Rename v2 to legacy (#1323)

README.md

@@ -8,7 +8,7 @@ PaddlePaddle provides a rich set of computational units to enable users to adopt
 
 - [fluid models](fluid): use PaddlePaddle's Fluid APIs. We especially recommend users to use Fluid models.
 
-- [v2 models](v2): use PaddlePaddle's v2 APIs.
+- [legacy models](legacy): use PaddlePaddle's v2 APIs.
 
 
 ## License

v2/README.md → legacy/README.md

@@ -12,23 +12,23 @@ The word embedding expresses words with a real vector. Each dimension of the vec
 
 In the example of word vectors, we show how to use Hierarchical-Sigmoid and Noise Contrastive Estimation (NCE) to accelerate word-vector learning.
 
-- 1.1 [Hsigmoid Accelerated Word Vector Training](https://github.com/PaddlePaddle/models/tree/develop/v2/hsigmoid)
-- 1.2 [Noise Contrastive Estimation Accelerated Word Vector Training](https://github.com/PaddlePaddle/models/tree/develop/v2/nce_cost)
+- 1.1 [Hsigmoid Accelerated Word Vector Training](https://github.com/PaddlePaddle/models/tree/develop/legacy/hsigmoid)
+- 1.2 [Noise Contrastive Estimation Accelerated Word Vector Training](https://github.com/PaddlePaddle/models/tree/develop/legacy/nce_cost)
 
 
 ## 2. RNN language model
 
 The language model is important in the field of natural language processing. In addition to getting the word vector (a by-product of language model training), it can also help us to generate text. Given a number of words, the language model can help us predict the next most likely word. In the example of using the language model to generate text, we focus on the recurrent neural network language model. We can use the instructions in the document quickly adapt to their training corpus, complete automatic writing poetry, automatic writing prose and other interesting models.
 
-- 2.1 [Generate text using the RNN language model](https://github.com/PaddlePaddle/models/tree/develop/v2/generate_sequence_by_rnn_lm)
+- 2.1 [Generate text using the RNN language model](https://github.com/PaddlePaddle/models/tree/develop/legacy/generate_sequence_by_rnn_lm)
 
 ## 3. Click-Through Rate prediction
 The click-through rate model predicts the probability that a user will click on an ad. This is widely used for advertising technology. Logistic Regression has a good learning performance for large-scale sparse features in the early stages of the development of click-through rate prediction. In recent years, DNN model because of its strong learning ability to gradually take the banner rate of the task of the banner.
 
 In the example of click-through rate estimates, we first give the Google's Wide & Deep model. This model combines the advantages of DNN and the applicable logistic regression model for DNN and large-scale sparse features. Then we provide the deep factorization machine for click-through rate prediction. The deep factorization machine combines the factorization machine and deep neural networks to model both low order and high order interactions of input features.
 
-- 3.1 [Click-Through Rate Model](https://github.com/PaddlePaddle/models/tree/develop/v2/ctr)
-- 3.2 [Deep Factorization Machine for Click-Through Rate prediction](https://github.com/PaddlePaddle/models/tree/develop/v2/deep_fm)
+- 3.1 [Click-Through Rate Model](https://github.com/PaddlePaddle/models/tree/develop/legacy/ctr)
+- 3.2 [Deep Factorization Machine for Click-Through Rate prediction](https://github.com/PaddlePaddle/models/tree/develop/legacy/deep_fm)
 
 ## 4. Text classification
 
@@ -36,7 +36,7 @@ Text classification is one of the most basic tasks in natural language processin
 
 For text classification, we provide a non-sequential text classification model based on DNN and CNN. (For LSTM-based model, please refer to PaddleBook [Sentiment Analysis](http://www.paddlepaddle.org/docs/develop/book/06.understand_sentiment/index.html)).
 
