NNtrainer is a Software Framework for training Neural Network models on devices.
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 from the scratch, NNtrainer finetunes the neural network model on device with user data for the personalization.
Even if NNtariner runs on device, it provides full functionalities to train models and also utilizes limited device resources efficiently. NNTrainer is able to train various machine learning algorithms such as k-Nearest Neighbor (k-NN), Neural Networks, Logistic Regression, Reinforcement Learning algorithms, Recurrent network and more. We also provide examples for various tasks such as Few-shot learning, ResNet, VGG, Product Rating and more will be added. All of these were tested on Samsung Galaxy smart phone with Android and PC (Ubuntu 18.04/20.04).
NNTrainer: Towards the on-device learning for personalization , Samsung Software Developer Conference 2021 (Korean)
NNTrainer: Personalize neural networks on devices! , Samsung Developer Conference 2021
NNTrainer: "On-device learning" , Samsung AI Forum 2021
| Tizen | Ubuntu | Android/NDK Build | |
|---|---|---|---|
| 6.0M2 and later | 18.04 | 9/P | |
| arm |  |
Available | Ready |
| arm64 |  |
Available |  |
| x64 |  |
 |
Ready |
| x86 |  |
N/A | N/A |
| Publish | Tizen Repo | PPA | |
| API | C (Official) | C/C | C/C |
- Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
- Available: binary packages are released and deployed automatically and periodically along with CI tests.
- Daily Release
- SDK Support: Tizen Studio (6.0 M2 )
- Sangjung Woo
- Wook Song
- Jaeyun Jung
- Hyoungjoo Ahn
- Parichay Kapoor
- Dongju Chae
- Gichan Jang
- Yongjoo Ahn
- Jihoon Lee
- Hyeonseok Lee
- Mete Ozay
- Hyunil Park
- Jiho Chu
This component defines layers which consist of a neural network model. Layers have their own properties to be set.
| Keyword | Layer Class Name | Description |
|---|---|---|
| conv1d | Conv1DLayer | Convolution 1-Dimentional Layer |
| conv2d | Conv2DLayer | Convolution 2-Dimentional Layer |
| pooling2d | Pooling2DLayer | Pooling 2-Dimentional Layer. Support average / max / global average / global max pooling |
| flatten | FlattenLayer | Flatten layer |
| fully_connected | FullyConnectedLayer | Fully connected layer |
| input | InputLayer | Input Layer. This is not always required. |
| batch_normalization | BatchNormalizationLayer | Batch normalization layer |
| activation | ActivaitonLayer | Set by layer property |
| addition | AdditionLayer | Add input input layers |
| attention | AttentionLayer | Attenstion layer |
| centroid_knn | CentroidKNN | Centroid K-nearest neighbor layer |
| concat | ConcatLayer | Concatenate input layers |
| multiout | MultiOutLayer | Multi-Output Layer |
| backbone_nnstreamer | NNStreamerLayer | Encapsulate NNStreamer layer |
| backbone_tflite | TfLiteLayer | Encapsulate tflite as an layer |
| permute | PermuteLayer | Permute layer for transpose |
| preprocess_flip | PreprocessFlipLayer | Preprocess random flip layer |
| preprocess_l2norm | PreprocessL2NormLayer | Preprocess simple l2norm layer to normalize |
| preprocess_translate | PreprocessTranslateLayer | Preprocess translate layer |
| reshape | ReshapeLayer | Reshape tensor dimension layer |
| split | SplitLayer | Split layer |
| dropout | DropOutLayer | Dropout Layer |
| embedding | EmbeddingLayer | Embedding Layer |
| rnn | RNNLayer | Recurrent Layer |
| gru | GRULayer | Gated Recurrent Unit Layer |
| lstm | LSTMLayer | Long Short-Term Memory Layer |
| lstmcell | LSTMCellLayer | Long Short-Term Memory Cell Layer |
| time_dist | TimeDistLayer | Time distributed Layer |
NNTrainer Provides
| Keyword | Optimizer Name | Description |
|---|---|---|
| sgd | Stochastic Gradient Decent | - |
| adam | Adaptive Moment Estimation | - |
NNTrainer provides
| Keyword | Class Name | Description |
|---|---|---|
| cross_sigmoid | CrossEntropySigmoidLossLayer | Cross entropy sigmoid loss layer |
| cross_softmax | CrossEntropySoftmaxLossLayer | Cross entropy softmax loss layer |
| constant_derivative | ConstantDerivativeLossLayer | Constant derivative loss layer |
| mse | MSELossLayer | Mean square error loss layer |
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 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 | - |
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 |
Currently, we provide C APIs for Tizen. C APIs are also provided for other platform. Java & C# APIs will be provided soon.
A demo application which enable user defined custom shortcut on galaxy watch.
An example to train mnist dataset. It consists two convolution 2d layer, 2 pooling 2d layer, flatten layer and fully connected layer.
A reinforcement learning example with cartpole game. It is using DeepQ algorithm.
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.
An example to train resnet18 network.
An example to train vgg16 network.
This application contains a simple embedding-based model that predicts ratings given a user and a product.
An example to demonstrate few-shot learning : SimpleShot
An example to demonstrate how to create custom layers, optimizers or other supported objects.
A transfer learning example with for image classification using the Cifar 10 dataset. TFlite is used for feature extractor and compared with KNN.
A logistic regression example using NNTrainer.
Instructions for installing NNTrainer.
Instructions for preparing NNTrainer for execution
The nntrainer is an open source project released under the terms of the Apache License version 2.0.
Contributions are welcome! Please see our Contributing Guide for more details.
