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nlp模型转tensorflow lite，并使用c++调用

Python 0.39% CMake 0.71% C 6.74% C++ 90.49% Makefile 0.80% Objective-C 0.87%

nlp_tflite's Introduction

c++调用tflite

一，进入到char-rnn下面训练模型，并将模型转换成tflite。详见char-rnn/README.md

二，编译libtensorflowlite.so文件（mac下测试，linux类似）

1，安装bazel。见官方教程 https://docs.bazel.build/versions/master/install.html
2，git clone https://github.com/tensorflow/tensorflow.git
3，进入到tensorflow文件夹下，也就是位于WORKSPACE下面，bazel需要在这个文件的目录下执行编译
4，./configure
5，bazel build --cxxopt='--std=c++11' //tensorflow/lite:libtensorflowlite.so 如果要在安卓上使用，需要指定ndk

三，进入到cpp_tflite中，引入头文件和so文件

1，将tflite的头文件放置到include下(cd tensorflow/tensorflow) (find ./lite -name "*.h" | tar -cf headers.tar -T -)
2，将上面的headders.tar压缩包移动到自己项目下的cpp_tflite/include中并解压，解压后可以直接去除
3，将tensorflow/bazel-bin/tensorflow/lite/libtensorflowlite.so和tensorflow/bazel-bin/external/flatbuffers/libflatbuffers.a 放到cpp_tflite/lib文件夹中
4，进入到tensorflow/tensorflow/lite/tools/make下执行download_dependencies.sh，或者直接下载 flatbuffers压缩包，然后取出include的内容放置到cpp_tflite/include中即可。
5，编写CMakeLists.txt，链接到so文件
6，mkdir build
7，cd build
8，cmake ..
9，make
10，./nlp_tflite

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