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Python start guide for data science bowl

The following set of scripts should be a good way to get started with Convolution Neural Networks. It uses a GraphLab-Create's deep learning which is based on CXXNet.

  • Setup time: ~2 mins
  • Train time: ~20 mins on a GPU (it could take much longer on a CPU)
  • Validation score: 0.98
  • Leaderboard score: 0.97

Update: I haven't had much time to improve this score but francoisluus has improved the score to 0.77 using some interesting ideas!

Solution

Here is a quick summary of the submission:

  • Load images into an SFrame (scalable dataframe).
  • Use Pillow to augment the data with rotations with angle 90, 180, and 270.
  • Setup a simple deep learning architecture (based on antinucleon)
  • Create a "fair" train, validaiton split to make sure the classes are balanced.
  • Train a deep learning model.
  • Evaluate the multi-class log loss score.
  • Save the predictions in Kaggle's format into a submission file called "submission.csv".

Install

CPU instructions

pip install -r requirements.pip

GPU instructions

pip install -r requirements-gpu.pip

Data

Let us assume that you have the data downloaded into two folders called train and test. You can do that as follows:

wget https://www.kaggle.com/c/datasciencebowl/download/train.zip
wget https://www.kaggle.com/c/datasciencebowl/download/test.zip
unzip train.zip
unzip test.zip

Make submission

Now run the following script. The script will create a submission file. It could take around 1 hour depending on how many interations you perform. The network can train at around 5k images a second.

python make_submission.py