如何用自动机器学习实现神经网络进化

for i in range(0,self.population_sz,2):

print(self.population[i]['nb_layers'],

self.population[i]['layers'][0]['nb_units'])

self.evaluate()

children = []

print('\nPopulation mean:{} max:{}'.format(

np.mean(self.evaluations), np.max(self.evaluations)))

n_elite = 2

sorted_pop = np.argsort(self.evaluations)[::-1]

elite = sorted_pop[:n_elite]

# print top@n_elite scores

# elites always included in the next population

self.elite = []

print('\nTop performers:')

for i,e in enumerate(elite):

self.elite.append((self.evaluations[e], self.population[e]))

print("{}-score:{}".format( str(i), self.evaluations[e]))

children.append(self.population[e])

# tournament probabilities:

# first p

# second p*(1-p)

# third p*((1-p)^2)

# etc...

p = 0.85 # winner probability

tournament_size = 3

probs = [p*((1-p)**i) for i in range(tournament_size-1)]

# a little trick to certify that probs is adding up to 1.0

probs.append(1-np.sum(probs))

while len(children) < self.population_sz:

pop = range(len(self.population))

sel_k = random.sample(pop, k=tournament_size)

fitness_k = list(np.array(self.evaluations)[sel_k])

selected = zip(sel_k, fitness_k)

rank = sorted(selected, key=itemgetter(1), reverse=True)

pick = np.random.choice(tournament_size, size=1, p=probs)[0]

best = rank[pick][0]

model = self.mutate_fn(self.population[best])

children.append(model)

self.population = children

# if we want to do a completely completely random search per epoch

# self.population = [randomize_network(bounded=False) for i in range(self.population_sz) ]

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