget(const vector<int>& sent, ComputationGraph& cg) {
function main (line 66) | int main(int argc, char** argv) {
FILE: cnn/examples/encdec.cc
type EncoderDecoder (line 33) | struct EncoderDecoder {
method EncoderDecoder (line 45) | explicit EncoderDecoder(Model& model) :
method Expression (line 62) | Expression BuildGraph(const vector<int>& insent, const vector<int>& os...
function main (line 122) | int main(int argc, char** argv) {
FILE: cnn/examples/mlc.cc
type TrainingInstance (line 18) | struct TrainingInstance {
method TrainingInstance (line 19) | TrainingInstance() {}
method TrainingInstance (line 20) | TrainingInstance(const vector<pair<unsigned,float>>& x, const vector<u...
function swap (line 25) | void swap(TrainingInstance& a, TrainingInstance& b) {
function ReadFiles (line 33) | vector<TrainingInstance> ReadFiles(const char* xfname, const char* yfnam...
type MLCBuilder (line 80) | struct MLCBuilder {
method MLCBuilder (line 81) | explicit MLCBuilder(Model& m, unsigned nfeats, unsigned labels) {
method Expression (line 91) | Expression BuildPredictionScores(ComputationGraph& cg, const vector<pa...
function main (line 109) | int main(int argc, char** argv) {
FILE: cnn/examples/nlm.cc
function main (line 16) | int main(int argc, char** argv) {
FILE: cnn/examples/poisson-regression.cc
type RNNLengthPredictor (line 33) | struct RNNLengthPredictor {
method RNNLengthPredictor (line 38) | explicit RNNLengthPredictor(Model& model) : builder(LAYERS, INPUT_DIM,...
method Expression (line 45) | Expression BuildLMGraph(const vector<int>& sent, unsigned len, Computa...
function main (line 66) | int main(int argc, char** argv) {
FILE: cnn/examples/read-write.cc
class XORModel (line 20) | class XORModel {
method XORModel (line 30) | XORModel() {}
method XORModel (line 32) | XORModel(const unsigned& hidden_len, Model *m) {
method InitParams (line 37) | void InitParams(Model *m) {
method AddParamsToCG (line 44) | void AddParamsToCG(ComputationGraph *cg) {
method Train (line 51) | float Train(vector<cnn::real> &input, cnn::real &gold_output,
method Decode (line 68) | float Decode(vector<cnn::real> &input) {
method serialize (line 81) | void serialize(Archive& ar, const unsigned int) {
function WriteToFile (line 89) | void WriteToFile(string& filename, XORModel &model, Model &cnn_model) {
function ReadFromFile (line 101) | void ReadFromFile(string& filename, XORModel *model, Model *cnn_model) {
function main (line 119) | int main(int argc, char** argv) {
FILE: cnn/examples/rnnlm-aevb.cc
type RNNLanguageModel (line 35) | struct RNNLanguageModel {
method RNNLanguageModel (line 52) | explicit RNNLanguageModel(Model& model) :
method Expression (line 71) | Expression BuildLMGraph(const vector<int>& sent, ComputationGraph& cg,...
function main (line 121) | int main(int argc, char** argv) {
FILE: cnn/examples/rnnlm-batch.cc
type RNNLanguageModel (line 33) | struct RNNLanguageModel {
method RNNLanguageModel (line 38) | explicit RNNLanguageModel(Model& model) : builder(LAYERS, INPUT_DIM, H...
method Expression (line 45) | Expression BuildLMGraphs(const vector<vector<int> >& sents,
method RandomSample (line 75) | void RandomSample(int max_len = 150) {
type CompareLen (line 113) | struct CompareLen {
function main (line 119) | int main(int argc, char** argv) {
FILE: cnn/examples/rnnlm-cfsm.cc
type RNNLanguageModel (line 32) | struct RNNLanguageModel {
method RNNLanguageModel (line 36) | explicit RNNLanguageModel(Model& model, ClassFactoredSoftmaxBuilder& h) :
method Expression (line 42) | Expression BuildLMGraph(const vector<int>& sent, ComputationGraph& cg) {
method RandomSample (line 62) | void RandomSample(int max_len = 150) {
function main (line 83) | int main(int argc, char** argv) {
FILE: cnn/examples/rnnlm-givenbag.cc
type RNNLanguageModel (line 32) | struct RNNLanguageModel {
method RNNLanguageModel (line 37) | explicit RNNLanguageModel(Model& model) : builder(LAYERS, INPUT_DIM, H...
method Expression (line 44) | Expression BuildLMGraph(const vector<int>& sent, ComputationGraph& cg,...
method RandomSample (line 69) | void RandomSample(int max_len = 150) {
function main (line 106) | int main(int argc, char** argv) {
FILE: cnn/examples/rnnlm-mp.cc
function ReadData (line 27) | vector<Datum> ReadData(string filename) {
class Learner (line 42) | class Learner : public ILearner<D, cnn::real> {
method Learner (line 44) | explicit Learner(RNNLanguageModel<T>& rnnlm, unsigned data_size) : rnn...
method LearnFromDatum (line 47) | cnn::real LearnFromDatum(const D& datum, bool learn) {
method SaveModel (line 57) | void SaveModel() {}
function main (line 63) | int main(int argc, char** argv) {
FILE: cnn/examples/rnnlm.cc
type RNNLanguageModel (line 31) | struct RNNLanguageModel {
method RNNLanguageModel (line 36) | explicit RNNLanguageModel(Model& model) : builder(LAYERS, INPUT_DIM, H...
method Expression (line 43) | Expression BuildLMGraph(const vector<int>& sent, ComputationGraph& cg) {
method RandomSample (line 80) | void RandomSample(int max_len = 150) {
function main (line 117) | int main(int argc, char** argv) {
FILE: cnn/examples/rnnlm.h
function explicit (line 27) | explicit RNNLanguageModel(Model& model) : builder(LAYERS, INPUT_DIM, HID...
function Expression (line 36) | Expression BuildLMGraph(const vector<int>& sent, ComputationGraph& cg) {
FILE: cnn/examples/rnnlm2.cc
type RNNLanguageModel (line 31) | struct RNNLanguageModel {
method RNNLanguageModel (line 36) | explicit RNNLanguageModel(Model& model) : builder(LAYERS, INPUT_DIM, H...
method Expression (line 43) | Expression BuildLMGraph(const vector<int>& sent, ComputationGraph& cg) {
method RandomSample (line 85) | void RandomSample(int max_len = 150) {
function main (line 122) | int main(int argc, char** argv) {
FILE: cnn/examples/segrnn-sup.cc
type PKey (line 30) | struct PKey {
method PKey (line 31) | PKey(int x1, int x2, unsigned x3)
type std (line 43) | namespace std {
type hash<PKey> (line 45) | struct hash<PKey>
type SymbolEmbedding (line 73) | struct SymbolEmbedding {
method SymbolEmbedding (line 74) | SymbolEmbedding(Model& m, unsigned n, unsigned dim) {
method load_embedding (line 77) | void load_embedding(cnn::Dict& d, string pretrain_path){
method new_graph (line 96) | void new_graph(ComputationGraph& g) { cg = &g; }
method Expression (line 97) | Expression embed(unsigned label_id) {
type DurationEmbedding (line 104) | struct DurationEmbedding {
type MLPDurationEmbedding (line 112) | struct MLPDurationEmbedding : public DurationEmbedding {
method MLPDurationEmbedding (line 113) | MLPDurationEmbedding(Model& m, unsigned hidden, unsigned dim) {
method new_graph (line 125) | void new_graph(ComputationGraph& g) override {
method Expression (line 133) | Expression embed(unsigned dur) override {
type BinnedDurationEmbedding (line 153) | struct BinnedDurationEmbedding : public DurationEmbedding {
method BinnedDurationEmbedding (line 154) | BinnedDurationEmbedding(Model& m, unsigned dim, unsigned num_bins = 8)...
method new_graph (line 157) | void new_graph(ComputationGraph& g) override {
method Expression (line 160) | Expression embed(unsigned dur) override {
type BiTrans (line 171) | struct BiTrans {
method BiTrans (line 178) | explicit BiTrans(Model& model) :
method transcribe (line 186) | vector<Expression> transcribe(ComputationGraph& cg, const vector<Expre...
type SegEmbedUni (line 220) | struct SegEmbedUni {
method SegEmbedUni (line 225) | explicit SegEmbedUni(Model& m) :
method construct_chart (line 229) | void construct_chart(ComputationGraph& cg, const vector<Expression>& c...
method Expression (line 255) | const Expression& operator()(int i, int j) const {
type SegEmbedBi (line 265) | struct SegEmbedBi {
method SegEmbedBi (line 269) | explicit SegEmbedBi(Model& m) : fwd(m), rev(m) {}
method construct_chart (line 270) | void construct_chart(ComputationGraph& cg, const vector<Expression>& c...
type SegmentalRNN (line 301) | struct SegmentalRNN {
method SegmentalRNN (line 309) | explicit SegmentalRNN(Model& model, cnn::Dict& d_, cnn::Dict& td_) :
method ConstructInput (line 343) | vector<Expression> ConstructInput(const vector<int>& x,
method ConstructSegmentMap (line 352) | unordered_map<PKey,Expression> ConstructSegmentMap(vector<Expression>&...
method Expression (line 410) | Expression SupervisedCRFLoss(vector<Expression>& xins,
method Expression (line 477) | Expression PartiallySupervisedCRFLoss(vector<Expression>& xins,
method Expression (line 562) | Expression SumParts(int len,
method Expression (line 594) | Expression SupervisedHingeLoss(vector<Expression>& xins,
method PureDecode (line 612) | void PureDecode(int len,
method ViterbiDecode (line 683) | void ViterbiDecode(vector<Expression>& xins,
function ParseTrainingInstance (line 708) | pair<vector<int>,vector<pair<int,int>>> ParseTrainingInstance(const std:...
function check_max_seg (line 744) | bool inline check_max_seg(const vector<pair<int,int>>& yz, int max_seg_l...
function evaluate (line 756) | double evaluate(vector<vector<pair<int,int>>>& yz_preds,
function test_only (line 839) | void test_only(SegmentalRNN<LSTMBuilder>& segrnn,
function read_file (line 861) | void read_file(string file_path,
function save_models (line 880) | void save_models(string model_file_prefix,
function load_models (line 906) | void load_models(string model_file_prefix,
function load_dicts (line 919) | void load_dicts(string model_file_prefix,
function edit_distance (line 938) | unsigned int edit_distance(const std::string& s1, const std::string& s2)
function evaluate_partial (line 952) | double evaluate_partial(vector<vector<pair<int,int>>>& yz_preds,
function predict_and_evaluate (line 986) | double predict_and_evaluate(SegmentalRNN<LSTMBuilder>& segrnn,
function main (line 1012) | int main(int argc, char** argv) {
FILE: cnn/examples/skiprnnlm.cc
type RNNSkipLM (line 43) | struct RNNSkipLM {
method RNNSkipLM (line 48) | explicit RNNSkipLM(Model& model) : builder(LAYERS, INPUT_DIM, HIDDEN_D...
method Expression (line 55) | Expression BuildLMGraph(const Document& doc, ComputationGraph& cg) {
function main (line 95) | int main(int argc, char** argv) {
function read_documents (line 208) | void read_documents(const std::string &filename, Corpus &corpus) {
FILE: cnn/examples/tag-bilstm.cc
type RNNLanguageModel (line 38) | struct RNNLanguageModel {
method RNNLanguageModel (line 48) | explicit RNNLanguageModel(Model& model) :
method Expression (line 61) | Expression BuildTaggingGraph(const vector<int>& sent, const vector<int...