-- 4.1 [Sentiment analysis based on DNN / CNN](https://github.com/PaddlePaddle/models/tree/develop/v2/text_classification)
+- 4.1 [Sentiment analysis based on DNN / CNN](https://github.com/PaddlePaddle/models/tree/develop/legacy/text_classification)
 
 ## 5. Learning to rank
 
@@ -45,14 +45,14 @@ The depth neural network can be used to model the fractional function to form va
 
 The algorithms for learning to rank are usually categorized into three groups by their input representation and the loss function. These are pointwise, pairwise and listwise approaches. Here we demonstrate RankLoss loss function method (pairwise approach), and LambdaRank loss function method (listwise approach). (For Pointwise approaches, please refer to [Recommended System](http://www.paddlepaddle.org/docs/develop/book/05.recommender_system/index.html)).
 
-- 5.1 [Learning to rank based on Pairwise and Listwise approches](https://github.com/PaddlePaddle/models/tree/develop/v2/ltr)
+- 5.1 [Learning to rank based on Pairwise and Listwise approches](https://github.com/PaddlePaddle/models/tree/develop/legacy/ltr)
 
 ## 6. Semantic model
 The deep structured semantic model uses the DNN model to learn the vector representation of the low latitude in a continuous semantic space, finally models the semantic similarity between the two sentences.
 
 In this example, we demonstrate how to use PaddlePaddle to implement a generic deep structured semantic model to model the semantic similarity between two strings. The model supports different network structures such as CNN (Convolutional Network), FC (Fully Connected Network), RNN (Recurrent Neural Network), and different loss functions such as classification, regression, and sequencing.
 
-- 6.1 [Deep structured semantic model](https://github.com/PaddlePaddle/models/tree/develop/v2/dssm)
+- 6.1 [Deep structured semantic model](https://github.com/PaddlePaddle/models/tree/develop/legacy/dssm)
 
 ## 7. Sequence tagging
 
@@ -60,7 +60,7 @@ Given the input sequence, the sequence tagging model is one of the most basic ta
 
 In the example of the sequence tagging, we describe how to train an end-to-end sequence tagging model with the Named Entity Recognition (NER) task as an example.
 
-- 7.1 [Name Entity Recognition](https://github.com/PaddlePaddle/models/tree/develop/v2/sequence_tagging_for_ner)
+- 7.1 [Name Entity Recognition](https://github.com/PaddlePaddle/models/tree/develop/legacy/sequence_tagging_for_ner)
 
 ## 8. Sequence to sequence learning
 
@@ -68,19 +68,19 @@ Sequence-to-sequence model has a wide range of applications. This includes machi
 
 As an example for sequence-to-sequence learning, we take the machine translation task. We demonstrate the sequence-to-sequence mapping model without attention mechanism, which is the basis for all sequence-to-sequence learning models. We will use scheduled sampling to improve the problem of error accumulation in the RNN model, and machine translation with external memory mechanism.
 
-- 8.1 [Basic Sequence-to-sequence model](https://github.com/PaddlePaddle/models/tree/develop/v2/nmt_without_attention)
+- 8.1 [Basic Sequence-to-sequence model](https://github.com/PaddlePaddle/models/tree/develop/legacy/nmt_without_attention)
 
 ## 9. Image classification
 
 For the example of image classification, we show you how to train AlexNet, VGG, GoogLeNet, ResNet, Inception-v4, Inception-Resnet-V2 and Xception models in PaddlePaddle. It also provides model conversion tools that convert Caffe or TensorFlow trained model files into PaddlePaddle model files.
 
-- 9.1 [convert Caffe model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification/caffe2paddle)
-- 9.2 [convert TensorFlow model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification/tf2paddle)
-- 9.3 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification)
-- 9.4 [VGG](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification)
-- 9.5 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification)
-- 9.6 [Inception-v4](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification)
-- 9.7 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification)
-- 9.8 [Xception](https://github.com/PaddlePaddle/models/tree/develop/v2/image_classification)
+- 9.1 [convert Caffe model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification/caffe2paddle)
+- 9.2 [convert TensorFlow model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification/tf2paddle)
+- 9.3 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification)
+- 9.4 [VGG](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification)
+- 9.5 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification)
+- 9.6 [Inception-v4](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification)
+- 9.7 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification)
+- 9.8 [Xception](https://github.com/PaddlePaddle/models/tree/develop/legacy/image_classification)
 
 This tutorial is contributed by [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) and licensed under the [Apache-2.0 license](LICENSE).