[utils] Add getRealpath function

[platform/core/ml/nntrainer.git] / README.md

diff --git a/README.md b/README.md
index b5f69f3..5be83be 100644 (file)
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ NNtrainer is an Open Source Project. The aim of the NNtrainer is to develop a So
 
 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).
 
-[ NNTraner: Light-Weight On-Device Training Framework ](https://arxiv.org/pdf/2206.04688.pdf), arXiv, 2022 <br />
+[ NNTrainer: Light-Weight On-Device Training Framework ](https://arxiv.org/pdf/2206.04688.pdf), arXiv, 2022 <br />
 [ NNTrainer: Towards the on-device learning for personalization ](https://www.youtube.com/watch?v=HWiV7WbIM3E), Samsung Software Developer Conference 2021 (Korean) <br />
 [ NNTrainer: Personalize neural networks on devices! ](https://www.youtube.com/watch?v=HKKowY78P1A), Samsung Developer Conference 2021 <br />
 [ NNTrainer: "On-device learning" ](https://www.youtube.com/embed/Jy_auavraKg?start=4035&end=4080), Samsung AI Forum 2021
@@ -79,6 +79,7 @@ This component defines layers which consist of a neural network model. Layers ha
  | pooling2D | Pooling2DLayer | Pooling 2D layer |
  | input | InputLayer | Input Layer.  This is not always required. |
  | batch_normalization | BatchNormalizationLayer | Batch normalization layer |
+ | layer_normalization | LayerNormalizationLayer | Layer normalization layer |
  | activation | ActivaitonLayer | Set by layer property |
  | addition | AdditionLayer | Add input input layers |
  | attention | AttentionLayer | Attenstion layer |
@@ -86,7 +87,7 @@ This component defines layers which consist of a neural network model. Layers ha
  | concat | ConcatLayer | Concatenate input layers |
  | multiout | MultiOutLayer | Multi-Output Layer |
  | backbone_nnstreamer | NNStreamerLayer | Encapsulate NNStreamer layer |
- | backbone_tflite | TfLiteLayer | Encapsulate tflite as an layer |
+ | backbone_tflite | TfLiteLayer | Encapsulate tflite as a layer |
  | permute | PermuteLayer | Permute layer for transpose |
  | preprocess_flip | PreprocessFlipLayer | Preprocess random flip layer |
  | preprocess_l2norm | PreprocessL2NormLayer | Preprocess simple l2norm layer to normalize |
@@ -95,6 +96,7 @@ This component defines layers which consist of a neural network model. Layers ha
  | split | SplitLayer | Split layer |
  | dropout | DropOutLayer | Dropout Layer |
  | embedding | EmbeddingLayer | Embedding Layer |
+ | positional_encoding | PositionalEncodingLayer | Positional Encoding Layer |
  | rnn | RNNLayer | Recurrent Layer |
  | rnncell | RNNCellLayer | Recurrent Cell Layer |
  | gru | GRULayer | Gated Recurrent Unit Layer |
@@ -103,6 +105,8 @@ This component defines layers which consist of a neural network model. Layers ha
  | lstmcell | LSTMCellLayer | Long Short-Term Memory Cell Layer |
  | zoneoutlstmcell | ZoneoutLSTMCellLayer | Zoneout Long Short-Term Memory Cell Layer |
  | time_dist | TimeDistLayer | Time distributed Layer |
+ | multi_head_attention | MultiHeadAttentionLayer | Multi Head Attention Layer |
+
 
 ### Supported Optimizers
 
@@ -113,6 +117,12 @@ NNTrainer Provides
  | sgd | Stochastic Gradient Decent | - |
  | adam | Adaptive Moment Estimation | - |
 
+ | Keyword | Leanring Rate | Description |
+ |:-------:|:---:|:---:|
+ | exponential | exponential learning rate decay | - |
+ | constant | constant learning rate | - |
+ | step | step learning rate | - |
+
 ### Supported Loss Functions
 
 NNTrainer provides
@@ -123,6 +133,7 @@ NNTrainer provides
  | cross_softmax | CrossEntropySoftmaxLossLayer | Cross entropy softmax loss layer |
  | constant_derivative | ConstantDerivativeLossLayer | Constant derivative loss layer |
  | mse | MSELossLayer | Mean square error loss layer |
+ | kld | KLDLossLayer | Kullback-Leibler Divergence loss layer |
 
 ### Supported Activation Functions
 
@@ -134,9 +145,6 @@ NNTrainer provides
  | 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
 
@@ -159,8 +167,7 @@ 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 |
+ | weight_regularizer | weight decay ( L2Norm only ) | needs set weight_regularizer_param & type |
 
 ### APIs
 Currently, we provide [C APIs](https://github.com/nnstreamer/nntrainer/blob/master/api/capi/include/nntrainer.h) for Tizen. [C++ APIs](https://github.com/nnstreamer/nntrainer/blob/master/api/ccapi/include) are also provided for other platform. Java & C# APIs will be provided soon.
@@ -180,7 +187,7 @@ NNTrainer example for a variety of networks
 
 ## Open Source License
 
-The nntrainer is an open source project released under the terms of the Apache License version 2.0.
+The NNtrainer is an open source project released under the terms of the Apache License version 2.0.
 
 ## Contributing