update youtube dnn (#181)

Co-authored-by: tangwei12 <[email protected]>

Author: 123malin

models/recall/youtube_dnn/README.md

@@ -0,0 +1,146 @@
+# Youtebe-DNN
+
+以下是本例的简要目录结构及说明： 
+
+```
+├── data #样例数据
+	├── train
+		├── data.txt
+    ├── test
+		├── data.txt
+├── generate_ramdom_data # 随机训练数据生成文件
+├── __init__.py
+├── README.md # 文档
+├── model.py #模型文件
+├── config.yaml #配置文件
+├── data_prepare.sh #一键数据处理脚本
+├── reader.py #reader
+├── infer.py # 预测程序
+```
+
+注：在阅读该示例前，建议您先了解以下内容：
+
+[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md)
+
+
+---
+## 内容
+
+- [模型简介](#模型简介)
+- [数据准备](#数据准备)
+- [运行环境](#运行环境)
+- [快速开始](#快速开始)
+- [论文复现](#论文复现)
+- [进阶使用](#进阶使用)
+- [FAQ](#FAQ)
+
+## 模型简介
+[《Deep Neural Networks for YouTube Recommendations》](https://link.zhihu.com/?target=https%3A//static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) 这篇论文是google的YouTube团队在推荐系统上DNN方面的尝试，是经典的向量化召回模型，主要通过模型来学习用户和物品的兴趣向量，并通过内积来计算用户和物品之间的相似性，从而得到最终的候选集。YouTube采取了两层深度网络完成整个推荐过程：
+
+1.第一层是**Candidate Generation Model**完成候选视频的快速筛选，这一步候选视频集合由百万降低到了百的量级。
+
+2.第二层是用**Ranking Model**完成几百个候选视频的精排。
+
+本项目在paddlepaddle上完成YouTube dnn的召回部分Candidate Generation Model，分别获得用户和物品的向量表示，从而后续可以通过其他方法（如用户和物品的余弦相似度）给用户推荐物品。
+
+由于原论文没有开源数据集，本项目随机构造数据验证网络的正确性。
+
+本项目支持功能
+
+训练：单机CPU、单机单卡GPU、本地模拟参数服务器训练、增量训练，配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md)   
+
+预测：单机CPU、单机单卡GPU；配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md) 
+
+## 数据处理
+调用python generate_ramdom_data.py生成随机训练数据，每行数据格式如下：
+```
+#watch_vec;search_vec;other_feat;label
+0.01,0.02,...,0.09;0.01,0.02,...,0.09;0.01,0.02,...,0.09;20
+```
+方便起见，我们提供了一键式数据生成脚本：
+```
+sh data_prepare.sh
+```
+
+## 运行环境
+
+PaddlePaddle>=1.7.2 
+
+python 2.7/3.5/3.6/3.7
+
+PaddleRec >=0.1
+
+os : windows/linux/macos
+
+## 快速开始
+
+### 单机训练
+
+```
+mode: [cpu_single_train]
+
+runner:
+- name: cpu_single_train
+  class: train
+  device: cpu   # if use_gpu, set it to gpu
+  epochs: 20
+  save_checkpoint_interval: 1
+  save_inference_interval: 1
+  save_checkpoint_path: "increment_youtubednn"
+  save_inference_path: "inference_youtubednn"
+  save_inference_feed_varnames: ["watch_vec", "search_vec", "other_feat"] # feed vars of save inference
+  save_inference_fetch_varnames: ["l3.tmp_2"]
+  print_interval: 1
+```
+
+### 单机预测
+通过计算每个用户和每个物品的余弦相似度，给每个用户推荐topk视频：
+
+cpu infer:
+```
+python infer.py --test_epoch 19 --inference_model_dir ./inference_youtubednn --increment_model_dir ./increment_youtubednn --watch_vec_size 64 --search_vec_size 64 --other_feat_size 64 --topk 5
+```
+
+gpu infer:
+```
+python infer.py --use_gpu 1 --test_epoch 19 --inference_model_dir ./inference_youtubednn --increment_model_dir ./increment_youtubednn --watch_vec_size 64 --search_vec_size 64 --other_feat_size 64 --topk 5
+```
+### 运行
+```
+python -m paddlerec.run -m paddlerec.models.recall.w2v
+```
+
+### 结果展示
+
+样例数据训练结果展示：
+
+```
+Running SingleStartup.
+Running SingleRunner.
+batch: 1, acc: [0.03125]
+batch: 2, acc: [0.0625]
+batch: 3, acc: [0.]
+...
+epoch 0 done, use time: 0.0605320930481, global metrics: acc=[0.]
+...
+epoch 19 done, use time: 0.33447098732, global metrics: acc=[0.]
+```
+
+样例数据预测结果展示:
+```
+user:0, top K videos:[40, 31, 4, 33, 93]
+user:1, top K videos:[35, 57, 58, 40, 17]
+user:2, top K videos:[35, 17, 88, 40, 9]
+user:3, top K videos:[73, 35, 39, 58, 38]
+user:4, top K videos:[40, 31, 57, 4, 73]
+user:5, top K videos:[38, 9, 7, 88, 22]
+user:6, top K videos:[35, 73, 14, 58, 28]
+user:7, top K videos:[35, 73, 58, 38, 56]
+user:8, top K videos:[38, 40, 9, 35, 99]
+user:9, top K videos:[88, 73, 9, 35, 28]
+user:10, top K videos:[35, 52, 28, 54, 73]
+```
+
+## 进阶使用
+
+## FAQ

models/recall/youtube_dnn/config.yaml

@@ -17,11 +17,10 @@ workspace: "models/recall/youtube_dnn"
 
 dataset:
 - name: dataset_train
-  batch_size: 5
-  type: DataLoader
-  #type: QueueDataset
+  batch_size: 32
+  type: DataLoader # or QueueDataset
   data_path: "{workspace}/data/train"
-  data_converter: "{workspace}/random_reader.py"
+  data_converter: "{workspace}/reader.py"
 
 hyper_parameters:
   watch_vec_size: 64
@@ -30,22 +29,23 @@ hyper_parameters:
   output_size: 100
   layers: [128, 64, 32]
   optimizer: 
-    class: adam
-    learning_rate: 0.001
-    strategy: async
+    class: SGD
+    learning_rate: 0.01
 
-mode: train_runner
+mode: [cpu_single_train]
 
 runner:
-- name: train_runner
+- name: cpu_single_train
   class: train
   device: cpu
-  epochs: 3
-  save_checkpoint_interval: 2
-  save_inference_interval: 4
-  save_checkpoint_path: "increment"
-  save_inference_path: "inference"
-  print_interval: 10
+  epochs: 20
+  save_checkpoint_interval: 1
+  save_inference_interval: 1
+  save_checkpoint_path: "increment_youtubednn"
+  save_inference_path: "inference_youtubednn"
+  save_inference_feed_varnames: ["watch_vec", "search_vec", "other_feat"] # feed vars of save inference
+  save_inference_fetch_varnames: ["l3.tmp_2"]
+  print_interval: 1
 
 phase:
 - name: train