/cnn-rnn

kaggle egg spectrograms cnn-rnn

Primary LanguagePythonApache License 2.0Apache-2.0

cnn-rcnn

kaggle eeg

Description

Seizure Prediction

  • Competition page at Kaggle
  • This is a proof-of-concept for applying deep learning techniques to EEG data converted into spectrograms.

Usage

These steps take about 4 hours on a system with 4 processors, a single GPU and a spinning hard-disk. Tested only on Ubuntu.

  1. Download and install neon 1.6.0

    git clone https://github.com/NervanaSystems/neon.git
cd neon
git checkout v1.6.0
make
source .venv/bin/activate

  2. Verify neon installation

    Make sure that this command does not result in any errors:

    ./examples/cifar10_msra.py -e1

  3. Install prerequisites

    pip install scipy sklearn scikits.audiolab

  4. Download the data files from Kaggle:

    a. Save all files to a directory (referred to as /path/to/data below) and unzip the .zip files. b. Run the conversion script to move data around and exclude bad data.

  5. Clone this repository

    git clone https://github.com/bigsnarfdude/cnn-rcnn.git
cd cnn-rcnn

  6. Train models and generate predictions

    ./run.sh /path/to/data /path/to/output 2>&1 | tee run.log

    where /path/to/data must contain the data subdirectories (train_1, train_2 etc.) as well as sample_submission.csv and /path/to/output is a new directory that will be created to store intermediate output files.

  7. Evaluate predictions

    Submit subm.csv to Kaggle

Notes

  • The model needs 4GB of device memory.
  • The first run takes longer due to conversion of .mat files into .wav files.
  • Conversion of data to spectrograms is performed on the fly by neon.

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