function main (line 115) | int main(int argc, char** argv) {
FILE: cnn/examples/textcat.cc
type NeuralBagOfWords (line 31) | struct NeuralBagOfWords {
method NeuralBagOfWords (line 38) | explicit NeuralBagOfWords(Model& m) :
method Expression (line 45) | Expression BuildClassifier(const vector<int>& x, ComputationGraph& cg) {
type ConvLayer (line 61) | struct ConvLayer {
method ConvLayer (line 67) | ConvLayer(Model&m, int in_rows, int k_fold_rows, int filter_width, int...
method apply (line 87) | vector<Expression> apply(ComputationGraph& cg, const vector<Expression...
type ConvNet (line 116) | struct ConvNet {
method ConvNet (line 123) | explicit ConvNet(Model& m) :
method Expression (line 132) | Expression BuildClassifier(const vector<int>& x, ComputationGraph& cg,...
function IsCurrentPredictionCorrection (line 156) | bool IsCurrentPredictionCorrection(ComputationGraph& cg, int y_true) {
function Expression (line 166) | Expression CrossEntropyLoss(const Expression& y_pred, int y_true) {
function Expression (line 172) | Expression HingeLoss(const Expression& y_pred, int y_true) {
function main (line 177) | int main(int argc, char** argv) {
FILE: cnn/examples/tok-embed.cc
function UTF8Len (line 43) | inline unsigned int UTF8Len(unsigned char x) {
type PrefixNode (line 53) | struct PrefixNode {
method PrefixNode (line 54) | PrefixNode() :
type PrefixCode (line 76) | struct PrefixCode {
method PrefixCode (line 77) | PrefixCode() : params_allocated(false) {}
method PrefixNode (line 79) | PrefixNode* add(const string& pfc) {
method AllocateParameters_rec (line 96) | void AllocateParameters_rec(Model& m, unsigned dim, PrefixNode* n) {
method AllocateParameters (line 111) | void AllocateParameters(Model& m, unsigned dim) {
type SymbolEmbedding (line 121) | struct SymbolEmbedding {
method SymbolEmbedding (line 122) | SymbolEmbedding(Model& m, unsigned n, unsigned dim) {
method new_graph (line 125) | void new_graph(ComputationGraph& g) { cg = &g; }
method Expression (line 126) | Expression embed(unsigned label_id) {
type PrefixCodeDecoder (line 133) | struct PrefixCodeDecoder {
method PrefixCodeDecoder (line 137) | explicit PrefixCodeDecoder(Model& model, PrefixCode* pc) :
method Expression (line 141) | Expression loss(ComputationGraph& cg, const Expression& v, const strin...
type BiCharLSTM (line 171) | struct BiCharLSTM {
method BiCharLSTM (line 181) | explicit BiCharLSTM(Model& model) :
method Expression (line 192) | Expression embed(ComputationGraph& cg, const vector<unsigned>& x) {
function main (line 221) | int main(int argc, char** argv) {
FILE: cnn/examples/xor-batch-lookup.cc
function main (line 16) | int main(int argc, char** argv) {
FILE: cnn/examples/xor-batch.cc
function main (line 16) | int main(int argc, char** argv) {
FILE: cnn/examples/xor-xent.cc
function main (line 16) | int main(int argc, char** argv) {
FILE: cnn/examples/xor.cc
function main (line 16) | int main(int argc, char** argv) {
FILE: cnn/pycnn/pycnn_viz.py
function new_index (line 7) | def new_index():
function init (line 12) | def init(random_seed=None): pass
class SimpleConcreteDim (line 14) | class SimpleConcreteDim(object):
method __init__ (line 15) | def __init__(self, nrows, ncols, inferred):
method __getitem__ (line 19) | def __getitem__(self, key): return [self.nrows, self.ncols][key]
method __iter__ (line 20) | def __iter__(self): return iter([self.nrows, self.ncols])
method __str__ (line 21) | def __str__(self): return 'Dim(%s,%s)' % (self.nrows, self.ncols)
method __eq__ (line 22) | def __eq__(self, other): return isinstance(other, SimpleConcreteDim) a...
method __ne__ (line 23) | def __ne__(self, other): return not self==other
method __hash__ (line 24) | def __hash__(self): return hash((self.nrows, self.ncols))
method isvalid (line 25) | def isvalid(self): return True
method invalid (line 26) | def invalid(self): return False
class InvalidConcreteDim (line 28) | class InvalidConcreteDim(object):
method __init__ (line 29) | def __init__(self, a_dim=None, b_dim=None):
method __getitem__ (line 32) | def __getitem__(self, key): return None
method __repr__ (line 33) | def __repr__(self):
method __str__ (line 38) | def __str__(self): return repr(self)
method isvalid (line 39) | def isvalid(self): return False
method invalid (line 40) | def invalid(self): return True
function make_dim (line 44) | def make_dim(a, b=None, inferred=False):
function ensure_freshness (line 65) | def ensure_freshness(a):
function copy_dim (line 69) | def copy_dim(a):
function ensure_same_dim (line 74) | def ensure_same_dim(a,b):
function ensure_mul_dim (line 81) | def ensure_mul_dim(a,b):
function ensure_all_same_dim (line 88) | def ensure_all_same_dim(xs):
function _add (line 99) | def _add(a, b): return GVExpr('add', [a,b], ensure_same_dim(a,b))
function _mul (line 100) | def _mul(a, b): return GVExpr('mul', [a,b], ensure_mul_dim(a,b))
function _neg (line 101) | def _neg(a): return GVExpr('neg', [a], copy_dim(a))
function _scalarsub (line 102) | def _scalarsub(a, b): return GVExpr('scalarsub', [a,b], copy_dim(b))
function _cadd (line 103) | def _cadd(a, b): return GVExpr('cadd', [a,b], copy_dim(a))
function _cmul (line 104) | def _cmul(a, b): return GVExpr('cmul', [a,b], copy_dim(a))
function _cdiv (line 105) | def _cdiv(a, b): return GVExpr('cdiv', [a,b], copy_dim(a))
class Expression (line 107) | class Expression(object): #{{{
method __init__ (line 108) | def __init__(self, name, args, dim):
method cg (line 115) | def cg(self): return cg()
method get_cg_version (line 116) | def get_cg_version(self): return self.cg_version
method get_vindex (line 117) | def get_vindex(self): return self.vindex
method __repr__ (line 119) | def __repr__(self): return str(self)
method __str__ (line 120) | def __str__(self): return '%s([%s], %s, %s/%s)' % (self.name, ', '.joi...
method __getitem__ (line 121) | def __getitem__(self, i): return None
method __getslice__ (line 122) | def __getslice__(self, i, j): return None
method scalar_value (line 123) | def scalar_value(self, recalculate=False): return 0.0
method vec_value (line 124) | def vec_value(self, recalculate=False): return []
method npvalue (line 125) | def npvalue(self, recalculate=False): return None
method value (line 126) | def value(self, recalculate=False): return None
method forward (line 127) | def forward(self, recalculate=False): return None
method set (line 128) | def set(self, x): pass
method backward (line 130) | def backward(self): pass
method __add__ (line 132) | def __add__(self, other):
method __mul__ (line 138) | def __mul__(self, other):
method __div__ (line 144) | def __div__(self, other):
method __neg__ (line 148) | def __neg__(self): return _neg(self)
method __sub__ (line 149) | def __sub__(self, other):
method init_row (line 157) | def init_row(self, i, row): pass
function GVExpr (line 160) | def GVExpr(name, args, dim):
class Model (line 166) | class Model(object):
method __init__ (line 167) | def __init__(self):
method add_parameters (line 172) | def add_parameters(self, name, dim, scale=0):
method add_lookup_parameters (line 179) | def add_lookup_parameters(self, name, dim):
method __getitem__ (line 187) | def __getitem__(self, name): return self.named_params[name]
method __contains__ (line 188) | def __contains__(self, name): return name in self.named_params
method save (line 189) | def save(self, fname): pass
method load (line 190) | def load(self, fname): pass
function cg_version (line 196) | def cg_version(): return _cg._cg_version
function renew_cg (line 197) | def renew_cg(): return _cg.renew()
function cg (line 199) | def cg():
class ComputationGraph (line 203) | class ComputationGraph(object):
method __init__ (line 204) | def __init__(self, guard=0):
method renew (line 208) | def renew(self):
method version (line 213) | def version(self): return self._cg_version
method parameters (line 215) | def parameters(self, params):
method forward_scalar (line 219) | def forward_scalar(self): return 0.0
method inc_forward_scalar (line 220) | def inc_forward_scalar(self): return 0.0
method forward_vec (line 221) | def forward_vec(self): return []
method inc_forward_vec (line 222) | def inc_forward_vec(self): return []
method forward (line 223) | def forward(self): return None
method inc_forward (line 224) | def inc_forward(self): return None
method backward (line 225) | def backward(self): return None
function parameter (line 231) | def parameter(p):
function scalarInput (line 235) | def scalarInput(s): return GVExpr('scalarInput', [s], make_dim(1, inferr...
function vecInput (line 236) | def vecInput(dim): return GVExpr('vecInput', [dim], make_dim(dim))
function inputVector (line 237) | def inputVector(v): return GVExpr('inputVector', [v], make_dim(len(v), i...
function matInput (line 238) | def matInput(d1, d2): return GVExpr('matInput', [d1, d2], make_dim(d1, d2))
function inputMatrix (line 239) | def inputMatrix(v, d): return GVExpr('inputMatrix', [v, d], make_dim(d, ...
function lookup (line 240) | def lookup(p, index=0, update=True): return GVExpr('lookup', [p, index, ...
function lookup_batch (line 241) | def lookup_batch(p, indices, update=True): return GVExpr('lookup_batch',...
function pick (line 242) | def pick(a, index=0): return GVExpr('pick', [a, index], make_dim(1, infe...
function pick_batch (line 243) | def pick_batch(a, indices): return GVExpr('pick_batch', [a, indices], ma...
function hinge (line 244) | def hinge(x, index, m=1.0): return GVExpr('hinge', [x, index, m], copy_d...
function nobackprop (line 246) | def nobackprop(x): return GVExpr('nobackprop', [x], copy_dim(x))
function cdiv (line 249) | def cdiv(x, y): return GVExpr('cdiv', [x,y], ensure_same_dim(x,y))
function colwise_add (line 250) | def colwise_add(x, y):
function trace_of_product (line 259) | def trace_of_product(x, y): return GVExpr('trace_of_product', [x,y], ens...
function cwise_multiply (line 260) | def cwise_multiply(x, y): return GVExpr('cwise_multiply', [x,y], ensure_...
function dot_product (line 261) | def dot_product(x, y): return GVExpr('dot_product', [x,y], ensure_same_d...
function squared_distance (line 262) | def squared_distance(x, y): return GVExpr('squared_distance', [x,y], ens...
function l1_distance (line 263) | def l1_distance(x, y): return GVExpr('l1_distance', [x,y], ensure_same_d...
function binary_log_loss (line 264) | def binary_log_loss(x, y): return GVExpr('binary_log_loss', [x,y], ensur...
