# NNtrainer [![Code Coverage](http://nnsuite.mooo.com/nntrainer/ci/badge/codecoverage.svg)](http://nnsuite.mooo.com/nntrainer/ci/gcov_html/index.html) ![GitHub repo size](https://img.shields.io/github/repo-size/nnstreamer/nntrainer) ![GitHub issues](https://img.shields.io/github/issues/nnstreamer/nntrainer) ![GitHub pull requests](https://img.shields.io/github/issues-pr/nnstreamer/nntrainer) NNtrainer is a Software Framework for training Neural Network models on devices. ## Overview NNtrainer is an Open Source Project. The aim of the NNtrainer is to develop a Software Framework to train neural network models on embedded devices which have relatively limited resources. Rather than training whole layers of a network, NNtrainer trains only one or a few layers of the layers added after a feature extractor. Even though NNTrainer can be used to train sub-models, it requires implementation of additional functionalities to train models obtained from other machine learning and deep learning libraries. In the current version, various machine learning algorithms such as k-Nearest Neighbor (k-NN), Neural Networks, Logistic Regression and Reinforcement Learning algorithms are implemented. We also provide examples for various tasks such as transfer learning of models. In some of these examples, deep learning models such as Mobilenet V2 trained with Tensorflow-lite, are used as feature extractors. All of these were tested on Galaxy S8 with Android and PC (Ubuntu 16.04). ## Maintainer * [Jijoong Moon](https://github.com/jijoongmoon) * [MyungJoo Ham](https://github.com/myungjoo) * [Geunsik Lim](https://github.com/leemgs) ## Reviewers * [Sangjung Woo](https://github.com/again4you) * [Wook Song](https://github.com/wooksong) * [Jaeyun Jung](https://github.com/jaeyun-jung) * [Hyoungjoo Ahn](https://github.com/helloahn) * [Parichay Kapoor](https://github.com/kparichay) * [Dongju Chae](https://github.com/dongju-chae) * [Gichan Jang](https://github.com/gichan-jang) * [Yongjoo Ahn](https://github.com/anyj0527) * [Jihoon Lee](https://github.com/zhoonit) * [Hyeonseok Lee](https://github.com/lhs8928) * [Mete Ozay](https://github.com/meteozay) ## Components ### Supported Layers This component defines layers which consist of a neural network model. Layers have their own properties to be set. | Keyword | Layer Name | Description | |:-------:|:---:|:---| | conv2d | Convolution 2D |Convolution 2-Dimentional Layer | | pooling2d | Pooling 2D |Pooling 2-Dimentional Layer. Support average / max / global average / global max pooing | | flatten | Flatten | Flatten Layer | | fully_connected | Fully Connected | Fully Connected Layer | | input | Input | Input Layer. This is not always requied. | | batch_normalization | Batch Normalization Layer | Batch Normalization Layer. | | loss layer | loss layer | hidden from users | | activation | activaiton layer | set by layer property | ### Supported Optimizers NNTrainer Provides | Keyword | Optimizer Name | Description | |:-------:|:---:|:---:| | sgd | Stochastic Gradient Decent | - | | adam | Adaptive Moment Estimation | - | ### Supported Loss Functions NNTrainer provides | Keyword | Loss Name | Description | |:-------:|:---:|:---:| | mse | Mean squared Error | - | | cross | Cross Entropy - sigmoid | if activation last layer is sigmoid | | cross | Cross Entropy - softmax | if activation last layer is softmax | ### Supported Activation Functions NNTrainer provides | Keyword | Loss Name | Description | |:-------:|:---:|:---| | tanh | tanh function | set as layer property | | sigmoid | sigmoid function | set as layer property | | relu | relu function | set as layer propery | | softmax | softmax function | set as layer propery | | weight_initializer | Weight Initialization | Xavier(Normal/Uniform), LeCun(Normal/Uniform), HE(Normal/Unifor) | | weight_regularizer | weight decay ( L2Norm only ) | needs set weight_regularizer_param & type | | learnig_rate_decay | learning rate decay | need to set step | ### Tensor Tensor is responsible for calculation of a layer. It executes several operations such as addition, division, multiplication, dot production, data averaging and so on. In order to accelerate calculation speed, CBLAS (C-Basic Linear Algebra: CPU) and CUBLAS (CUDA: Basic Linear Algebra) for PC (Especially NVIDIA GPU) are implemented for some of the operations. Later, these calculations will be optimized. Currently, we supports lazy calculation mode to reduce complexity for copying tensors during calculations. | Keyword | Description | |:-------:|:---:| | 4D Tensor | B, C, H, W| | Add/sub/mul/div | - | | sum, average, argmax | - | | Dot, Transpose | - | | normalization, standardization | - | | save, read | - | ### Others NNTrainer provides | Keyword | Loss Name | Description | |:-------:|:---:|:---| | weight_initializer | Weight Initialization | Xavier(Normal/Uniform), LeCun(Normal/Uniform), HE(Normal/Unifor) | | weight_regularizer | weight decay ( L2Norm only ) | needs set weight_regularizer_constant & type | | learnig_rate_decay | learning rate decay | need to set step | ### APIs Currently, we provide [C APIs](https://github.com/nnstreamer/nntrainer/blob/master/api/capi/include/nntrainer.h) for Tizen. C++ API will be also provided soon. ### Examples for NNTrainer #### [Custom Shortcut Application](https://github.com/nnstreamer/nntrainer/tree/main/Applications/Tizen_native/CustomShortcut) A demo application which enable user defined custom shortcut on galaxy watch. #### [MNIST Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/MNIST) An example to train mnist dataset. It consists two convolution 2d layer, 2 pooling 2d layer, flatten layer and fully connected layer. #### [Reinforcement Learning Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/ReinforcementLearning/DeepQ) A reinforcement learning example with cartpole game. It is using DeepQ algorithm. #### [Transfer Learning Examples](https://github.com/nnstreamer/nntrainer/tree/main/Applications/TransferLearning) Transfer learning examples with for image classification using the Cifar 10 dataset and for OCR. TFlite is used for feature extractor and modify last layer (fully connected layer) of network. #### ~Tizen CAPI Example~ An example to demonstrate c api for Tizen. It is same transfer learing but written with tizen c api.~ Deleted instead moved to a [test](https://github.com/nnstreamer/nntrainer/blob/master/test/tizen_capi/unittest_tizen_capi.cpp) #### [KNN Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/KNN) A transfer learning example with for image classification using the Cifar 10 dataset. TFlite is used for feature extractor and compared with KNN. #### [Logistic Regression Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/LogisticRegression) A logistic regression example using NNTrainer. ## [Getting Started](https://github.com/nnstreamer/nntrainer/blob/main/docs/getting-started.md) Instructions for installing NNTrainer. ### [Running Examples](https://github.com/nnstreamer/nntrainer/blob/main/docs/how-to-run-examples.md) Instructions for preparing NNTrainer for execution ## Open Source License The nntrainer is an open source project released under the terms of the Apache License version 2.0. ## Contributing Contributions are welcome! Please see our [Contributing](https://github.com/nnstreamer/nntrainer/blob/main/docs/contributing.md) Guide for more details. 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