clstm
CLSTM is an implementation of the LSTM recurrent neural network model in C++, using the Eigen library for numerical computations.
Status and scope
CLSTM is mainly in maintenance mode now. It was created at a time when there weren't a lot of good LSTM implementations around, but several good options have become available over the last year. Nevertheless, if you need a small library for text line recognition with few dependencies, CLSTM is still a good option.
Installation using Docker
You can train and run clstm without installation to the local machine using the docker image, which is based on Ubuntu 16.04. This is the best option for running clstm on a Windows host.
You can either run the last version of the clstm image from Docker Hub or build the Docker image from the repo (see ./docker/Dockerfile).
The command line syntax differs from a native installation:
docker run --rm -it -e [VARIABLES...] kbai/clstm BINARY [ARGS...]
is equivalent to
[VARIABLES...] BINARY [ARGS...]
For example:
docker run --rm -it -e ntrain=1000 kbai/clstm clstmocrtrain traininglist.txt
is equivalent to
ntrain=1000 clstmocrtrain traininglist.txt
Installation from source
Prerequisites
- scons, swig, Eigen
- protocol buffer library and compiler
- libpng
- Optional: HDF5, ZMQ, Python
# Ubuntu 15.04, 16.04 / Debian 8, 9
sudo apt-get install scons libprotobuf-dev protobuf-compiler libpng-dev libeigen3-dev swig
# Ubuntu 14.04:
sudo apt-get install scons libprotobuf-dev protobuf-compiler libpng-dev swig
The Debian repositories jessie-backports and stretch include sufficiently new libeigen3-dev packages.
It is also possible to download Eigen with Tensor support (> v3.3-beta1) and copy the header files to an include path:
# with wget
wget 'https://github.com/RLovelett/eigen/archive/3.3-rc1.tar.gz'
tar xf 3.3-rc1.tar.gz
rm -f /usr/local/include/eigen3
mv eigen-3.3-rc1 /usr/local/include/eigen3
# or with git:
sudo git clone --depth 1 --single-branch --branch 3.3-rc1 \
"https://github.com/RLovelett/eigen" /usr/local/include/eigen3
To use the visual debugging methods, additionally:
# Ubuntu 15.04:
sudo apt-get install libzmq3-dev libzmq3 libzmqpp-dev libzmqpp3 libpng12-dev
For HDF5, additionally:
# Ubuntu 15.04:
sudo apt-get install hdf5-helpers libhdf5-8 libhdf5-cpp-8 libhdf5-dev python-h5py
# Ubuntu 14.04:
sudo apt-get install hdf5-helpers libhdf5-7 libhdf5-dev python-h5py
Building
To build a standalone C library, run
scons
sudo scons install
There are a bunch of options:
debug=1build with debugging options, no optimizationdisplay=1build with display support for debugging (requires ZMQ, Python)prefix=...install under a different prefix (untested)eigen=...where to look for Eigen include files (should containEigen/Eigen)openmp=...build with multi-processing support. Set theOMP_NUM_THREADSenvironment variable to the number of threads for Eigen to use.hdf5lib=hdf5what HDF5 library to use; enables HDF5 command line programs (may needhdf5_serialin some environments)
Running the tests
After building the executables, you can run two simple test runs as follows:
run-cmuwill train an English-to-IPA LSTMrun-uw3-500will download a small OCR training/test set and train an OCR LSTM
There is a full set of tests in the current version of clstm; just run them with:
./run-tests
This will check:
- gradient checkers for layers and compute steps
- training a simple model through the C++ API
- training a simple model through the Python API
- checking the command line training tools, including loading and saving
Python bindings
To build the Python extension, run
python setup.py build
sudo python setup.py install
(this is currently broken)
Documentation / Examples
You can find some documentation and examples in the form of iPython notebooks in the misc directory (these are version 3 notebooks and won't open in older versions).
You can view these notebooks online here: http://nbviewer.ipython.org/github/tmbdev/clstm/tree/master/misc/
C++ API
The clstm library operates on the Sequence type as its fundamental data type, representing variable length sequences of fixed length vectors. The underlying Sequence type is a rank 4 tensor with accessors for individual rank-2 tensors at different time steps.
