-Tensor is responsible for the calculation of Layer. It executes the addition, division, multiplication, dot production, averaging of Data and so on. In order to accelerate the calculation speed, CBLAS (C-Basic Linear Algebra: CPU) and CUBLAS (CUDA: Basic Linear Algebra) for PC (Especially NVIDIA GPU) for some of the operation. Later, these calculations will be optimized.
-
-## Getting Started
-
-### Prerequisites
-
-The following dependencies are needed to compile / build / run.
-
-* gcc/g++ (>= 4.9, std=c++14 is used)
-* meson (>= 0.50.0)
-* blas library (CBLAS) (for CPU Acceleration, libopenblas is used for now)
-* cuda, cudart, cublas (should match the version) (GPU Acceleration on PC)
-* tensorflow-lite (>= 1.4.0)
-* libjsoncpp ( >= 0.6.0) (openAI Environment on PC)
-* libcurl3 (>= 7.47) (openAI Environment on PC)
-* libiniparser
-* libgtest (for testing)
-
-
-### Give It a Go Build with Docker
-
-You can use [docker image](https://hub.docker.com/r/lunapocket/nntrainer-build-env) to easily set up and try building.
-
-To run the docker
-
-```bash
-$ docker pull lunapocket/nntrainer-build-env:ubuntu-18.04
-$ docker run --rm -it lunapocket/nntrainer-build-env:ubuntu-18.04
-```
-
-Inside docker...
-
-```bash
-$ cd /root/nntrainer
-$ git pull # If you want to build with latest sources.
-```
-
-You can try build from now on without concerning about Prerequisites.
-
-### How to Build
-
-Download the source file by cloning the github repository.
-
-```bash
-$ git clone https://github.com/nnstreamer/nntrainer
-```
-
-After completing download the sources, you can find the several directories and files as below.
-
-``` bash
-$ cd nntrainer
-
-$ ls -1
-api
-Applications
-debian
-doc
-jni
-LICENSE
-meson.build
-meson_options.txt
-nntrainer
-nntrainer.pc.in
-packaging
-README.md
-test
-
-$ git log --oneline
-f1a3a05 (HEAD -> master, origin/master, origin/HEAD) Add more badges
-37032a1 Add Unit Test Cases for Neural Network Initialization
-181a003 lower case for layer type.
-1eb399b Update clang-format
-87f1de7 Add Unit Test for Neural Network
-cd5c36e Add Coverage Test badge for nntrainer
-...
-```
-
-You can find the source code of the core library in nntrainer/src. In order to build them, use [meson](https://mesonbuild.com/)
-```bash
-$ meson build
-The Meson build system
-Version: 0.50.1
-Source dir: /home/wook/Work/NNS/nntrainer
-Build dir: /home/wook/Work/NNS/nntrainer/build
-Build type: native build
-Project name: nntrainer
-Project version: 0.0.1
-Native C compiler: cc (gcc 7.5.0 "cc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0")
-Native C++ compiler: c++ (gcc 7.5.0 "c++ (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0")
-Build machine cpu family: x86_64
-Build machine cpu: x86_64
-...
-Build targets in project: 11
-Found ninja-1.8.2 at /usr/bin/ninja
-
-$ ninja -C build
-ninja: Entering directory `build'
-[41/41] Linking target test/unittest/unittest_nntrainer_internal.
-```
-
-After completion of the build, the shared library, 'libnntrainer.so' and the static library, 'libnntrainer.a' will be placed in build/nntrainer.
-```bash
-$ ls build/nntrainer -1
-d48ed23@@nntrainer@sha
-d48ed23@@nntrainer@sta
-libnntrainer.a
-libnntrainer.so
-```
-
-In order to install them with related header files to your system, use the 'install' sub-command.
-```bash
-$ ninja -C build install
-```
-Then, you will find the libnntrainer.so and related .h files in /usr/local/lib and /usr/local/include directories.
-
-By default, the command ```ninja -C build`` generates the five example application binaries (Classification, k-NN, LogisticRegression, ReinforcementLearning, and Training) you could try in build/Applications. For 'Training' as an example case,
-```bash
-$ ls build/Applications/Training/jni/ -1
-e189c96@@nntrainer_training@exe
-nntrainer_training
-```
-
-In order to run such example binaries, Tensorflow-lite is a prerequisite. If you are trying to run on the Android, it will automatically download tensorflow (1.9.0) and compile as static library. Otherwise, you need to install it by yourself.
-
-### Running Examples
-
-
-1. [Training](https://github.com/nnstreamer/nntrainer/blob/master/Applications/Training/README.md)
-
-After build, run with following arguments
-Make sure to put last '/' for the resources directory.
-```bash
-$./path/to/example ./path/to/settings.ini ./path/to/resource/directory/
-```
-
-To run the 'Training', for example, do as follows.
-
-```bash
-$ pwd
-./nntrainer
-$ LD_LIBRARY_PATH=./build/nntrainer ./build/Applications/Training/jni/nntrainer_training ./Applications/Training/res/Training.ini ./Applications/Training/res/
-../../res/happy/happy1.bmp
-../../res/happy/happy2.bmp
-../../res/happy/happy3.bmp
-../../res/happy/happy4.bmp
-../../res/happy/happy5.bmp
-../../res/sad/sad1.bmp
-../../res/sad/sad2.bmp
-
-...
-
-```