function conv1d_narrow (line 265) | def conv1d_narrow(x, y):
function conv1d_wide (line 273) | def conv1d_wide(x, y):
function tanh (line 283) | def tanh(x): return GVExpr('tanh', [x], copy_dim(x))
function exp (line 284) | def exp(x): return GVExpr('exp', [x], copy_dim(x))
function square (line 285) | def square(x): return GVExpr('square', [x], copy_dim(x))
function cube (line 286) | def cube(x): return GVExpr('cube', [x], copy_dim(x))
function log (line 287) | def log(x): return GVExpr('log', [x], copy_dim(x))
function logistic (line 288) | def logistic(x): return GVExpr('logistic', [x], copy_dim(x))
function rectify (line 289) | def rectify(x): return GVExpr('rectify', [x], copy_dim(x))
function log_softmax (line 290) | def log_softmax(x, restrict=None): return GVExpr('log_softmax', [x,restr...
function softmax (line 291) | def softmax(x): return GVExpr('softmax', [x], copy_dim(x))
function softsign (line 292) | def softsign(x): return GVExpr('softsign', [x], copy_dim(x))
function bmin (line 293) | def bmin(x, y): return GVExpr('bmin', [x,y], ensure_same_dim(x,y))
function bmax (line 294) | def bmax(x, y): return GVExpr('bmax', [x,y], ensure_same_dim(x,y))
function transpose (line 295) | def transpose(x): return GVExpr('transpose', [x], x.dim[::-1] if x.dim.i...
function sum_cols (line 296) | def sum_cols(x): return GVExpr('sum_cols', [x], make_dim(x.dim[0],1) if ...
function sum_batches (line 298) | def sum_batches(x): return GVExpr('sum_batches', [x], copy_dim(x))
function fold_rows (line 301) | def fold_rows(x, nrows=2):
function pairwise_rank_loss (line 309) | def pairwise_rank_loss(x, y, m=1.0): return GVExpr('pairwise_rank_loss',...
function poisson_loss (line 310) | def poisson_loss(x, y): return GVExpr('poisson_loss', [x,y], copy_dim(x))
function huber_distance (line 311) | def huber_distance(x, y, c=1.345): return GVExpr('huber_distance', [x,y,...
function kmax_pooling (line 313) | def kmax_pooling(x, k): return GVExpr('kmax_pooling', [x,k], make_dim(x....
function pickneglogsoftmax (line 314) | def pickneglogsoftmax(x, v): return GVExpr('pickneglogsoftmax', [x,v], m...
function pickneglogsoftmax_batch (line 315) | def pickneglogsoftmax_batch(x, vs): return GVExpr('pickneglogsoftmax_bat...
function kmh_ngram (line 317) | def kmh_ngram(x, n): return GVExpr('kmh_ngram', [x,n], make_dim(x.dim[0]...
function pickrange (line 318) | def pickrange(x, v, u): return GVExpr('pickrange', [x,v,u], make_dim(u-v...
function noise (line 320) | def noise(x, stddev): return GVExpr('noise', [x,stddev], copy_dim(x))
function dropout (line 321) | def dropout(x, p): return GVExpr('dropout', [x,p], copy_dim(x))
function block_dropout (line 322) | def block_dropout(x, p): return GVExpr('block_dropout', [x,p], copy_dim(x))
function reshape (line 324) | def reshape(x, d): return GVExpr('reshape', [x,d], make_dim(d))
function esum (line 325) | def esum(xs): return GVExpr('esum', xs, ensure_all_same_dim(xs))
function average (line 326) | def average(xs): return GVExpr('average', xs, ensure_all_same_dim(xs))
function emax (line 327) | def emax(xs): return GVExpr('emax', xs, ensure_all_same_dim(xs))
function concatenate_cols (line 328) | def concatenate_cols(xs):
function concatenate (line 339) | def concatenate(xs):
function affine_transform (line 351) | def affine_transform(xs):
function new_builder_num (line 370) | def new_builder_num():
class _RNNBuilder (line 375) | class _RNNBuilder(object):
method new_graph (line 376) | def new_graph(self):
method start_new_sequence (line 380) | def start_new_sequence(self, es=None):
method add_input (line 383) | def add_input(self, e):
method add_input_to_prev (line 388) | def add_input_to_prev(self, prev, e):
method rewind_one_step (line 393) | def rewind_one_step(self):
method back (line 397) | def back(self):
method final_h (line 401) | def final_h(self):
method final_s (line 410) | def final_s(self):
method get_h (line 419) | def get_h(self, i):
method get_s (line 428) | def get_s(self, i):
method initial_state (line 437) | def initial_state(self,vecs=None):
method initial_state_from_raw_vectors (line 447) | def initial_state_from_raw_vectors(self,vecs=None):
class SimpleRNNBuilder (line 462) | class SimpleRNNBuilder(_RNNBuilder):
method __init__ (line 463) | def __init__(self, layers, input_dim, hidden_dim, model):
method whoami (line 471) | def whoami(self): return "SimpleRNNBuilder"
class LSTMBuilder (line 472) | class LSTMBuilder(_RNNBuilder):
method __init__ (line 473) | def __init__(self, layers, input_dim, hidden_dim, model):
method whoami (line 481) | def whoami(self): return "LSTMBuilder"
class FastLSTMBuilder (line 482) | class FastLSTMBuilder(_RNNBuilder):
method __init__ (line 483) | def __init__(self, layers, input_dim, hidden_dim, model):
method whoami (line 491) | def whoami(self): return "FastLSTMBuilder"
class BiRNNBuilder (line 493) | class BiRNNBuilder(object):
method __init__ (line 500) | def __init__(self, num_layers, input_dim, hidden_dim, model, rnn_build...
method whoami (line 519) | def whoami(self): return "BiRNNBuilder"
method add_inputs (line 521) | def add_inputs(self, es):
method transduce (line 551) | def transduce(self, es):
class RNNState (line 579) | class RNNState(object): # {{{
method __init__ (line 580) | def __init__(self, builder, state_idx=-1, prev_state=None, out=None):
method add_input (line 586) | def add_input(self, x): # x: Expression
method add_inputs (line 595) | def add_inputs(self, xs):
method transduce (line 605) | def transduce(self, xs):
method output (line 608) | def output(self): return self._out
method prev (line 610) | def prev(self): return self._prev
method b (line 611) | def b(self): return self.builder
method get_state_idx (line 612) | def get_state_idx(self): return self.state_idx
class StackedRNNState (line 615) | class StackedRNNState(object):
method __init__ (line 618) | def __init__(self, states, prev=None):
method add_input (line 622) | def add_input(self, x):
method output (line 630) | def output(self): return self.states[-1].output()
method prev (line 632) | def prev(self): return self.prev
method h (line 633) | def h(self): return [s.h() for s in self.states]
method s (line 634) | def s(self): return [s.s() for s in self.states]
method add_inputs (line 636) | def add_inputs(self, xs):
class Trainer (line 649) | class Trainer(object):
method update (line 650) | def update(self, s=1.0): pass
method update_epoch (line 651) | def update_epoch(self, r = 1.0): pass
method status (line 652) | def status(self): pass
class SimpleSGDTrainer (line 653) | class SimpleSGDTrainer(Trainer):
method __init__ (line 654) | def __init__(self, m, lam = 1e-6, e0 = 0.1): pass
class MomentumSGDTrainer (line 655) | class MomentumSGDTrainer(Trainer):
method __init__ (line 656) | def __init__(self, m, lam = 1e-6, e0 = 0.01, mom = 0.9): pass
class AdagradTrainer (line 657) | class AdagradTrainer(Trainer):
method __init__ (line 658) | def __init__(self, m, lam = 1e-6, e0 = 0.1, eps = 1e-20): pass
class AdadeltaTrainer (line 659) | class AdadeltaTrainer(Trainer):
method __init__ (line 660) | def __init__(self, m, lam = 1e-6, eps = 1e-6, rho = 0.95): pass
class AdamTrainer (line 661) | class AdamTrainer(Trainer):
method __init__ (line 662) | def __init__(self, m, lam = 1e-6, alpha = 0.001, beta_1 = 0.9, beta_2 ...
function shape_str (line 680) | def shape_str(e_dim):
class GVNode (line 695) | class GVNode(object):
method __init__ (line 696) | def __init__(self, name, input_dim, label, output_dim, children, featu...
method __iter__ (line 705) | def __iter__(self): return iter([self.name, self.input_dim, self.label...
method __repr__ (line 706) | def __repr__(self): return 'GVNode(%s)' % ', '.join(map(str, self))
method __str__ (line 707) | def __str__(self): return repr(self)
function make_network_graph (line 709) | def make_network_graph(compact, expression_names, lookup_names):
function parents_of (line 870) | def parents_of(n, nodes):
function collapse_birnn_states (line 878) | def collapse_birnn_states(nodes, compact):
function PrintGraphviz (line 929) | def PrintGraphviz(compact=False, show_dims=True, expression_names=None, ...
FILE: cnn/pyexamples/attention.py
function embedd_sentence (line 33) | def embedd_sentence(model, sentence):
function run_lstm (line 42) | def run_lstm(model, init_state, input_vecs):
function encode_sentence (line 53) | def encode_sentence(model, enc_fwd_lstm, enc_bwd_lstm, sentence):
function attend (line 64) | def attend(model, input_vectors, state):
function decode (line 79) | def decode(model, dec_lstm, vectors, output):
function generate (line 100) | def generate(model, input, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
function get_loss (line 134) | def get_loss(model, input_sentence, output_sentence, enc_fwd_lstm, enc_b...
function train (line 141) | def train(model, sentence):
FILE: cnn/pyexamples/bilstmtagger.py
function read (line 14) | def read(fname):
function build_tagging_graph (line 66) | def build_tagging_graph(words, tags, model, builders):
function tag_sent (line 92) | def tag_sent(sent, model, builders):
FILE: cnn/pyexamples/cpu_vs_gpu.py
function do_cpu (line 11) | def do_cpu():
function do_gpu (line 19) | def do_gpu():
FILE: cnn/pyexamples/rnnlm.py
class RNNLanguageModel (line 15) | class RNNLanguageModel:
method __init__ (line 16) | def __init__(self, model, LAYERS, INPUT_DIM, HIDDEN_DIM, VOCAB_SIZE, b...
method BuildLMGraph (line 24) | def BuildLMGraph(self, sent):
method sample (line 44) | def sample(self, first=1, nchars=0, stop=-1):
FILE: cnn/pyexamples/rnnlm_transduce.py
class RNNLanguageModel (line 16) | class RNNLanguageModel:
method __init__ (line 17) | def __init__(self, model, LAYERS, INPUT_DIM, HIDDEN_DIM, VOCAB_SIZE, b...
method BuildLMGraph (line 25) | def BuildLMGraph(self, sent):
method sample (line 43) | def sample(self, first=1, nchars=0, stop=-1):
FILE: cnn/pyexamples/util.py
class Vocab (line 3) | class Vocab:
method __init__ (line 4) | def __init__(self, w2i=None):
method from_corpus (line 9) | def from_corpus(cls, corpus):
method size (line 15) | def size(self): return len(self.w2i.keys())
class CorpusReader (line 17) | class CorpusReader:
method __init__ (line 18) | def __init__(self, fname):
method __iter__ (line 20) | def __iter__(self):
class CharsCorpusReader (line 26) | class CharsCorpusReader:
method __init__ (line 27) | def __init__(self, fname, begin=None):
method __iter__ (line 30) | def __iter__(self):
FILE: cnn/rnnlm/lm.cc
function InitCommandLine (line 42) | void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
type RNNLanguageModel (line 76) | struct RNNLanguageModel {
method RNNLanguageModel (line 81) | explicit RNNLanguageModel(Model& model) : builder(LAYERS, INPUT_DIM, H...