Networks are built from objects implementing the INetwork interface. The INetwork interface contains:
struct INetwork {
Sequence inputs, d_inputs; // input sequence, input deltas
Sequence outputs, d_outputs; // output sequence, output deltas
void forward(); // propagate inputs to outputs
void backward(); // propagate d_outputs to d_inputs
void update(); // update weights from the last backward() step
void setLearningRate(Float,Float); // set learning rates
...
};
Network structures can be hierarchical and there are some network implementations whose purpose it is to combine other networks into more complex structures.
struct INetwork {
...
vector<shared_ptr<INetwork>> sub;
void add(shared_ptr<INetwork> net);
...
};
At its lowest level, layers are created by:
- create an instance of the layer with
make_layer - set any parameters (including
ninputandnoutput) as attributes - add any sublayers to the
subvector - call
initialize()
There are three different functions for constructing layers and networks:
make_layer(kind)looks up the constructor and gives you an uninitialized layerlayer(kind,ninput,noutput,args,sub)performs all initialization steps in sequencemake_net(kind,args)initializes a whole collection of layers at oncemake_net_init(kind,params)is likemake_net, but parameters are given in string form
The layer(kind,ninput,noutput,args,sub) function will perform these steps in sequence.
Layers and networks are usually passed around as shared_ptr<INetwork>; there is a typedef of this calling it Network.
This can be used to construct network architectures in C++ pretty easily. For example, the following creates a network that stacks a softmax output layer on top of a standard LSTM layer:
Network net = layer("Stacked", ninput, noutput, {}, {
layer("LSTM", ninput, nhidden,{},{}),
layer("SoftmaxLayer", nhidden, noutput,{},{})
});
Note that you need to make sure that the number of input and output units are consistent between layers.
In addition to these basic functions, there is also a small implementation of CTC alignment.
The C++ code roughly follows the lstm.py implementation from the Python version of OCRopus. Gradients have been verified for the core LSTM implementation, although there may be still be bugs in other parts of the code.
There is also a small multidimensional array class in multidim.h; that isn't used in the core LSTM implementation, but it is used in debugging and testing code, for plotting, and for HDF5 input/output. Unlike Eigen, it uses standard C/C++ row major element order, as libraries like HDF5 expect. (NB: This will be replaced with Eigen::Tensor.)
LSTM models are stored in protocol buffer format (clstm.proto), although adding new formats is easy. There is an older HDF5-based storage format.
Python API
The clstm.i file implements a simple Python interface to clstm, plus a wrapper that makes an INetwork mostly a replacement for the lstm.py implementation from ocropy.
Command Line Drivers
There are several command line drivers:
clstmfiltertrain training-data test-datalearns text filters;- input files consiste of lines of the form "inputoutput"
clstmfilterapplies learned text filtersclstmocrtrain training-images test-imageslearns OCR (or image-to-text) transformations;- input files are lists of text line images; the corresponding UTF-8 ground truth is expected in the corresponding
.gt.txtfile
- input files are lists of text line images; the corresponding UTF-8 ground truth is expected in the corresponding
clstmocrapplies learned OCR models
In addition, you get the following HDF5-based commands:
- clstmseq learns sequence-to-sequence mappings
- clstmctc learns sequence-to-string mappings using CTC alignment
- clstmtext learns string-to-string transformations
Note that most parameters are passed through the environment:
lrate=3e-5 clstmctc uw3-dew.h5
See the notebooks in the misc/ subdirectory for documentation on the parameters and examples of usage.
(You can find all parameters via grep 'get.env' *.cc.)
2 Jan 16, 2022
20 May 31, 2019
1 Nov 12, 2021
346 Jan 07, 2023
62 Oct 10, 2022
1.8k Dec 28, 2022
4 Oct 18, 2022
4 Nov 02, 2022
3.2k Dec 31, 2022
147 Dec 04, 2022
2 Oct 28, 2021
5 Sep 19, 2022
2.6k Dec 29, 2022
89 Nov 18, 2022
13 Mar 31, 2022
4 Oct 26, 2022
49 Jan 03, 2023
1 Jan 10, 2022
26 Jan 07, 2023