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Codes for Cdiscount kaggle competition of NullPointerException team

This document is for kaggle Cdiscount competition. All codes are written by Python3.5.

Directory & File structure

.
├── code                            # Code files
|   ├── model                       # model package
|   |   ├── loss.py
|   |   ├── lr_schedule.py
|   |   └── xception.py
|   ├── utils                       # utils package
|   |   ├── callbacks.py
|   |   ├── iterator.py
|   |   ├── sysmonitor.py           # (optional) use for monitoring CPU & GPU status
|   |   └── utils.py                # default settings
|   ├── feature_extractor.py
|   ├── fine_tuning.py
|   ├── predict_with_snapshot.py
|   ├── prediction.py
|   ├── preprocessing.py
|   ├── split_validation.py
|   ├── train_with_branch.py
|   └── training.py
├── data                            # Data files
|   ├── input                       # Original data files
|   |   ├── category_names.csv
|   |   ├── sample_submission.csv
|   |   ├── test.bson
|   |   └── train.bson
|   ├── logs
|   |   └── ...
|   ├── results
|   |   └── ...
|   ├── utils
|   |   └── ...
|   └── weights
|       └── ...
├── source_code                     # Unused files, only for reference
|   └── ...
└── ReadMe.md

Instruction

  1. Put your input files into /data/input folder.
  2. Preprocess the dataset using preprocessing.py
  3. Split validation set from train.bson using split_validation.py
  4. Train the model using training.py
  5. Make prediction and submission using prediction.py or prediction_with_snapshot.py

Team Members

LZM

ZJY

MTJ

Requirements

  1. Tensorflow 1.3.0
  2. Keras 2.0.9 (2.0.9 support multi-gpu)
  3. Pymongo 3.5.1 (use for import bson, don't really import pymongo)
  4. Pandas
  5. Numpy
  6. Matplotlib
  7. h5py

Citations

All codes from internet are listed in source_code folder

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