wehub-resource-sync
127 lines
5.9 KiB
Python
127 lines
5.9 KiB
Python
from __future__ import print_function, division
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import numpy as np
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import copy
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class Neuroevolution():
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""" Evolutionary optimization of Neural Networks.
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Parameters:
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-----------
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n_individuals: int
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The number of neural networks that are allowed in the population at a time.
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mutation_rate: float
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The probability that a weight will be mutated.
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model_builder: method
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A method which returns a user specified NeuralNetwork instance.
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"""
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def __init__(self, population_size, mutation_rate, model_builder):
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self.population_size = population_size
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self.mutation_rate = mutation_rate
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self.model_builder = model_builder
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def _build_model(self, id):
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""" Returns a new individual """
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model = self.model_builder(n_inputs=self.X.shape[1], n_outputs=self.y.shape[1])
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model.id = id
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model.fitness = 0
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model.accuracy = 0
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return model
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def _initialize_population(self):
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""" Initialization of the neural networks forming the population"""
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self.population = []
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for _ in range(self.population_size):
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model = self._build_model(id=np.random.randint(1000))
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self.population.append(model)
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def _mutate(self, individual, var=1):
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""" Add zero mean gaussian noise to the layer weights with probability mutation_rate """
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for layer in individual.layers:
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if hasattr(layer, 'W'):
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# Mutation of weight with probability self.mutation_rate
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mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=layer.W.shape)
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layer.W += np.random.normal(loc=0, scale=var, size=layer.W.shape) * mutation_mask
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mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=layer.w0.shape)
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layer.w0 += np.random.normal(loc=0, scale=var, size=layer.w0.shape) * mutation_mask
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return individual
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def _inherit_weights(self, child, parent):
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""" Copies the weights from parent to child """
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for i in range(len(child.layers)):
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if hasattr(child.layers[i], 'W'):
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# The child inherits both weights W and bias weights w0
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child.layers[i].W = parent.layers[i].W.copy()
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child.layers[i].w0 = parent.layers[i].w0.copy()
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def _crossover(self, parent1, parent2):
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""" Performs crossover between the neurons in parent1 and parent2 to form offspring """
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child1 = self._build_model(id=parent1.id+1)
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self._inherit_weights(child1, parent1)
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child2 = self._build_model(id=parent2.id+1)
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self._inherit_weights(child2, parent2)
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# Perform crossover
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for i in range(len(child1.layers)):
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if hasattr(child1.layers[i], 'W'):
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n_neurons = child1.layers[i].W.shape[1]
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# Perform crossover between the individuals' neuron weights
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cutoff = np.random.randint(0, n_neurons)
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child1.layers[i].W[:, cutoff:] = parent2.layers[i].W[:, cutoff:].copy()
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child1.layers[i].w0[:, cutoff:] = parent2.layers[i].w0[:, cutoff:].copy()
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child2.layers[i].W[:, cutoff:] = parent1.layers[i].W[:, cutoff:].copy()
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child2.layers[i].w0[:, cutoff:] = parent1.layers[i].w0[:, cutoff:].copy()
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return child1, child2
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def _calculate_fitness(self):
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""" Evaluate the NNs on the test set to get fitness scores """
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for individual in self.population:
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loss, acc = individual.test_on_batch(self.X, self.y)
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individual.fitness = 1 / (loss + 1e-8)
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individual.accuracy = acc
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def evolve(self, X, y, n_generations):
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""" Will evolve the population for n_generations based on dataset X and labels y"""
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self.X, self.y = X, y
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self._initialize_population()
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# The 40% highest fittest individuals will be selected for the next generation
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n_winners = int(self.population_size * 0.4)
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# The fittest 60% of the population will be selected as parents to form offspring
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n_parents = self.population_size - n_winners
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for epoch in range(n_generations):
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# Determine the fitness of the individuals in the population
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self._calculate_fitness()
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# Sort population by fitness
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sorted_i = np.argsort([model.fitness for model in self.population])[::-1]
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self.population = [self.population[i] for i in sorted_i]
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# Get the individual with the highest fitness
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fittest_individual = self.population[0]
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print ("[%d Best Individual - Fitness: %.5f, Accuracy: %.1f%%]" % (epoch,
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fittest_individual.fitness,
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float(100*fittest_individual.accuracy)))
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# The 'winners' are selected for the next generation
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next_population = [self.population[i] for i in range(n_winners)]
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total_fitness = np.sum([model.fitness for model in self.population])
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# The probability that a individual will be selected as a parent is proportionate to its fitness
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parent_probabilities = [model.fitness / total_fitness for model in self.population]
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# Select parents according to probabilities (without replacement to preserve diversity)
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parents = np.random.choice(self.population, size=n_parents, p=parent_probabilities, replace=False)
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for i in np.arange(0, len(parents), 2):
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# Perform crossover to produce offspring
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child1, child2 = self._crossover(parents[i], parents[i+1])
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# Save mutated offspring for next population
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next_population += [self._mutate(child1), self._mutate(child2)]
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self.population = next_population
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return fittest_individual
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