Welcome to DeepCTR’s documentation!

DeepCTR is a Easy-to-use , Modular and Extendible package of deep-learning based CTR models along with lots of core components layer which can be used to easily build custom models.You can use any complex model with model.fit() and model.predict().

• Provide tf.keras.Model like interface for quick experiment. example
• Provide tensorflow estimator interface for large scale data and distributed training. example
• It is compatible with both tf 1.x and tf 2.x.

Let’s Get Started! (Chinese Introduction)

You can read the latest code and related projects

• DeepCTR: https://github.com/shenweichen/DeepCTR
• DeepMatch: https://github.com/shenweichen/DeepMatch
• DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch

News

03/13/2021 : Add BST . Changelog

02/12/2021 : Fix bug in DCN-Mix. Changelog

01/06/2021 : Add DCN-Mix (中文介绍) and support transform_fn in DenseFeat. Changelog

DisscussionGroup

Discussions 公众号：浅梦的学习笔记 wechat ID: deepctrbot

Home:

• Quick-Start
  • Installation Guide
  • Getting started: 4 steps to DeepCTR
  • Getting started: 4 steps to DeepCTR Estimator with TFRecord
• Features
  • Overview
  • Feature Columns
  • Models
  • Layers
• Examples
  • Classification: Criteo
  • Classification: Criteo with feature hashing on the fly
  • Regression: Movielens
  • Multi-value Input : Movielens
  • Multi-value Input : Movielens with feature hashing on the fly
  • Estimator with TFRecord: Classification Criteo
  • Estimator with Pandas DataFrame: Classification Criteo
• FAQ
  • 1. Save or load weights/models
  • 2. Set learning rate and use earlystopping
  • 3. Get the attentional weights of feature interactions in AFM
  • 4. How to extract the embedding vectors in deepfm?
  • 5. How to add a long dense feature vector as a input to the model?
  • 6. How to use pretrained weights to initialize embedding weights and frozen embedding weights?
  • 7. How to run the demo with GPU ?
  • 8. How to run the demo with multiple GPUs
• History

API:

• Models
  • Model Methods
    • compile
    • fit
    • evaluate
    • predict
    • train_on_batch
    • test_on_batch
    • predict_on_batch
    • fit_generator
    • evaluate_generator
    • predict_generator
    • get_layer
  • CCPM
  • FNN
  • PNN
  • WDL
  • DeepFM
  • MLR
  • NFM
  • AFM
  • DCN
  • DCNMix
  • DIN
  • DIEN
  • DSIN
  • BST
  • xDeepFM
  • AutoInt
  • ONN
  • FGCNN
  • FiBiNET
  • FLEN
• Estimators
  • CCPM
  • FNN
  • PNN
  • WDL
  • DeepFM
  • NFM
  • AFM
  • DCN
  • xDeepFM
  • AutoInt
  • FiBiNET
• Layers
  • Core Layers
  • Interaction Layers
  • Activation Layers
  • Normalization Layers
  • Sequence Layers

Indices and tables

• Index
• Module Index
• Search Page