v0.1.2

About

Relying on TensorFlow and Keras as backends, pidgan is a Python package designed to simplify the implementation and training of GAN-based models intended for High Energy Physics (HEP) applications.

Available modules

• algorithms
  • BceGAN
  • BceGAN_ALP
  • BceGAN_GP
  • CramerGAN
  • GAN - ✨
  • LSGAN
  • WGAN
  • WGAN_ALP
  • WGAN_GP
• callbacks
  • schedulers
    • LearnRateBaseScheduler
    • LearnRateCosineDecay
    • LearnRateExpDecay
    • LearnRateInvTimeDecay
    • LearnRatePiecewiseConstDecay
    • LearnRatePolynomialDecay
• metrics
  • Accuracy
  • BaseMetric
  • BinaryCrossentropy
  • JSDivergence
  • KLDivergence
  • MeanAbsoluteError
  • MeanSquaredError
  • RootMeanSquaredError
  • WassertsteinDistance
• optimization
  • callbacks
    • HopaasPruner
  • scores
    • BaseScore
    • EMDistance
    • KSDistance
• players
  • classifiers
    • AuxClassifier - ✨
    • AuxMultiClassifier - ✨
    • Classifier - ✨
    • MultiClassifier - ✨
    • ResClassifier - ✨
    • ResMultiClassifier - ✨
  • discriminators
    • AuxDiscriminator - ✨
    • Discriminator - ✨
    • ResDiscriminator - ✨
  • generators
    • Generator - ✨
    • ResGenerator - ✨
• utils
  • checks
    • checkMetrics
    • checkOptimizer
  • preprocessing
    • invertColumnTransformer
  • reports
    • HPSingleton
    • getSummaryHTML

✨ New features

• The Generator player is implemented via a neural network using the TensorFlow's sequential model. To prevent the vanishing gradient problem even when playing with deep models, we enabled the use of skip connections. The ResGenerator player allows to use skip connections thanks to the TensorFlow's functional API.
• The Discriminator player is implemented via a neural network using the TensorFlow's sequential model. To prevent the vanishing gradient problem even when playing with deep models, we enabled the use of skip connections. The ResDiscriminator and AuxDiscriminator players allow to use skip connections thanks to the TensorFlow's functional API.
• The Classifier and MultiClassifier players are implemented via neural networks using the TensorFlow's sequential model. To prevent the vanishing gradient problem even when playing with deep models, we enabled the use of skip connections. The ResClassifier, AuxClassifier, ResMultiClassifier and AuxMultiClassifier players allow to use skip connections thanks to the TensorFlow's functional API.