method Expression (line 88) | Expression BuildLMGraph(const vector<int>& sent, ComputationGraph& cg,...
method RandomSample (line 122) | void RandomSample(int max_len = 200) {
function main (line 157) | int main(int argc, char** argv) {
FILE: cnn/tests/test-cnn.cc
type ConfigureCNNTest (line 5) | struct ConfigureCNNTest {
method ConfigureCNNTest (line 6) | ConfigureCNNTest() {
function BOOST_AUTO_TEST_CASE (line 24) | BOOST_AUTO_TEST_CASE( aligned_allocator ) {
FILE: cnn/tests/test-nodes.cc
type NodeTest (line 12) | struct NodeTest {
method NodeTest (line 13) | NodeTest() {
method print_vec (line 47) | std::string print_vec(const std::vector<T> vec) {
function BOOST_AUTO_TEST_CASE (line 66) | BOOST_AUTO_TEST_CASE( negate_gradient ) {
function BOOST_AUTO_TEST_CASE (line 75) | BOOST_AUTO_TEST_CASE( add_gradient ) {
function BOOST_AUTO_TEST_CASE (line 85) | BOOST_AUTO_TEST_CASE( addscalar_gradient ) {
function BOOST_AUTO_TEST_CASE (line 94) | BOOST_AUTO_TEST_CASE( scalaradd_gradient ) {
function BOOST_AUTO_TEST_CASE (line 103) | BOOST_AUTO_TEST_CASE( subtract_gradient ) {
function BOOST_AUTO_TEST_CASE (line 113) | BOOST_AUTO_TEST_CASE( scalarsubtract_gradient ) {
function BOOST_AUTO_TEST_CASE (line 122) | BOOST_AUTO_TEST_CASE( subtractscalar_gradient ) {
function BOOST_AUTO_TEST_CASE (line 131) | BOOST_AUTO_TEST_CASE( multiply_gradient ) {
function BOOST_AUTO_TEST_CASE (line 142) | BOOST_AUTO_TEST_CASE( multiply_batch_gradient ) {
function BOOST_AUTO_TEST_CASE (line 153) | BOOST_AUTO_TEST_CASE( affine_gradient ) {
function BOOST_AUTO_TEST_CASE (line 165) | BOOST_AUTO_TEST_CASE( affine_batch_gradient ) {
function BOOST_AUTO_TEST_CASE (line 177) | BOOST_AUTO_TEST_CASE( affine_batch2_gradient ) {
function BOOST_AUTO_TEST_CASE (line 189) | BOOST_AUTO_TEST_CASE( multiplyscalar_gradient ) {
function BOOST_AUTO_TEST_CASE (line 198) | BOOST_AUTO_TEST_CASE( scalarmultiply_gradient ) {
function BOOST_AUTO_TEST_CASE (line 207) | BOOST_AUTO_TEST_CASE( dividescalar_gradient ) {
function BOOST_AUTO_TEST_CASE (line 216) | BOOST_AUTO_TEST_CASE( cdiv_gradient ) {
function BOOST_AUTO_TEST_CASE (line 226) | BOOST_AUTO_TEST_CASE( colwise_add_gradient ) {
function BOOST_AUTO_TEST_CASE (line 243) | BOOST_AUTO_TEST_CASE( sqrt_gradient ) {
function BOOST_AUTO_TEST_CASE (line 252) | BOOST_AUTO_TEST_CASE( erf_gradient ) {
function BOOST_AUTO_TEST_CASE (line 261) | BOOST_AUTO_TEST_CASE( tanh_gradient ) {
function BOOST_AUTO_TEST_CASE (line 270) | BOOST_AUTO_TEST_CASE( exp_gradient ) {
function BOOST_AUTO_TEST_CASE (line 279) | BOOST_AUTO_TEST_CASE( square_gradient ) {
function BOOST_AUTO_TEST_CASE (line 288) | BOOST_AUTO_TEST_CASE( cube_gradient ) {
function BOOST_AUTO_TEST_CASE (line 297) | BOOST_AUTO_TEST_CASE( lgamma_gradient ) {
function BOOST_AUTO_TEST_CASE (line 306) | BOOST_AUTO_TEST_CASE( log_gradient ) {
function BOOST_AUTO_TEST_CASE (line 315) | BOOST_AUTO_TEST_CASE( logistic_gradient ) {
function BOOST_AUTO_TEST_CASE (line 324) | BOOST_AUTO_TEST_CASE( rectify_gradient ) {
function BOOST_AUTO_TEST_CASE (line 333) | BOOST_AUTO_TEST_CASE( hinge_gradient ) {
function BOOST_AUTO_TEST_CASE (line 342) | BOOST_AUTO_TEST_CASE( hingeptr_gradient ) {
function BOOST_AUTO_TEST_CASE (line 351) | BOOST_AUTO_TEST_CASE( log_softmax_gradient ) {
function BOOST_AUTO_TEST_CASE (line 360) | BOOST_AUTO_TEST_CASE( restricted_log_softmax_gradient ) {
function BOOST_AUTO_TEST_CASE (line 370) | BOOST_AUTO_TEST_CASE( softmax_gradient ) {
function BOOST_AUTO_TEST_CASE (line 379) | BOOST_AUTO_TEST_CASE( softsign_gradient ) {
function BOOST_AUTO_TEST_CASE (line 388) | BOOST_AUTO_TEST_CASE( pow_gradient ) {
function BOOST_AUTO_TEST_CASE (line 398) | BOOST_AUTO_TEST_CASE( min_gradient ) {
function BOOST_AUTO_TEST_CASE (line 408) | BOOST_AUTO_TEST_CASE( max_gradient ) {
function BOOST_AUTO_TEST_CASE (line 441) | BOOST_AUTO_TEST_CASE( reshape_gradient ) {
function BOOST_AUTO_TEST_CASE (line 450) | BOOST_AUTO_TEST_CASE( transpose_gradient ) {
function BOOST_AUTO_TEST_CASE (line 459) | BOOST_AUTO_TEST_CASE( trace_of_product_gradient ) {
function BOOST_AUTO_TEST_CASE (line 468) | BOOST_AUTO_TEST_CASE( cwise_multiply_gradient ) {
function BOOST_AUTO_TEST_CASE (line 478) | BOOST_AUTO_TEST_CASE( dot_product_gradient ) {
function BOOST_AUTO_TEST_CASE (line 487) | BOOST_AUTO_TEST_CASE( squared_distance_gradient ) {
function BOOST_AUTO_TEST_CASE (line 496) | BOOST_AUTO_TEST_CASE( huber_distance_gradient ) {
function BOOST_AUTO_TEST_CASE (line 505) | BOOST_AUTO_TEST_CASE( l1_distance_gradient ) {
function BOOST_AUTO_TEST_CASE (line 514) | BOOST_AUTO_TEST_CASE( binary_log_loss_gradient ) {
function BOOST_AUTO_TEST_CASE (line 523) | BOOST_AUTO_TEST_CASE( pairwise_rank_loss_gradient ) {
function BOOST_AUTO_TEST_CASE (line 545) | BOOST_AUTO_TEST_CASE( pick_gradient ) {
function BOOST_AUTO_TEST_CASE (line 554) | BOOST_AUTO_TEST_CASE( pickptr_gradient ) {
function BOOST_AUTO_TEST_CASE (line 563) | BOOST_AUTO_TEST_CASE( pick_batch_gradient ) {
function BOOST_AUTO_TEST_CASE (line 573) | BOOST_AUTO_TEST_CASE( pickrange_gradient ) {
function BOOST_AUTO_TEST_CASE (line 582) | BOOST_AUTO_TEST_CASE( pickneglogsoftmax_gradient ) {
function BOOST_AUTO_TEST_CASE (line 591) | BOOST_AUTO_TEST_CASE( pickneglogsoftmax_batch_gradient ) {
FILE: get_dictionary.py
function is_next_open_bracket (line 1) | def is_next_open_bracket(line, start_idx):
function get_between_brackets (line 9) | def get_between_brackets(line, start_idx):
function get_dict (line 18) | def get_dict(lines):
FILE: get_oracle.py
function unkify (line 5) | def unkify(tokens, words_dict):
function is_next_open_bracket (line 72) | def is_next_open_bracket(line, start_idx):
function get_between_brackets (line 80) | def get_between_brackets(line, start_idx):
function get_tags_tokens_lowercase (line 98) | def get_tags_tokens_lowercase(line):
function get_nonterminal (line 120) | def get_nonterminal(line, start_idx):
function get_actions (line 131) | def get_actions(line):
function main (line 162) | def main():
FILE: get_oracle_gen.py
function unkify (line 5) | def unkify(tokens, words_dict):
function is_next_open_bracket (line 72) | def is_next_open_bracket(line, start_idx):
function get_between_brackets (line 80) | def get_between_brackets(line, start_idx):
function get_tags_tokens_lowercase (line 98) | def get_tags_tokens_lowercase(line):
function get_nonterminal (line 120) | def get_nonterminal(line, start_idx):
function get_actions (line 131) | def get_actions(line):
function main (line 162) | def main():
FILE: interpreting-rnng/nt-parser-gen-attention-gated-stack-only.cc
function InitCommandLine (line 63) | void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
type ParserBuilder (line 96) | struct ParserBuilder {
method ParserBuilder (line 132) | explicit ParserBuilder(Model* model, const unordered_map<unsigned, vec...
method IsActionForbidden_Generative (line 174) | static bool IsActionForbidden_Generative(const string& a, char prev_a,...
method log_prob_parser (line 194) | vector<unsigned> log_prob_parser(ComputationGraph* hg,
function signal_callback_handler (line 536) | void signal_callback_handler(int /* signum */) {
function main (line 545) | int main(int argc, char** argv) {
FILE: nt-parser/compressed-fstream.h
function namespace (line 12) | namespace cnn {
FILE: nt-parser/eval.cc
type parser (line 12) | namespace parser {
function EvalBResults (line 14) | EvalBResults Evaluate(const string& ref_fname, const string& hyp_fname) {
FILE: nt-parser/eval.h
function namespace (line 7) | namespace parser {
FILE: nt-parser/nt-parser-gen.cc
function InitCommandLine (line 63) | void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
type ParserBuilder (line 96) | struct ParserBuilder {
method ParserBuilder (line 125) | explicit ParserBuilder(Model* model, const unordered_map<unsigned, vec...
method IsActionForbidden_Generative (line 160) | static bool IsActionForbidden_Generative(const string& a, char prev_a,...
method log_prob_parser (line 180) | vector<unsigned> log_prob_parser(ComputationGraph* hg,
function signal_callback_handler (line 440) | void signal_callback_handler(int /* signum */) {
function main (line 449) | int main(int argc, char** argv) {
FILE: nt-parser/nt-parser.cc
function InitCommandLine (line 66) | void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
type ParserBuilder (line 104) | struct ParserBuilder {
method ParserBuilder (line 135) | explicit ParserBuilder(Model* model, const unordered_map<unsigned, vec...
method IsActionForbidden_Discriminative (line 181) | static bool IsActionForbidden_Discriminative(const string& a, char pre...
