neuro-evolution

Neuro-evelution for Neural Network hyper parameter tuning

This repository is wrapper and slightly modified implementation for the code provided by Matt Harvey repository: https://github.com/harvitronix/neural-network-genetic-algorithm

installation: pip install git+https://github.com/subpath/neuro-evolution.git

Example of usage:

1. Create dictionary with parameters

from neuro_evolution import evolution

params = { "epochs": [10, 20, 35],
           "batch_size": [10, 20, 40],
           "n_layers": [1, 2, 3, 4],
           "n_neurons": [20, 40, 60],
           "dropout": [0.1, 0.2, 0.5],
           "optimizers": ["nadam", "adam"],
           "activations": ["relu", "sigmoid"], "last_layer_activations": ["sigmoid"],
           "losses": ["binary_crossentropy"],
           "metrics": ["accuracy"] }

# x_train, y_train, x_test, y_test - prepared data

search = evolution.NeuroEvolution(generations = 10, population = 10, params=params)

search.evolve(x_train, y_train, x_test, y_test)

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100%|██████████| 10/10 [02:42<00:00, 25.29s/it]

"best accuracy: 0.79,
best params: {'epochs': 35, 'batch_size': 40, 'n_layers': 2, 'n_neurons': 20, 'dropout': 0.1, 'optimizers': 'nadam', 'activations': 'relu', 'last_layer_activations': 'sigmoid', 'losses': 'binary_crossentropy', 'metrics': 'accuracy'}"

or you can call it with

search.best_params