/GenderClassifierNN

Classify names into male/female using Neural Nets

Primary LanguagePythonMIT LicenseMIT

GenderClassifierNN

A GenderClassifier built with python, served using FastAPI

Installation

  • Clone the project
  • For installing dependencies, run pip install -r requirements.txt
  • set path variable for python using export PYTHONPATH=$PWD (linux)
  • Now, we need to train the model, for doing so run python3 classifier/train.py
  • For starting the uvicorn server, run uvicorn web.main:app
  • If we go to the localhost:8000, a frontend is present to use the classifier

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Usage

  • Using frontend :
    • A basic frontend for typing in names and getting classifications
  • Using REST API

    • for single name classification classify?name=<name>

      returns

      {
  'name' : name being classified 
  'male'   :  Confidence/Uncertainty of being male
  'female' :  Confidence/Uncertainty of being female 
}

    • for multiple name classification bulk_classify?names=<name1>&names=<name2>

      [{
  'name' : name being classified 
  'male'   :  Confidence/Uncertainty of being male
  'female' :  Confidence/Uncertainty of being female 
}]

Model

  • The names are one hot encoded and fed to the neural net

  • The model is for now is a bidirectional stacked LSTM followed by a dense layer

    Operation-of-two-stacked-bidirectional-LSTM-RNN-model

  • The output is passed through a sigmoid function such that outputs a confidence for male (Zero begin female, One being male)

  • The test accuracy of the model currently is roughly 87% where accuracy = (tn + tp)/ total for a confusion matrix

TODO :

  • Work on automating best config for training the nn for hardware
  • Try out other approaches with more complex archetecture
  • Host the website