method log_prob_parser (line 222) | vector<unsigned> log_prob_parser(ComputationGraph* hg,
type ParserState (line 510) | struct ParserState {
type ParserStateCompare (line 531) | struct ParserStateCompare {
method prune (line 537) | static void prune(vector<ParserState>& pq, unsigned k) {
method all_complete (line 549) | static bool all_complete(const vector<ParserState>& pq) {
method log_prob_parser_beam (line 561) | vector<unsigned> log_prob_parser_beam(ComputationGraph* hg,
function signal_callback_handler (line 853) | void signal_callback_handler(int /* signum */) {
function main (line 862) | int main(int argc, char** argv) {
FILE: nt-parser/oracle.cc
type parser (line 11) | namespace parser {
function is_ws (line 16) | inline bool is_ws(char x) { //check whether the character is a space o...
function is_not_ws (line 20) | inline bool is_not_ws(char x) {
FILE: nt-parser/oracle.h
function namespace (line 8) | namespace cnn { class Dict; }
function namespace (line 10) | namespace parser {
type Oracle (line 21) | struct Oracle {
function class (line 40) | class TopDownOracle : public Oracle {
function class (line 57) | class TopDownOracleGen : public Oracle {
function class (line 65) | class TopDownOracleGen2 : public Oracle {
FILE: nt-parser/pretrained.cc
type parser (line 10) | namespace parser {
function ReadEmbeddings_word2vec (line 12) | void ReadEmbeddings_word2vec(const string& fname,
FILE: nt-parser/pretrained.h
function namespace (line 8) | namespace cnn { struct Dict; }
function namespace (line 10) | namespace parser {
FILE: remove_dev_unk.py
function is_next_open_bracket (line 3) | def is_next_open_bracket(line, start_idx):
function get_between_brackets (line 11) | def get_between_brackets(line, start_idx):
function get_tags_tokens_lowercase (line 20) | def get_tags_tokens_lowercase(line):
function main (line 42) | def main():
FILE: utils/remove_dev_unk.py
function is_next_open_bracket (line 3) | def is_next_open_bracket(line, start_idx):
function get_between_brackets (line 11) | def get_between_brackets(line, start_idx):
function get_tags_tokens_lowercase (line 20) | def get_tags_tokens_lowercase(line):
function main (line 42) | def main():
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Condensed preview — 176 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (12,632K chars).
[
{
"path": ".gitignore",
"chars": 242,
"preview": "# Compiled Object files\n*.slo\n*.lo\n*.o\n*.obj\n\n# Precompiled Headers\n*.gch\n*.pch\n\n# Compiled Dynamic libraries\n*.so\n*.dyl"
},
{
"path": "CMakeLists.txt",
"chars": 785,
"preview": "project(cnn)\ncmake_minimum_required(VERSION 2.8 FATAL_ERROR)\n\nset(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)\nset(CMA"
},
{
"path": "README.md",
"chars": 10716,
"preview": "# Recurrent Neural Network Grammars\nCode for the [Recurrent Neural Network Grammars](https://arxiv.org/abs/1602.07776) p"
},
{
"path": "clusters-train-berk.txt",
"chars": 456390,
"preview": "00000000\tstimulating\t2\n00000000\tpaper-company\t2\n00000000\tSalvadoran\t2\n00000000\tharms\t2\n00000000\tcliched\t2\n00000000\twet\t2"
},
{
"path": "cmake/FindEigen3.cmake",
"chars": 2995,
"preview": "# - Try to find Eigen3 lib\n#\n# This module supports requiring a minimum version, e.g. you can do\n# find_package(Eigen3"
},
{
"path": "cnn/.gitignore",
"chars": 584,
"preview": "# cmake stuff\nbuild/\nTesting/\ncnn/Testing/\ncnn/tests.bin/\nCTestTestfile.cmake\nconfig.h\nMakefile\nCMakeCache.txt\nCMakeFile"
},
{
"path": "cnn/.gitmodules",
"chars": 124,
"preview": "[submodule \"external/easyloggingpp\"]\n\tpath = external/easyloggingpp\n\turl = https://github.com/easylogging/easyloggingpp."
},
{
"path": "cnn/.travis.yml",
"chars": 545,
"preview": "language: cpp\ncompiler:\n - gcc\n\ninstall:\n- if [ \"$CXX\" = \"g++\" ]; then export CXX=\"g++-4.8\" CC=\"gcc-4.8\"; fi\naddons:\n "
},
{
"path": "cnn/CMakeLists.txt",
"chars": 3450,
"preview": "project(cnn)\ncmake_minimum_required(VERSION 2.8 FATAL_ERROR)\n\nset(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)\n\n# CNN "
},
{
"path": "cnn/INSTALL.md",
"chars": 4802,
"preview": "# Installing the pyCNN module.\n\n(for instructions on installing on a computer with GPU, see below)\n\nFirst, get CNN:\n\n```"
},
{
"path": "cnn/LICENSE",
"chars": 11308,
"preview": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licens"
},
{
"path": "cnn/MEM.notes",
"chars": 491,
"preview": "The code that computes the l2 norm of the gradient is going to need\nscratch space on every device that CNN is using that"
},
{
"path": "cnn/README.md",
"chars": 3578,
"preview": "# cnn\nC++ neural network library\n\n#### Important: Eigen version requirement\n\nYou need the [development version of the Ei"
},
{
"path": "cnn/TODO.cnn",
"chars": 2244,
"preview": "PRIORITIES:\n\nMultiprocessor/single memory version has to get merged with good, clear examples [cdyer needs to try it out"
},
{
"path": "cnn/cmake/FindCNN.cmake",
"chars": 318,
"preview": "\nCMAKE_MINIMUM_REQUIRED(VERSION 2.8.7 FATAL_ERROR)\n\nINCLUDE(FindPackageHandleStandardArgs)\n\nFIND_LIBRARY(TH_LIBRARY TH)\n"
},
{
"path": "cnn/cmake/FindEigen3.cmake",
"chars": 3185,
"preview": "# - Try to find Eigen3 lib\n#\n# This module supports requiring a minimum version, e.g. you can do\n# find_package(Eigen3"
},
{
"path": "cnn/cnn/CMakeLists.txt",
"chars": 3032,
"preview": "# ########## cnn library ##########\n# Sources:\nset(cnn_library_SRCS\n cfsm-builder.cc\n cnn.cc\n conv.cc\n deep-"
},
{
"path": "cnn/cnn/aligned-mem-pool.cc",
"chars": 30,
"preview": "#include \"aligned-mem-pool.h\"\n"
},
{
"path": "cnn/cnn/aligned-mem-pool.h",
"chars": 1264,
"preview": "#ifndef CNN_ALIGNED_MEM_POOL_H\n#define CNN_ALIGNED_MEM_POOL_H\n\n#include <iostream>\n#include \"cnn/mem.h\"\n\nnamespace cnn {"
},
{
"path": "cnn/cnn/c2w.h",
"chars": 1867,
"preview": "#ifndef CNN_C2W_H_\n#define CNN_C2W_H_\n\n#include <vector>\n#include <map>\n\n#include \"cnn/cnn.h\"\n#include \"cnn/model.h\"\n#in"
},
{
"path": "cnn/cnn/cfsm-builder.cc",
"chars": 5601,
"preview": "#include \"cnn/cfsm-builder.h\"\n\n#include <fstream>\n#include <iostream>\n\nusing namespace std;\n\nnamespace cnn {\n\nusing name"
},
{
"path": "cnn/cnn/cfsm-builder.h",
"chars": 2783,
"preview": "#ifndef CNN_CFSMBUILDER_H\n#define CNN_CFSMBUILDER_H\n\n#include <vector>\n#include <string>\n#include \"cnn/cnn.h\"\n#include \""
},
{
"path": "cnn/cnn/cnn-helper.h",
"chars": 363,
"preview": "#ifndef CNN_HELPER_H_\n#define CNN_HELPER_H_\n\n#include <string>\n\n/// helper functions\n\nnamespace cnn {\n\n/**\n this fix "
},
{
"path": "cnn/cnn/cnn.cc",
"chars": 7955,
"preview": "#include \"cnn/cnn.h\"\n#include \"cnn/exec.h\"\n#include \"cnn/nodes.h\"\n#include \"cnn/param-nodes.h\"\n#include \"cnn/aligned-mem"
},
{
"path": "cnn/cnn/cnn.h",
"chars": 9224,
"preview": "#ifndef CNN_CNN_H_\n#define CNN_CNN_H_\n\n#include <string>\n#include <vector>\n#include <iostream>\n#include <initializer_lis"
},
{
"path": "cnn/cnn/conv.cc",
"chars": 10744,
"preview": "#include \"cnn/conv.h\"\n\n#include <sstream>\n#include <limits>\n#include <cmath>\n#include <stdexcept>\n\n#include \"cnn/functor"
},
{
"path": "cnn/cnn/conv.h",
"chars": 3194,
"preview": "#ifndef CNN_CONV_H_\n#define CNN_CONV_H_\n\n#include \"cnn/cnn.h\"\n\nnamespace cnn {\n\nstruct AddVectorToAllColumns : public No"
},
{
"path": "cnn/cnn/cuda.cc",
"chars": 5590,
"preview": "#include <iostream>\n#include <vector>\n#include <algorithm>\n\n#include \"cnn/cnn.h\"\n#include \"cnn/cuda.h\"\n\nusing namespace "
},
{
"path": "cnn/cnn/cuda.h",
"chars": 1627,
"preview": "#ifndef CNN_CUDA_H\n#define CNN_CUDA_H\n#if HAVE_CUDA\n\n#include <vector>\n#include <cassert>\n#include <utility>\n#include <s"
},
{
"path": "cnn/cnn/deep-lstm.cc",
"chars": 5350,
"preview": "#include \"cnn/deep-lstm.h\"\n\n#include <string>\n#include <cassert>\n#include <vector>\n#include <iostream>\n\n#include \"cnn/no"
},
{
"path": "cnn/cnn/deep-lstm.h",
"chars": 1558,
"preview": "#ifndef CNN_DEEP_LSTM_H_\n#define CNN_DEEP_LSTM_H_\n\n#include \"cnn/cnn.h\"\n#include \"cnn/rnn.h\"\n#include \"cnn/expr.h\"\n\nusin"
},
{
"path": "cnn/cnn/devices.cc",
"chars": 2207,
"preview": "#include \"cnn/devices.h\"\n\n#include <iostream>\n\n#include \"cnn/cuda.h\"\n\nusing namespace std;\n\nnamespace cnn {\n\nDevice::~De"
},
{
"path": "cnn/cnn/devices.h",
"chars": 977,
"preview": "#ifndef CNN_DEVICES_H\n#define CNN_DEVICES_H\n\n#include <string>\n#include \"cnn/aligned-mem-pool.h\"\n#include \"cnn/cuda.h\"\n\n"
},
{
"path": "cnn/cnn/dict.cc",
"chars": 776,
"preview": "#include \"dict.h\"\n\n#include <string>\n#include <vector>\n#include <sstream>\n\nusing namespace std;\n\nnamespace cnn {\n\nstd::v"
},
{
"path": "cnn/cnn/dict.h",
"chars": 2695,
"preview": "#ifndef CNN_DICT_H_\n#define CNN_DICT_H_\n\n#include <cassert>\n#include <unordered_map>\n#include <string>\n#include <vector>"
},
{
"path": "cnn/cnn/dim.cc",
"chars": 478,
"preview": "#include \"cnn/dim.h\"\n\n#include <iostream>\n\nusing namespace std;\n\nnamespace cnn {\n\nostream& operator<<(ostream& os, const"
},
{
