distributed version of product-nets
Clone or download
Latest commit 99d214f Oct 8, 2018
Type Name Latest commit message Commit time
Failed to load latest commit information.
pnn fix memory leak Oct 9, 2018
.gitignore update Jan 19, 2018
README.md readme Jul 4, 2018


Note: Any problems, you can contact me at kevinqu@apex.sjtu.edu.cn, or kevinqu16@gmail.com. Through email, you will get my rapid response.

This repository provides the experiments code of the paper Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data. This paper extends the conference paper, and is accepted by TOIS.

In general, the journal paper extends the conference paper in 3 aspects:

  • We analyze the coupled gradient issue of FM-like models, and propose kernel product to solve it. We apply 2 types of kernel product in FM, (Kernel FM (KFM) and Network in FM (NIFM)), to verify this issue.
  • We analyze the insensitive gradient issue of DNN-based models, and propose to use interactive feature extractors to tackle this issue. Using FM, KFM, and NIFM as feature extractors, we propose Inner PNN (IPNN), Kernel PNN (KPNN), and Product-network In Network (PIN).
  • We study practical issue in training and generalization. We conduct more offline experiments and an online A/B test.

The demo of the conference paper implements IPNN, KPNN and some baselines, and it is easier to read/run. This repository implements all the proposed models as well as baseline models in tensorflow, with large-scale data access, multi-gpu support, and distributed training support.

How to run

In order to accelerate large-scale data access, we use hdf format to store the processed data. The datasets and APIs are at Ads-RecSys-Datasets. The first step you should download the APIs and datasets at path/to/data. You can check data access through:

#!/usr/bin/env python
import sys
from __future__ import print_function
from datasets import iPinYou

data = iPinYou()
train_gen = data.batch_generator('train', batch_size=1000)

for X, y in train_gen:
    print(X.shape, y.shape)

The second step you should download this repository and configure data_path in __init__.py:

config['data_path'] = 'path/to/data'

This repository contains 6 .py files, they are:

  • __init__.py: store configuration.
  • criteo_challenge.py: our solution to the Criteo Challenge.
  • kill.py: find and kill the threads when using distributed training.
  • print_hook.py: redirect stdout to logfile.
  • tf_main.py: training file.
  • tf_models.py: all the models, including LR, FM, FFM, KFM, NIFM, FNN, CCPM, DeepFM, IPNN, KPNN, and PIN.

You can run tf_main.py in different settings, for example:

Local training, 1 GPU:

CUDA_VISIBLE_DEVICES="0" python tf_main.py --distributed=False --num_gpus=1 --dataset=criteo --model=fnn --batch_size=2000 --optimizer=adam --learning_rate=1e-3 --embed_size=20 --nn_layers="[[\"full\", 400], [\"act\", \"relu\"], [\"full\", 1]]" --num_rounds=1

Local training, 2 GPUs:

CUDA_VISIBLE_DEVICES="0,1" python tf_main.py --distributed=False --num_gpus=2 --dataset=avazu --model=fm --batch_size=2000 --optimizer=adagrad --learning_rate=0.1 --embed_size=40 --num_rounds=3

Distributed training:

# ps host
python tf_main.py --distributed=True --ps_hosts='ps_host0' --worker_hosts='worker_host0,worker_host1' --job_name=ps --task_index=0 --tag=XXX

# worker host 0
python tf_main.py --distributed=True --ps_hosts='ps_host0' --worker_hosts='worker_host0,worker_host1' --job_name=worker --task_index=0 --worker_num_gpus=2,2 --tag=XXX

# worker host 1
python tf_main.py --distributed=True --ps_hosts='ps_host0' --worker_hosts='worker_host0,worker_host1' --job_name=worker --task_index=1 --worker_num_gpus=2,2 --tag=XXX

Criteo Challenge

criteo_challenge.py contains our solution to the contest. In this contest, libFFM (master branch) was the winning solution with log loss = 0.44506/0.44520 on the private/public leaderboard, and achieves 0.44493/0.44508 after calibration. We train KFM with the same training files as libFFM on one 1080Ti. KFM achieves 0.44484/0.44492 on the private/public leaderboard, and achieves 0.44484/0.44491 after calibration.