Machine learning has proven to be an indispensable tool in the selection of interesting events in high energy physics. Such technologies will become increasingly important as detector upgrades are introduced and data rates increase by orders of magnitude. NNDrone is a toolkit to enable the creation of a drone classifier from any machine learning classifier, such that different classifiers may be standardised into a single form and executed in parallel. In the package, we demonstrate the capability of the drone neural network to learn the required properties of the input neural network without the use of any training data, only using appropriate questioning of the input neural network - https://arxiv.org/abs/1712.09114.

To demonstrate the toolkit:

1. We create simulated HEP data from the RapidSim package (https://arxiv.org/abs/1612.07489)
2. We train a neural network using SciKit-Learn
3. We train a drone to learn the features
4. We export this to the equivalent coding in the production environment using JSON

• NumPy
• SkLearn and or Keras

• SciPy
• MatPlotLib

With your MLP from SkLearn or Keras created, making a drone is as simple as

  from nndrone.converters import BasicConverter
  from nndrone.models import BaseModel as Model
  model = Model(len(data[0]), 1)
  converter = BasicConverter(num_epochs=num_epochs, batch_size=batchSize, learning_rate=alpha, threshold=threshold)
  converter.convert_model(model, classifier, data)

where data is a 2D NumPy array of width num. features and arbitrary length, and classifier is the original network to be converted.