Numpy实现Neuroevolution（神经网络进化）

Posted 2021-12-23 AI浩

from __future__ import print_function, division
import numpy as np
import copy

class Neuroevolution():
    """ Evolutionary optimization of Neural Networks.

    Parameters:
    -----------
    n_individuals: int
        The number of neural networks that are allowed in the population at a time.
    mutation_rate: float
        The probability that a weight will be mutated.
    model_builder: method
        A method which returns a user specified NeuralNetwork instance. 
    """
    def __init__(self, population_size, mutation_rate, model_builder):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.model_builder = model_builder

    def _build_model(self, id):
        """ Returns a new individual """
        model = self.model_builder(n_inputs=self.X.shape[1], n_outputs=self.y.shape[1])
        model.id = id
        model.fitness = 0
        model.accuracy = 0
        
        return model

    def _initialize_population(self):
        """ Initialization of the neural networks forming the population"""
        self.population = []
        for _ in range(self.population_size):
            model = self._build_model(id=np.random.randint(1000))
            self.population.append(model)

    def _mutate(self, individual, var=1):
        """ Add zero mean gaussian noise to the layer weights with probability mutation_rate """
        for layer in individual.layers:
            if hasattr(layer, 'W'):
                # Mutation of weight with probability self.mutation_rate
                mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=layer.W.shape)
                layer.W += np.random.normal(loc=0, scale=var, size=layer.W.shape) * mutation_mask
                mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=layer.w0.shape)
                layer.w0 += np.random.normal(loc=0, scale=var, size=layer.w0.shape) * mutation_mask
        
        return individual

    def _inherit_weights(self, child, parent):
        """ Copies the weights from parent to child """
        for i in range(len(child.layers)):
            if hasattr(child.layers[i], 'W'):
                # The child inherits both weights W and bias weights w0
                child.layers[i].W = parent.layers[i].W.copy()
                child.layers[i].w0 = parent.layers[i].w0.copy()

    def _crossover(self, parent1, parent2):
        """ Performs crossover between the neurons in parent1 and parent2 to form offspring """
        child1 = self._build_model(id=parent1.id+1)
        self._inherit_weights(child1, parent1)
        child2 = self._build_model(id=parent2.id+1)
        self._inherit_weights(child2, parent2)

        # Perform crossover
        for i in range(len(child1.layers)):
            if hasattr(child1.layers[i], 'W'):
                n_neurons = child1.layers[i].W.shape[1]
                # Perform crossover between the individuals' neuron weights
                cutoff = np.random.randint(0, n_neurons)
                child1.layers[i].W[:, cutoff:] = parent2.layers[i].W[:, cutoff:].copy()
                child1.layers[i].w0[:, cutoff:] = parent2.layers[i].w0[:, cutoff:].copy()
                child2.layers[i].W[:, cutoff:] = parent1.layers[i].W[:, cutoff:].copy()
                child2.layers[i].w0[:, cutoff:] = parent1.layers[i].w0[:, cutoff:].copy()
        
        return child1, child2

    def _calculate_fitness(self):
        """ Evaluate the NNs on the test set to get fitness scores """
        for individual in self.population:
            loss, acc = individual.test_on_batch(self.X, self.y)
            individual.fitness = 1 / (loss + 1e-8)
            individual.accuracy = acc

    def evolve(self, X, y, n_generations):
        """ Will evolve the population for n_generations based on dataset X and labels y"""
        self.X, self.y = X, y

        self._initialize_population()

        # The 40% highest fittest individuals will be selected for the next generation
        n_winners = int(self.population_size * 0.4)
        # The fittest 60% of the population will be selected as parents to form offspring
        n_parents = self.population_size - n_winners

        for epoch in range(n_generations):
            # Determine the fitness of the individuals in the population
            self._calculate_fitness()

            # Sort population by fitness
            sorted_i = np.argsort([model.fitness for model in self.population])[::-1]
            self.population = [self.population[i] for i in sorted_i]

            # Get the individual with the highest fitness
            fittest_individual = self.population[0]
            print ("[%d Best Individual - Fitness: %.5f, Accuracy: %.1f%%]" % (epoch, 
                                                                        fittest_individual.fitness, 
                                                                        float(100*fittest_individual.accuracy)))
            # The 'winners' are selected for the next generation
            next_population = [self.population[i] for i in range(n_winners)]

            total_fitness = np.sum([model.fitness for model in self.population])
            # The probability that a individual will be selected as a parent is proportionate to its fitness
            parent_probabilities = [model.fitness / total_fitness for model in self.population]
            # Select parents according to probabilities (without replacement to preserve diversity)
            parents = np.random.choice(self.population, size=n_parents, p=parent_probabilities, replace=False)
            for i in np.arange(0, len(parents), 2):
                # Perform crossover to produce offspring
                child1, child2 = self._crossover(parents[i], parents[i+1])
                # Save mutated offspring for next population
                next_population += [self._mutate(child1), self._mutate(child2)]

            self.population = next_population

        return fittest_individual