deatf 0.2

Distributed Evolutionary Algorithms in TensorFlow (DEATF) is a framework where networks generated with TensorFlow are evolved via DEAP.

DEATF

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Distributed Evolutionary Algorithms in TensorFlow (DEATF) is a framework where networks generated with TensorFlow [1] are evolved via DEAP [2]. DEATF is a framework directly based in EvoFlow [3] framework created by Unai Garciarena.

Installation

DEATF has available an easy installation with pip.

pip install deap

Requirements

TensorFlow DEAP NumPy Tensorflow-database ScikitLearn Pandas

Example

The easiest example of this library (taken from simple.py in the examples folder), where every used parameter is predifined is the following one:

import numpy as np

from deatf.auxiliary_functions import load_fashion
from deatf.network import MLPDescriptor
from deatf.evolution import Evolving

from sklearn.preprocessing import OneHotEncoder

x_train, y_train, x_test, y_test, x_val, y_val = load_fashion()

OHEnc = OneHotEncoder()

y_train = OHEnc.fit_transform(np.reshape(y_train, (-1, 1))).toarray()
y_test = OHEnc.fit_transform(np.reshape(y_test, (-1, 1))).toarray()
y_val = OHEnc.fit_transform(np.reshape(y_val, (-1, 1))).toarray()

e = Evolving(evaluation="XEntropy", desc_list=[MLPDescriptor], compl=False,
         x_trains=[x_train], y_trains=[y_train], x_tests=[x_val], y_tests=[y_val], 
         n_inputs=[[28, 28]], n_outputs=[[10]], batch_size=150, iters=10, 
         population=15, generations=10, max_num_layers=10, max_num_neurons=20,
         seed=0, dropout=False, batch_norm=False, evol_alg='mu_plus_lambda',
         evol_kwargs={'mu':10, 'lambda_':15, 'cxpb':0., "mutpb": 1.},
         sel = 'best')

a = e.evolve()

References

[1] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.

[2] Fortin, F. A., Rainville, F. M. D., Gardner, M. A., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(Jul), 2171-2175.

[3] Garciarena, U., Santana, R., & Mendiburu, A. (2018, July). Evolved GANs for generating Pareto set approximations. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 434-441). ACM.