"path": "cnn/cnn/dim.h",
"chars": 3121,
"preview": "#ifndef CNN_DIM_H\n#define CNN_DIM_H\n\n#include <cassert>\n#include <initializer_list>\n#include <type_traits>\n#include <std"
},
{
"path": "cnn/cnn/except.h",
"chars": 774,
"preview": "#ifndef CNN_EXCEPT_H_\n#define CNN_EXCEPT_H_\n\n#include <stdexcept>\n\nnamespace cnn {\n\n// if CNN exhausts its memory pool\nc"
},
{
"path": "cnn/cnn/exec.cc",
"chars": 4708,
"preview": "#include \"cnn/exec.h\"\n\n#include \"cnn/param-nodes.h\"\n\nusing namespace std;\n\nnamespace cnn {\n\nExecutionEngine::~ExecutionE"
},
{
"path": "cnn/cnn/exec.h",
"chars": 1390,
"preview": "#ifndef CNN_EXEC_H\n#define CNN_EXEC_H\n\n#include \"cnn/cnn.h\"\n\nnamespace cnn {\n\nclass ExecutionEngine {\n public:\n virtual"
},
{
"path": "cnn/cnn/expr.cc",
"chars": 12113,
"preview": "#include \"cnn/expr.h\"\n\n#include <initializer_list>\n\n#include \"cnn/nodes.h\"\n#include \"cnn/conv.h\"\n\nnamespace cnn { namesp"
},
{
"path": "cnn/cnn/expr.h",
"chars": 8738,
"preview": "#ifndef CNN_EXPR_H\n#define CNN_EXPR_H\n\n#include \"cnn/cnn.h\"\n#include \"cnn/nodes.h\"\n\nnamespace cnn { namespace expr {\n\nst"
},
{
"path": "cnn/cnn/fast-lstm.cc",
"chars": 5716,
"preview": "#include \"cnn/fast-lstm.h\"\n\n#include <string>\n#include <cassert>\n#include <vector>\n#include <iostream>\n\n#include \"cnn/no"
},
{
"path": "cnn/cnn/fast-lstm.h",
"chars": 2043,
"preview": "#ifndef CNN_FAST_LSTM_H_\n#define CNN_FAST_LSTM_H_\n\n#include \"cnn/cnn.h\"\n#include \"cnn/rnn.h\"\n#include \"cnn/expr.h\"\n\nusin"
},
{
"path": "cnn/cnn/functors.h",
"chars": 7794,
"preview": "#ifndef CNN_GPU_FUNCTORS_H\n#define CNN_GPU_FUNCTORS_H\n\n#include <cstdint>\n#include <limits>\n\n#if HAVE_CUDA\n# define CNN"
},
{
"path": "cnn/cnn/gpu-kernels.h",
"chars": 1402,
"preview": "#ifndef CNN_GPU_KERNELS_H\n#define CNN_GPU_KERNELS_H\n\n#include \"cnn/cuda.h\"\n\nnamespace cnn {\nnamespace gpu {\n\ntemplate<ty"
},
{
"path": "cnn/cnn/gpu-ops.cu",
"chars": 9366,
"preview": "#include \"cnn/cuda.h\"\n#include \"cnn/gpu-ops.h\"\n#include \"cnn/gpu-kernels.h\"\n#include \"cnn/functors.h\"\n\nnamespace cnn {\nn"
},
{
"path": "cnn/cnn/gpu-ops.h",
"chars": 1819,
"preview": "#ifndef CNN_GPU_OPS_H\n#define CNN_GPU_OPS_H\n\nnamespace cnn {\nnamespace gpu {\n\nvoid vpairwise_rank_loss(int n, float marg"
},
{
"path": "cnn/cnn/grad-check.cc",
"chars": 3033,
"preview": "#include \"cnn/grad-check.h\"\n\n#include <cassert>\n#include <iostream>\n\n#include \"cnn/model.h\"\n#include \"cnn/cnn.h\"\n#includ"
},
{
"path": "cnn/cnn/grad-check.h",
"chars": 284,
"preview": "#ifndef CNN_GRAD_CHECK_H\n#define CNN_GRAD_CHECK_H\n\nnamespace cnn {\n\nclass Model;\nstruct ComputationGraph;\n\n// verbosity "
},
{
"path": "cnn/cnn/graph.cc",
"chars": 899,
"preview": "#include \"cnn/graph.h\"\n#include \"cnn/cnn.h\"\n#include <vector>\n#include \"cnn/cnn-helper.h\"\n\nusing namespace std;\n\nnamespa"
},
{
"path": "cnn/cnn/graph.h",
"chars": 151,
"preview": "#ifndef CNN_GRAPH_H\n#define CNN_GRAPH_H\n\nnamespace cnn {\nstruct ComputationGraph;\nvoid GraphOptimize(ComputationGraph* c"
},
{
"path": "cnn/cnn/gru.cc",
"chars": 4142,
"preview": "#include \"cnn/gru.h\"\n\n#include <string>\n#include <cassert>\n#include <vector>\n#include <iostream>\n\n#include \"cnn/nodes.h\""
},
{
"path": "cnn/cnn/gru.h",
"chars": 1535,
"preview": "#ifndef CNN_GRU_H_\n#define CNN_GRU_H_\n\n#include \"cnn/cnn.h\"\n#include \"cnn/rnn.h\"\n\nnamespace cnn {\n\nclass Model;\n\nstruct "
},
{
"path": "cnn/cnn/hsm-builder.cc",
"chars": 7091,
"preview": "#include \"cnn/hsm-builder.h\"\n\n#include <fstream>\n#include <iostream>\n#include <cassert>\n#include <sstream>\n\n#undef asser"
},
{
"path": "cnn/cnn/hsm-builder.h",
"chars": 2352,
"preview": "#ifndef CNN_HSMBUILDER_H\n#define CNN_HSMBUILDER_H\n\n#include <vector>\n#include <string>\n#include <unordered_map>\n#include"
},
{
"path": "cnn/cnn/init.cc",
"chars": 2777,
"preview": "#include \"cnn/init.h\"\n#include \"cnn/aligned-mem-pool.h\"\n#include \"cnn/cnn.h\"\n\n#include <iostream>\n#include <random>\n#inc"
},
{
"path": "cnn/cnn/init.h",
"chars": 212,
"preview": "#ifndef CNN_EIGEN_INIT_H\n#define CNN_EIGEN_INIT_H\n\nnamespace cnn {\n\nvoid Initialize(int& argc, char**& argv, unsigned ra"
},
{
"path": "cnn/cnn/lstm.cc",
"chars": 5321,
"preview": "#include \"cnn/lstm.h\"\n\n#include <string>\n#include <cassert>\n#include <vector>\n#include <iostream>\n\n#include \"cnn/nodes.h"
},
{
"path": "cnn/cnn/lstm.h",
"chars": 2101,
"preview": "#ifndef CNN_LSTM_H_\n#define CNN_LSTM_H_\n\n#include \"cnn/cnn.h\"\n#include \"cnn/rnn.h\"\n#include \"cnn/expr.h\"\n\nusing namespac"
},
{
"path": "cnn/cnn/mem.cc",
"chars": 1669,
"preview": "#include \"cnn/mem.h\"\n\n#include <cstdlib>\n#include <cstring>\n#include <iostream>\n#include <sys/shm.h>\n#include <sys/mman."
},
{
"path": "cnn/cnn/mem.h",
"chars": 1427,
"preview": "#ifndef CNN_MEM_H\n#define CNN_MEM_H\n\n#include <vector>\n\nnamespace cnn {\n\n// allocates memory from the device (CPU, GPU)\n"
},
{
"path": "cnn/cnn/model.cc",
"chars": 5818,
"preview": "#include \"cnn/model.h\"\n#include \"cnn/tensor.h\"\n#include \"cnn/aligned-mem-pool.h\"\n#include \"cnn/cnn.h\"\n\n#include <unorder"
},
{
"path": "cnn/cnn/model.h",
"chars": 5102,
"preview": "#ifndef CNN_PARAMS_H_\n#define CNN_PARAMS_H_\n\n#include <vector>\n#include <unordered_set>\n#include <string>\n\n\n#include <bo"
},
{
"path": "cnn/cnn/mp.cc",
"chars": 2170,
"preview": "#include \"mp.h\"\nusing namespace std;\nusing namespace boost::interprocess;\n\nnamespace cnn {\n namespace mp {\n // TODO:"
},
{
"path": "cnn/cnn/mp.h",
"chars": 10875,
"preview": "#pragma once\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/expr.h\"\n#include \"cnn/dict.h\"\n#include \"cnn/ls"
},
{
"path": "cnn/cnn/nodes-common.cc",
"chars": 27159,
"preview": "#include \"cnn/nodes.h\"\n\n#include <limits>\n#include <cmath>\n#include <sstream>\n\nusing namespace std;\n\nnamespace cnn {\n\nin"
},
{
"path": "cnn/cnn/nodes.cc",
"chars": 69896,
"preview": "#include \"cnn/nodes.h\"\n\n#include <limits>\n#include <cmath>\n#include <stdexcept>\n\n#include \"cnn/simd-functors.h\"\n#include"
},
{
"path": "cnn/cnn/nodes.h",
"chars": 46395,
"preview": "#ifndef CNN_NODES_H_\n#define CNN_NODES_H_\n\n#include \"cnn/cnn.h\"\n\nnamespace cnn {\n\n// y = L_sparsemax(x_0; q)\n// where x_"
},
{
"path": "cnn/cnn/param-nodes.cc",
"chars": 5312,
"preview": "#include \"cnn/param-nodes.h\"\n#include \"cnn/tensor.h\"\n\n#include <sstream>\n\nusing namespace std;\n\nnamespace cnn {\n\nstring "
},
{
"path": "cnn/cnn/param-nodes.h",
"chars": 4705,
"preview": "#ifndef CNN_PARAM_NODES_H_\n#define CNN_PARAM_NODES_H_\n\n#include \"cnn/cnn.h\"\n#include \"cnn/model.h\"\n\nnamespace cnn {\n\nstr"
},
{
"path": "cnn/cnn/random.h",
"chars": 148,
"preview": "#ifndef CNN_EIGEN_RANDOM_H\n#define CNN_EIGEN_RANDOM_H\n\n#include <random>\n\nnamespace cnn {\n\nextern std::mt19937* rndeng;\n"
},
{
"path": "cnn/cnn/rnn-state-machine.cc",
"chars": 300,
"preview": "#include \"cnn/rnn-state-machine.h\"\n\n#include <iostream>\n#include \"cnn/cnn.h\"\n\nusing namespace std;\n\nnamespace cnn {\n\nvoi"
},
{
"path": "cnn/cnn/rnn-state-machine.h",
"chars": 1344,
"preview": "#ifndef CNN_RNN_STATE_MACHINE_H_\n#define CNN_RNN_STATE_MACHINE_H_\n\nnamespace cnn {\n\n// CURRENT STATE | ACTION "
},
{
"path": "cnn/cnn/rnn.cc",
"chars": 3482,
"preview": "#include \"cnn/rnn.h\"\n\n#include <string>\n#include <cassert>\n#include <vector>\n#include <iostream>\n\n#include \"cnn/nodes.h\""
},
{
"path": "cnn/cnn/rnn.h",
"chars": 4686,
"preview": "#ifndef CNN_RNN_H_\n#define CNN_RNN_H_\n\n#include \"cnn/cnn.h\"\n#include \"cnn/rnn-state-machine.h\"\n#include \"cnn/expr.h\"\n\nus"
},
{
"path": "cnn/cnn/saxe-init.cc",
"chars": 518,
"preview": "#include \"cnn/saxe-init.h\"\n#include \"cnn/tensor.h\"\n\n#include <random>\n#include <cstring>\n\n#include <Eigen/SVD>\n\nusing na"
},
{
"path": "cnn/cnn/saxe-init.h",
"chars": 153,
"preview": "#ifndef CNN_SAXE_INIT_H_\n#define CNN_SAXE_INIT_H_\n\nnamespace cnn {\n\nstruct Tensor;\n\nvoid OrthonormalRandom(unsigned dim,"
},
{
"path": "cnn/cnn/shadow-params.cc",
"chars": 1051,
"preview": "#include \"cnn/cnn.h\"\n#include \"cnn/shadow-params.h\"\n#include \"cnn/tensor.h\"\n#include \"cnn/aligned-mem-pool.h\"\n#include \""
},
{
"path": "cnn/cnn/shadow-params.h",
"chars": 852,
"preview": "#ifndef CNN_SHADOW_PARAMS_H\n#define CNN_SHADOW_PARAMS_H\n\n#include <vector>\n#include \"cnn/tensor.h\"\n\n// if your learner n"
},
{
"path": "cnn/cnn/simd-functors.h",
"chars": 7356,
"preview": "#ifndef CNN_XFUNCTORS_H\n#define CNN_XFUNCTORS_H\n\n#include <Eigen/Eigen>\n\n#include \"cnn/functors.h\"\n\n// these functors ar"
},
{
"path": "cnn/cnn/tensor.cc",
"chars": 3972,
"preview": "#include \"cnn/tensor.h\"\n\n#include <random>\n#include <vector>\n#include <cstring>\n\n#if HAVE_CUDA\n#include \"cnn/cuda.h\"\n#en"
},
{
"path": "cnn/cnn/tensor.h",
"chars": 8606,
"preview": "#ifndef CNN_EIGEN_TENSOR_H\n#define CNN_EIGEN_TENSOR_H\n\n#include <initializer_list>\n#include <vector>\n\n#include \"cnn/dim."
