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深度学习相关的模型训练、评估和预测相关代码

Python 96.13% Shell 3.87%

deeplearning's Introduction

介绍

常用的深度学习模型训练、评估和预测相关代码，基于Tensorflow高阶API（Estimator）实现；尽量做到可读性和通用性较好。

部分模型子目录下有较详细的文档介绍！

关于其中某些模型的介绍，请参考文章《主流CTR预估模型的演化及对比》！《深度CTR预估模型中的特征自动组合机制演化简史》

除非特殊说明，本项目的代码都是基于tensorflow 1.6.0开发。

后记

欢迎关注我的知乎专栏：算法工程师的自我修养
欢迎收藏我的个人博客，会不定期更新：https://yangxudong.github.io，或者国内镜像：https://xudongyang.coding.me

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deeplearning's Issues

DCN sample data

Hello。

could you please provide some sample data for DCN?
thank you.

我看这里面都是高级算子，线上调用的话，计算会很慢吧。请问能否给出线上调用的demo?

ESMM导出模型export_savedmodel之后的数据输入格式

请教大佬，在esmm中export_savedmodel之后得到保存的模型文件，然后想在新的session里load saved_model进行预测，这时候如果用feed_dict方式传入的话
sess.run('CTCVR:0', feed_dict={'input_example_tensor:0': [X]})
这里的X应该是什么格式呢？尝试了直接传入字符串会报如下错误
InvalidArgumentError (see above for traceback): Could not parse example input, value: '{"behaviorBids":[24844,2674,29474,2855,30250,32057,35],"behaviorC1ids":[3,5,591,1230,2],"behaviorCids":[2187,164,713,47,73,78,61],"behaviorPids":[8567830,1098681,3861814,577623,2965309,4019581,8369742],"behaviorSids":[85039235,138228,36774732,118520,46571321,49310857,38369486],"bidWeights":[0.9999832,0.99967366,0.999643,0.9995961,0.999551,0.9890053,0.9889464],"brandPrefer":0,"c1idWeights":[0.9999832,0.99967366,0.999643,0.9995961,0.9890053],"cate2Prefer":0.11423163,"catePrefer":0.04157726,"cidWeights":[0.9999832,0.99967366,0.999643,0.9995961,0.999551,0.9890053,0.9889464],"click":0,"matchScore":0.3556864,"matchType":4,"pay":0,"phoneBrand":"apple","phoneOs":"ios","phoneResolution":"375*812","pidWeights":[0.9999832,0.99967366,0.999643,0.9995961,0.999551,0.9890053,0.9889464],"popScore":0.001,"position":23,"productId":8518322,"sellerPrefer":0,"share":0,"sidWeights":[0.9999832,0.99967366,0.999643,0.9995961,0.999551,0.9890053,0.9889464],"time":1544615913,"triggerNum":1,"triggerRank":10,"type":1,"userId":41233203,"userType":0}' [[node ParseExample/ParseExample ]]

我看YouTubeNet原本的召回阶段使用的是softmax分类，是因为认为这个应该被理解为多分类问题，可以“同时”选择多个“下个”要播放的视频吗？
https://github.com/yangxudong/deeplearning/blob/master/youtube_match_model/youtube_match_model.py
loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=labels_one_hot,
logits=logits)

谢谢！