tensorflow-predictor-cpp

TensorFlow prediction using its C++ API. Having this repo, you will not need TensorFlow-Serving. This project has been tested on OSX and Linux.

Contains two examples:

simple model c = a * b
an industrial deep model for large scale click through rate prediction

Covered knowledge points:

save model and checkpoint
freeze model with checkpoint
replace part of nodes in the model for prediction
transform libfm data into tfrecord
use lookup table for sparse embedding
load model in C++
construct SparseTensor in C++
prediction in C++

Build on OSX

1) Build TensorFlow

Follow the instruction build tensorflow from source

git clone --recursive https://github.com/tensorflow/tensorflow.git
cd tensorflow
sh tensorflow/contrib/makefile/build_all_linux.sh (works for linux and osx)
cd ..

2) Build this repo

Keep this repo in the same directory with tensorflow.

git clone https://github.com/formath/tensorflow-predictor-cpp.git
cd tensorflow-predictor-cpp
mkdir build && cd build
cmake ..
make

Simple Model

This demo used c = a * b to show how to save the model and load it using C++ for prediction.

Save model
Load model
Prediction

More detail in Chinese: tensorflow_c++_api_prediction

cd demo/simple_model
# train
sh train.sh
# predict
sh predict.sh

If works right, you will see this

Session created successfully
Load graph protobuf successfully
Add graph to session successfully
Run session successfully
Tensor<type: float shape: [] values: 6>
output value: 6

Deep CTR Model

This demo show a real-world deep model usage in click through rate prediction.

Transform LibFM data into TFRecord
Usage of lookup table
Save model and checkpoint
Replace parts of model and freeze graph with checkpoint
Load model and checkpoint
Prediction

More detail in Chinese: tensorflow_c++_api_prediction

1) Transform LibFM data into TFRecord

LibFM format: label fieldId:featureId:value ...

cd demo/deep_model
sh trans_data_to_tfrecord.sh

2) Train model

sh train.sh

3) Freeze model

sh freeze_graph.sh

4) Predict using C++

sh predict.sh