},
{
"path": "cnn/cnn/tests/test_edges.cc",
"chars": 11189,
"preview": "#define BOOST_TEST_DYN_LINK\n#define BOOST_TEST_MODULE \"CNNEdges\"\n#include <boost/test/unit_test.hpp>\n\n#include <vector>\n"
},
{
"path": "cnn/cnn/tests/test_init.cc",
"chars": 1477,
"preview": "#define BOOST_TEST_DYN_LINK\n#define BOOST_TEST_MODULE \"CNNInit\"\n#include <boost/test/unit_test.hpp>\n\n#include <vector>\n\n"
},
{
"path": "cnn/cnn/tests/test_utils.h",
"chars": 1451,
"preview": "#ifndef CNN_TEST_UTILS_H_\n#define CNN_TEST_UTILS_H_\n\n#include \"cnn/tensor.h\"\n\nnamespace cnn {\n\n#if WITH_MINERVA_BACKEND\n"
},
{
"path": "cnn/cnn/timing.h",
"chars": 519,
"preview": "#ifndef _TIMING_H_\n#define _TIMING_H_\n\n#include <iostream>\n#include <string>\n#include <chrono>\n\nnamespace cnn {\n\nstruct "
},
{
"path": "cnn/cnn/training.cc",
"chars": 8858,
"preview": "#include \"cnn/training.h\"\n\n#include \"cnn/gpu-ops.h\"\n\nnamespace cnn {\n\nusing namespace std;\n\ntemplate <class Derived>\nboo"
},
{
"path": "cnn/cnn/training.h",
"chars": 3874,
"preview": "#ifndef CNN_TRAINING_H_\n#define CNN_TRAINING_H_\n\n#include <vector>\n#include \"cnn/model.h\"\n#include \"cnn/shadow-params.h\""
},
{
"path": "cnn/config.h.cmake",
"chars": 214,
"preview": "#ifndef CNN_CONFIG_H_\n#define CNN_CONFIG_H_\n\n#cmakedefine WITH_MINERVA_BACKEND @WITH_MINERVA_BACKEND@\n#cmakedefine WITH_"
},
{
"path": "cnn/examples/CMakeLists.txt",
"chars": 607,
"preview": "CMAKE_MINIMUM_REQUIRED(VERSION 2.8)\n\nforeach(TARGET mlc tok-embed segrnn-sup poisson-regression tag-bilstm embed-cl encd"
},
{
"path": "cnn/examples/embed-cl.cc",
"chars": 5954,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/dict.h\"\n#inc"
},
{
"path": "cnn/examples/encdec.cc",
"chars": 7946,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/example-data/clusters-hsm.txt",
"chars": 52278,
"preview": "SPECIAL\t</s>\t1\n00000\tstewardess\t5\n00000\tcombination\t5\n00000\tsudden\t5\n00000\tpoisonous\t5\n00000\tcalendar\t5\n00000\tlag\t5\n0000"
},
{
"path": "cnn/examples/example-data/dev-hsm.txt",
"chars": 479,
"preview": "<s> i think this is wrong . can you check it ? </s>\n<s> fine . </s>\n<s> i went back to my hotel room and found a <unk> h"
},
{
"path": "cnn/examples/example-data/dev-poi.txt",
"chars": 161,
"preview": "<s> i 'll take it . </s> ||| 5\n<s> we want to have a table near the window . </s> ||| 10\n<s> how can i help you ? </s> |"
},
{
"path": "cnn/examples/example-data/fin-dev.txt",
"chars": 145491,
"preview": "<s> l i b e r a a l i <spc> s a u d i b l o g g a r i <spc> s a i <spc> 1 0 <spc> v u o t t a <spc> v a n k e u t t a <s"
},
{
"path": "cnn/examples/example-data/fin-toy.txt",
"chars": 2047,
"preview": "<s> 21 11 7 6 7 22 22 1 24 1 21 22 11 </s>\n<s> 21 3 13 21 3 14 3 11 21 14 7 10 22 11 7 16 </s>\n<s> 24 3 14 11 22 22 3 24"
},
{
"path": "cnn/examples/example-data/fin-words-dev.txt",
"chars": 201314,
"preview": "<s> h y ö t y a n a l y y s i </s>\n<s> k e h i t y s h a a s t e i d e n </s>\n<s> e l i n p o i s t o j a </s>\n<s> h y v"
},
{
"path": "cnn/examples/example-data/fin-words.txt",
"chars": 9420051,
"preview": "<s> a a k k o s j ä r j e s t y k s e s s ä </s>\n<s> a a k k o s j ä r j e s t y s </s>\n<s> a a k k o s l a j i </s>\n<s>"
},
{
"path": "cnn/examples/example-data/fin.txt",
"chars": 779574,
"preview": "<s> o l e m m e <spc> s i t ä <spc> v a s t o i n <spc> h u o l i s s a m m e <spc> m e n e t t e l y t a v o i s t a <s"
},
{
"path": "cnn/examples/example-data/seg-sup.dev.txt",
"chars": 172,
"preview": "a a 0 1 a ||| O:1 O:1 N:2 O:1\nx y 1 a ||| O:1 O:1 N:1 O:1\na a a ||| O:1 O:1 O:1\n1 0 1 a ||| N:3 O:1\na 1 a 1 ||| O:1 N:1 "
},
{
"path": "cnn/examples/example-data/textcat.txt",
"chars": 159,
"preview": "what is your name ? ||| Q\nwhere do you live ? ||| Q\ni live in pittsburgh . ||| D\nmy name is bond . ||| D\nwtf ? she asked"
},
{
"path": "cnn/examples/example-data/train-hsm.txt",
"chars": 45599,
"preview": "<s> can i catch a bus that goes to the hilton hotel ? </s>\n<s> is there a train that goes to the city ? </s>\n<s> it 's j"
},
{
"path": "cnn/examples/example-data/train-poi.txt",
"chars": 10623,
"preview": "<s> a bag of cookies and a lemon bar . </s> ||| 9\n<s> sorry , i 'll take care of the dog . </s> ||| 10\n<s> i would like "
},
{
"path": "cnn/examples/mlc.cc",
"chars": 5419,
"preview": "#include \"cnn/timing.h\"\n#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/expr.h\"\n#inc"
},
{
"path": "cnn/examples/nlm.cc",
"chars": 2198,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/expr.h\"\n\n#in"
},
{
"path": "cnn/examples/poisson-regression.cc",
"chars": 6023,
"preview": "#include \"cnn/grad-check.h\"\n#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h"
},
{
"path": "cnn/examples/read-write.cc",
"chars": 4620,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/gpu-ops.h\"\n#include \"cnn/expr.h\"\n#in"
},
{
"path": "cnn/examples/rnnlm-aevb.cc",
"chars": 7774,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/rnnlm-batch.cc",
"chars": 7808,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/rnnlm-cfsm.cc",
"chars": 6295,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/rnnlm-givenbag.cc",
"chars": 7369,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/rnnlm-mp.cc",
"chars": 2190,
"preview": "#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/expr.h\"\n#include \"cnn/lstm.h\"\n#include \"cnn/mp.h\"\n#include "
},
{
"path": "cnn/examples/rnnlm.cc",
"chars": 7008,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/rnnlm.h",
"chars": 2727,
"preview": "#include \"cnn/cnn.h\"\n#include \"cnn/expr.h\"\n#include \"cnn/dict.h\"\n#include \"cnn/lstm.h\"\n\n#include <vector>\n\nusing namespa"
},
{
"path": "cnn/examples/rnnlm2.cc",
"chars": 7254,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/segrnn-sup.cc",
"chars": 39878,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/skiprnnlm.cc",
"chars": 8464,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/tag-bilstm.cc",
"chars": 7801,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/textcat.cc",
"chars": 9841,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/tok-embed.cc",
"chars": 8640,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/examples/xor-batch-lookup.cc",
"chars": 2098,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/gpu-ops.h\"\n#include \"cnn/expr.h\"\n#in"
},
{
"path": "cnn/examples/xor-batch.cc",
"chars": 1874,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/gpu-ops.h\"\n#include \"cnn/expr.h\"\n#in"
},
{
"path": "cnn/examples/xor-xent.cc",
"chars": 1764,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/expr.h\"\n\n#include <boost/archive/tex"
},
{
"path": "cnn/examples/xor.cc",
"chars": 1908,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/gpu-ops.h\"\n#include \"cnn/expr.h\"\n#in"
},
{
"path": "cnn/pycnn/INSTALL",
"chars": 46,
"preview": "See installation instruction in ../INSTALL.md\n"
},
{
"path": "cnn/pycnn/makefile",
"chars": 697,
"preview": "\nPYTHON := python\nUNAME := $(shell uname)\nifeq ($(UNAME),Darwin)\n\tDYSUF := dylib\nelse\n\tDYSUF := so\nendif\n\npycnn.${DYSUF}"
},
{
"path": "cnn/pycnn/pycnn.pxd",
"chars": 13829,
"preview": "from libcpp.vector cimport vector\nfrom libcpp.string cimport string\n\nctypedef float real\n\ncdef extern from \"cnn/init.h\" "
},
{
"path": "cnn/pycnn/pycnn.pyx",
"chars": 44824,
"preview": "# on numpy arrays, see: https://github.com/cython/cython/wiki/tutorials-NumpyPointerToC\n\nimport sys\nfrom cython.operator"
},
{
"path": "cnn/pycnn/pycnn_viz.py",
"chars": 36368,
"preview": "import re\nfrom collections import defaultdict\n\ngraphviz_items = []\n\nvindex_count = -1\ndef new_index():\n global vindex_c"
},
{
"path": "cnn/pycnn/setup.py",
"chars": 1145,
"preview": "from setuptools import setup\nfrom setuptools.extension import Extension\nfrom Cython.Distutils import build_ext\n\n\n# Remov"
},
{
"path": "cnn/pycnn/setup_gpu.py",
"chars": 1347,
"preview": "from setuptools import setup\nfrom setuptools.extension import Extension\nfrom Cython.Distutils import build_ext\n\n\n# Remov"
},
{
"path": "cnn/pyexamples/attention.py",
"chars": 4801,
"preview": "import pycnn as pc\nimport random\n\nEOS = \"<EOS>\"\ncharacters = list(\"abcdefghijklmnopqrstuvwxyz \")\ncharacters.append(EOS)\n"
