This is a python implementation of Memetic algorithm with crossover to search architecture of neural network (MACSANN). The objective of the algorithm is to find an architecture of an ANN that could solve a given problem.
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Configure your config file, you can see an example in ("config-example.ini")
- Here you set the hyperparameters of your experiment:
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Create the population
import macsann population = macsann.population('config-example.ini') -
Set train and validation data
#Train data population.train_input = boston['data'][:300] population.train_output = boston['target'][:300] #Validation data population.eval_input = boston['data'][300:] population.eval_output = boston['target'][300:] -
Run your experiment and save the best population
best_pop = population.run() with open('best_pop.macsann','wb') as f: pickle.dump(best_pop, f) -
See the complete example in 'boston-example.py'