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logoNeuvol

Genetic algorithm to find the best neural network architecture with Keras

Neuvol is a genetic algorithm API for neural networks based on Keras. The main idea is to work with data only, without direct architecture construction (but it is still possible).

Grow your own neural network!
  • Data in -> Neural Network Architecture out
  • Set your assumptions about architecture, it will be taken into account! (not yet)
  • A large number of allowed layers types

Features:

  • Supported data types: texts, images
  • CNN, Dense, LSTM, etc layers are available
  • Dropout and reshape sub-layers (Flattern) are available too

How-to-use

Short example of usage pipeline.

  • IMDB data:

    from keras.datasets import imdb

import neuvol


def main():
    (x_train, y_train), (x_test, y_test) = imdb.load_data(
        path="imdb.npz",
        num_words=30000,
        skip_top=0,
        maxlen=100,
        seed=113,
        start_char=1,
        oov_char=2,
        index_from=3)

    evaluator = neuvol.Evaluator(x_train, y_train, kfold_number=5)
    mutator = neuvol.Mutator()

    evaluator.create_tokens = False
    evaluator.fitness_measure = 'f1'
    options = {'classes': 2, 'shape': (100,), 'depth': 4}

    wop = neuvol.evolution.Evolution(
                                    stages=10,
                                    population_size=10,
                                    evaluator=evaluator,
                                    mutator=mutator,
                                    data_type='text',
                                    task_type='classification',
                                    active_distribution=True,
                                    freeze=None,
                                    **options)
    wop.cultivate()

    for individ in wop.population_raw_individ:
        print('Architecture: \n')
        print(individ.schema)
        print('\nScore: ', individ.result)


if __name__ == "__main__":
    main()

  • Cifar10 data:

    from keras.datasets import cifar10

import neuvol


def main():
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()

    evaluator = neuvol.Evaluator(x_train, y_train, kfold_number=5)
    mutator = neuvol.Mutator()

    evaluator.create_tokens = False
    evaluator.fitness_measure = 'AUC'
    options = {'classes': 10, 'shape': (32, 32, 3,), 'depth': 4}

    wop = neuvol.evolution.Evolution(
                                    stages=10,
                                    population_size=10,
                                    evaluator=evaluator,
                                    mutator=mutator,
                                    data_type='image',
                                    task_type='classification',
                                    active_distribution=False,
                                    freeze=None,
                                    **options)
    wop.cultivate()

    for individ in wop.population_raw_individ:
        print('Architecture: \n')
        print(individ.schema)
        print('\nScore: ', individ.result)


if __name__ == "__main__":
    main()

    Important note: you should set shape of data in option dictionary.

    Also, you can use GPU for calculation. In order to do that add:

    evaluator.device = 'gpu'

TODO

  • Architectures distribution generation
  • Images support
  • Regression models
  • Generative networks (??? almost impossible)
  • More available layers
  • More options (assumptions)
  • Logo
  • Visualization