},
{
"path": "cnn/pyexamples/bilstmtagger.py",
"chars": 3738,
"preview": "from pycnn import *\nfrom collections import Counter\nimport random\n\nimport util\n\n# format of files: each line is \"word<TA"
},
{
"path": "cnn/pyexamples/cpu_vs_gpu.py",
"chars": 542,
"preview": "import gpycnn as G\nprint \nimport pycnn as C\n\ncm = C.Model()\ngm = G.Model()\n\ncpW = cm.add_parameters(\"W\",(1000,1000))\ngpW"
},
{
"path": "cnn/pyexamples/minibatch.py",
"chars": 845,
"preview": "from pycnn import *\nimport numpy as np\n\nm = Model()\nlp = m.add_lookup_parameters(\"a\",(100,10))\n\n# regular lookup\na = lp["
},
{
"path": "cnn/pyexamples/rnnlm.py",
"chars": 3294,
"preview": "from pycnn import *\nimport time\nimport random\n\nLAYERS = 2\nINPUT_DIM = 50 #256\nHIDDEN_DIM = 50 #1024\nVOCAB_SIZE = 0\n\nfr"
},
{
"path": "cnn/pyexamples/rnnlm_transduce.py",
"chars": 3417,
"preview": "# a version rnnlm.py using the transduce() interface.\nfrom pycnn import *\nimport time\nimport random\n\nLAYERS = 2\nINPUT_DI"
},
{
"path": "cnn/pyexamples/tutorials/API.ipynb",
"chars": 9080,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Expression building\\n\",\n \"\\n\""
},
{
"path": "cnn/pyexamples/tutorials/RNNs.ipynb",
"chars": 25828,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outp"
},
{
"path": "cnn/pyexamples/tutorials/tutorial-1-xor.ipynb",
"chars": 29530,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"out"
},
{
"path": "cnn/pyexamples/util.py",
"chars": 1072,
"preview": "from collections import defaultdict\nfrom itertools import count\nclass Vocab:\n def __init__(self, w2i=None):\n i"
},
{
"path": "cnn/pyexamples/xor.py",
"chars": 1510,
"preview": "from pycnn import *\n\n#xsent = True\nxsent = False\n\nHIDDEN_SIZE = 8\nITERATIONS = 2000\n\nm = Model()\nsgd = SimpleSGDTrainer("
},
{
"path": "cnn/rnnlm/CMakeLists.txt",
"chars": 420,
"preview": "CMAKE_MINIMUM_REQUIRED(VERSION 2.8)\n\nforeach(TARGET lm)\n ADD_EXECUTABLE(${TARGET} ${TARGET}.cc)\n target_link_libraries"
},
{
"path": "cnn/rnnlm/README.md",
"chars": 1028,
"preview": "### Obtaining LM data\n\n ./install-examples.sh\n\nThis downloads the data used in this tutorial.\n\n### Training example\n\n"
},
{
"path": "cnn/rnnlm/install-examples.sh",
"chars": 265,
"preview": "#!/bin/bash\nset -e\n\ndata_version=ptb-mikolov-01.tar.gz\n\nROOTDIR=`dirname $0`\ncd $ROOTDIR\n\nrm -f $data_version\nrm -rf ptb"
},
{
"path": "cnn/rnnlm/lm.cc",
"chars": 13889,
"preview": "#include \"cnn/nodes.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/training.h\"\n#include \"cnn/timing.h\"\n#include \"cnn/rnn.h\"\n#incl"
},
{
"path": "cnn/tests/CMakeLists.txt",
"chars": 661,
"preview": "find_package (Boost COMPONENTS system filesystem unit_test_framework REQUIRED)\ninclude_directories (${TEST_SOURCE_DIR}/s"
},
{
"path": "cnn/tests/README.md",
"chars": 69,
"preview": "Use `test-cnn.cc` as a reference for how to set up subsequent tests.\n"
},
{
"path": "cnn/tests/test-cnn.cc",
"chars": 777,
"preview": "#include <cnn/cnn.h>\n#define BOOST_TEST_MODULE CNNBasicTest\n#include <boost/test/unit_test.hpp>\n\nstruct ConfigureCNNTest"
},
{
"path": "cnn/tests/test-nodes.cc",
"chars": 19478,
"preview": "#include <cnn/cnn.h>\n#include <cnn/expr.h>\n#include <cnn/grad-check.h>\n#include <boost/test/unit_test.hpp>\n#include <std"
},
{
"path": "get_dictionary.py",
"chars": 1819,
"preview": "def is_next_open_bracket(line, start_idx):\n for char in line[(start_idx + 1):]:\n if char == '(':\n r"
},
{
"path": "get_oracle.py",
"chars": 7234,
"preview": "import sys\nimport get_dictionary\n\n# tokens is a list of tokens, so no need to split it again\ndef unkify(tokens, words_di"
},
{
"path": "get_oracle_gen.py",
"chars": 7236,
"preview": "import sys\nimport get_dictionary\n\n# tokens is a list of tokens, so no need to split it again\ndef unkify(tokens, words_di"
},
{
"path": "get_unkified_terminals.py",
"chars": 390,
"preview": "import sys\nimport argparse\n\n\nparser = argparse.ArgumentParser(description='Give me oracle file.')\nparser.add_argument('-"
},
{
"path": "interpreting-rnng/README.md",
"chars": 882,
"preview": "# Recurrent Neural Network Grammars\nCode for the [What Do Recurrent Neural Network Grammars Learn About Syntax?](https:/"
},
{
"path": "interpreting-rnng/nt-parser-gen-attention-gated-stack-only.cc",
"chars": 32742,
"preview": "#include <cstdlib>\n#include <iostream>\n#include <vector>\n#include <fstream>\n#include <cmath>\n#include <chrono>\n#include "
},
{
"path": "nt-parser/CMakeLists.txt",
"chars": 324,
"preview": "PROJECT(cnn:nt-parser)\nCMAKE_MINIMUM_REQUIRED(VERSION 2.8)\n\nADD_EXECUTABLE(nt-parser nt-parser.cc eval.cc oracle.cc pret"
},
{
"path": "nt-parser/compressed-fstream.h",
"chars": 1052,
"preview": "#ifndef PARSER_COMPRESSED_FSTREAM_H_\n#define PARSER_COMPRESSED_FSTREAM_H_\n\n#include <iostream>\n#include <string>\n#includ"
},
{
"path": "nt-parser/eval.cc",
"chars": 707,
"preview": "#include \"nt-parser/eval.h\"\n\n#include <iostream>\n#include <cstdio>\n\n#include <boost/iostreams/stream.hpp>\n#include <boos"
},
{
"path": "nt-parser/eval.h",
"chars": 264,
"preview": "#ifndef NTPARSER_EVAL_H_\n#define NTPARSER_EVAL_H_\n\n#include <string>\n#include <vector>\n\nnamespace parser {\n\nstruct EvalB"
},
{
"path": "nt-parser/nt-parser-gen.cc",
"chars": 27559,
"preview": "#include <cstdlib>\n#include <iostream>\n#include <vector>\n#include <fstream>\n#include <cmath>\n#include <chrono>\n#include "
},
{
"path": "nt-parser/nt-parser.cc",
"chars": 50059,
"preview": "#include <cstdlib>\n#include <iostream>\n#include <vector>\n#include <fstream>\n#include <cmath>\n#include <chrono>\n#include "
},
{
"path": "nt-parser/oracle.cc",
"chars": 8428,
"preview": "#include \"nt-parser/oracle.h\"\n\n#include <cassert>\n#include <fstream>\n\n#include \"cnn/dict.h\"\n#include \"nt-parser/compress"
},
{
"path": "nt-parser/oracle.h",
"chars": 2671,
"preview": "#ifndef PARSER_ORACLE_H_\n#define PARSER_ORACLE_H_\n\n#include <iostream>\n#include <vector>\n#include <string>\n\nnamespace cn"
},
{
"path": "nt-parser/pretrained.cc",
"chars": 1313,
"preview": "#include \"nt-parser/pretrained.h\"\n\n#include <sstream>\n#include \"cnn/dict.h\"\n#include \"nt-parser/compressed-fstream.h\"\n\nu"
},
{
"path": "nt-parser/pretrained.h",
"chars": 353,
"preview": "#ifndef PARSER_PRETRAINED_H\n#define PARSER_PRETRAINED_H\n\n#include <unordered_map>\n#include <vector>\n#include <string>\n\nn"
},
{
"path": "remove_dev_unk.py",
"chars": 2660,
"preview": "import sys\n\ndef is_next_open_bracket(line, start_idx):\n for char in line[(start_idx + 1):]:\n if char == '(':\n "
},
{
"path": "sample_input_chinese.txt",
"chars": 580,
"preview": "(IP (NP (NP (NR 上海) (NR 浦东)) (NP (NN 开发) (CC 与) (NN 法制) (NN 建设))) (VP (VV 同步)))\n(FRAG (NN 新华社) (NR 上海) (NT 二月) (NT 十日) ("
},
{
"path": "sample_input_english.txt",
"chars": 1054,
"preview": "(S (PP (IN In) (NP (NP (DT an) (NNP Oct.) (CD 19) (NN review)) (PP (IN of) (NP (`` ``) (NP (DT The) (NN Misanthrope)) ('"
},
{
"path": "utils/add-fake-preterms-for-eval.pl",
"chars": 325,
"preview": "#!/usr/bin/perl -w\nuse strict;\nwhile(<>) {\n chomp;\n my @toks = split /\\s+/;\n my $sentid = shift @toks;\n shift @toks;"
},
{
"path": "utils/cut-corpus.pl",
"chars": 717,
"preview": "#!/usr/bin/perl -w\nuse strict;\ndie \"Usage: $0 N\\nSplits a corpus separated by ||| symbols and returns the Nth field\\n\" u"
},
{
"path": "utils/is-estimate-marginal-llh.pl",
"chars": 1513,
"preview": "#!/usr/bin/perl -w\n\nmy $num_sents = shift @ARGV;\nmy $num_samples_per_sent = shift @ARGV;\n\nopen Q, \"<$ARGV[0]\" or die;\nop"
},
{
"path": "utils/remove_dev_unk.py",
"chars": 2660,
"preview": "import sys\n\ndef is_next_open_bracket(line, start_idx):\n for char in line[(start_idx + 1):]:\n if char == '(':\n "
},
{
"path": "utils/replace-unks-in-trees.pl",
"chars": 857,
"preview": "#!/usr/bin/perl -w\nuse strict;\n\ndie \"Usage: $0 oracle.txt hyp-trees.txt\\n\" unless scalar @ARGV == 2;\n\nopen O, \"<$ARGV[0]"
}
]
About this extraction
This page contains the full source code of the clab/rnng GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 176 files (11.5 MB), approximately 3.0M tokens, and a symbol index with 1036 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.