[ README ] update readme

This patch includes readme update.

**Self evaluation:**
1. Build test:	 [X]Passed [ ]Failed [ ]Skipped
2. Run test:	 [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

diff --git a/README.md b/README.md
index 2ff8019..0d66b70 100644 (file)
--- a/README.md
+++ b/README.md
@@ -59,16 +59,36 @@ Even though NNTrainer can be used to train sub-models, it requires implementatio
 
 This component defines layers which consist of a neural network model. Layers have their own properties to be set.
 
- | Keyword | Layer Name | Description |
+ | Keyword | Layer Class Name | Description |
  |:-------:|:---:|:---|
- |  conv2d | Convolution 2D |Convolution 2-Dimentional Layer |
- |  pooling2d | Pooling 2D |Pooling 2-Dimentional Layer. Support average / max / global average / global max pooling |
- | flatten | Flatten | Flatten Layer |
- | fully_connected | Fully Connected | Fully Connected Layer |
- | input | Input | Input Layer.  This is not always required. |
- | batch_normalization | Batch Normalization Layer | Batch Normalization Layer. |
- | loss layer | loss layer | hidden from users |
- | activation | activaiton layer | set by layer property |
+ | 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 |
+ | backobne_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 |
 
 ### Supported Optimizers
 
@@ -83,11 +103,12 @@ NNTrainer Provides
 
 NNTrainer provides
 
- | Keyword | Loss Name | Description |
+ | Keyword | Class 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 |
+ | 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 |
 
 ### Supported Activation Functions
 
@@ -150,6 +171,26 @@ 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.
 
+#### [ResNet Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/Resnet)
+
+An example to train resnet18 network.
+
+#### [VGG Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/VGG)
+
+An example to train vgg16 network.
+
+#### [ProductRating Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/ProductRatings)
+
+This application contains a simple embedding-based model that predicts ratings given a user and a product.
+
+#### [SimpleShot Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/SimpleShot)
+
+An example to demonstrate few-shot learning : SimpleShot
+
+#### [Custom Example](https://github.com/nnstreamer/nntrainer/tree/main/Applications/Custom)
+
+An example to demonstrate how to create custom layers, optimizers or other supported objects.
+
 #### ~Tizen CAPI Example~
 
 An example to demonstrate c api for Tizen. It is same transfer learing but written with tizen c api.~