0. 前言

我們已經(jīng)學習了進化策略 (Evolutionary Strategies, ES) 的基本原理，并且嘗試使用 ES 解決了函數(shù)逼近問題。函數(shù)逼近是一個很好的基準問題，但為了充分展示 ES 的作用，本節(jié)中，我們將重新思考 EvoLisa 問題，采用 ES 作為解決策略，以將 ES 和常規(guī)遺傳算法進行對比。

1. 使用進化策略實現(xiàn) EvoLisa

接下來，使用進化策略 (Evolutionary Strategies, ES) 通過復現(xiàn) EvoLisa 項目重建《蒙娜麗莎》圖像。

import random
import numpy as npfrom deap import algorithms
from deap import base
from deap import creator
from deap import toolsimport os
import cv2
import urllib.request
import matplotlib.pyplot as plt
from IPython.display import clear_outputdef load_target_image(image_url, color=True, size=None):image_path = "target_image"    urllib.request.urlretrieve(image_url,image_path)if color:target = cv2.imread(image_path, cv2.IMREAD_COLOR)# Switch from bgr to rgbtarget = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)else:target = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)if size:# Only resizes image if it is needed!target = cv2.resize(src=target, dsize=size, interpolation=cv2.INTER_AREA)return targetdef show_image(img_arr):    plt.figure(figsize=(10,10))plt.axis("off")plt.imshow(img_arr/255)plt.show()def show_results(history, img_arr, org):plt.figure(figsize=(10,10))plt.tight_layout()plt.subplot(221)plt.axis("off")plt.imshow(img_arr/255)plt.title('best of generation')plt.subplot(222)plt.axis("off")plt.imshow(org/255)plt.title('target image')plt.subplot(212)lh = len(history)plt.xlim([lh-50, lh])plt.plot(history)plt.title('min fitness by generation') plt.show()polygons = 255 #@param {type:"slider", min:10, max:1000, step:1}
size = 32 #@param {type:"slider", min:16, max:1000, step:2}
target_image = "Mona Lisa" #@param ["Mona Lisa", "Stop Sign", "Landscape", "Celebrity", "Art", "Abstract"]
report_every_gen = 10 #@param {type:"slider", min:1, max:100, step:1}
number_generations = 10000 #@param {type:"slider", min:100, max:10000, step:10}POLYGONS = polygons
SIZE = (size, size)target_urls = { "Mona Lisa" : 'https://upload.wikimedia.org/wikipedia/commons/b/b7/Mona_Lisa_face_800x800px.jpg',"Stop Sign" : 'https://images.uline.com/is/image//content/dam/images/H/H2500/H-2381.jpg',"Landscape" : 'https://www.adorama.com/alc/wp-content/uploads/2018/11/landscape-photography-tips-yosemite-valley-feature.jpg',"Celebrity" : 'https://s.abcnews.com/images/Entertainment/WireAP_91d6741d1954459f9993bd7a2f62b6bb_16x9_992.jpg',"Art" : "http://www.indianruminations.com/wp-content/uploads/what-is-modern-art-definition-2.jpg","Abstract" : "https://scx2.b-cdn.net/gfx/news/2020/abstractart.jpg"}target_image_url = target_urls[target_image]
target = load_target_image(target_image_url, size=SIZE)
show_image(target)
print(target.shape)#polygon genes
GENE_LENGTH = 10
NUM_GENES = POLYGONS * GENE_LENGTH#create a sample invidiual
individual = np.random.uniform(0,1,NUM_GENES)
print(individual)
# [0.62249533 0.44090963 0.14777921 ... 0.57283261 0.9325435  0.25907929]def extract_genes(genes, length): for i in range(0, len(genes), length): yield genes[i:i + length]def render_individual(individual):if isinstance(individual,list):individual = np.array(individual)canvas = np.zeros(SIZE+(3,))radius_avg = (SIZE[0] + SIZE[1]) / 2 / 6genes = extract_genes(individual, GENE_LENGTH)for gene in genes:try:overlay = canvas.copy()# alternative drawing methods circle or rectangle# circle brush uses a GENE_LENGTH of 7# center = (0, 1) [2]# radius = (2) [3]# color = (3,4,5) [6]# alpha = (6) [7]#cv2.circle(#    overlay,#    center=(int(gene[1] * SIZE[1]), int(gene[0] * SIZE[0])),#    radius=int(gene[2] * radius_avg),#    color=color,#    thickness=-1,#)# rectangle brush uses GENE_LENGTH = 8# top left = (0, 1) [2]# btm right = (2, 3) [4]# color = (4, 5, 6) [7]# alpha = (7) [8]#cv2.rectangle(overlay, (x1, y1), (x2, y2), color, -1)    # polyline brush uses GENE_LENGTH = 10# pts = (0, 1), (2, 3), (4, 5) [6]      # color = (6, 7, 8) [9]# alpha = (9) [10]x1 = int(gene[0] * SIZE[0])x2 = int(gene[2] * SIZE[0])x3 = int(gene[4] * SIZE[0])y1 = int(gene[1] * SIZE[1])y2 = int(gene[3] * SIZE[1])y3 = int(gene[5] * SIZE[1])color = (gene[6:-1] * 255).astype(int).tolist() pts = np.array([[x1,y1],[x2,y2],[x3,y3]], np.int32)  pts = pts.reshape((-1, 1, 2))pts = np.array([[x1,y1],[x2,y2],[x3,y3]])cv2.fillPoly(overlay, [pts], color)alpha = gene[-1]canvas = cv2.addWeighted(overlay, alpha, canvas, 1 - alpha, 0)  except:passreturn canvasrender = render_individual(individual)
show_image(render)from skimage.metrics import structural_similarity as ss
#@title Fitness Function
def fitness_mse(render):"""Calculates Mean Square Error Fitness for a render"""error = (np.square(render - target)).mean(axis=None)return errordef fitness_ss(render):"""Calculated Structural Similiarity Fitness"""index = ss(render, target, multichannel=True)return 1-indexprint(fitness_mse(render))IND_SIZE = NUM_GENES
MIN_VALUE = -1
MAX_VALUE = 1
MIN_STRATEGY = 0.5
MAX_STRATEGY = 5CXPB = .6
MUTPB = .3creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, typecode="d", fitness=creator.FitnessMin, strategy=None)
creator.create("Strategy", list, typecode="d")def generateES(icls, scls, size, imin, imax, smin, smax):  ind = icls(random.uniform(imin, imax) for _ in range(size))  ind.strategy = scls(random.uniform(smin, smax) for _ in range(size))  return inddef checkStrategy(minstrategy):def decorator(func):def wrappper(*args, **kargs):children = func(*args, **kargs)for child in children:for i, s in enumerate(child.strategy):if s < minstrategy:child.strategy[i] = minstrategyreturn childrenreturn wrappper
return decoratordef uniform(low, up, size=None):try:return [random.uniform(a, b) for a, b in zip(low, up)]except TypeError:return [random.uniform(a, b) for a, b in zip([low] * size, [up] * size)]def clamp(low, up, n):return max(low, min(n, up))def custom_blend(ind1, ind2, alpha):    for i, (x1, s1, x2, s2) in enumerate(zip(ind1, ind1.strategy,ind2, ind2.strategy)):# Blend the valuesgamma = (1. + 2. * alpha) * random.random() - alphaind1[i] = clamp(0.0, 1.0, (1. - gamma) * x1 + gamma * x2)ind2[i] = clamp(0.0, 1.0, gamma * x1 + (1. - gamma) * x2)# Blend the strategiesgamma = (1. + 2. * alpha) * random.random() - alphaind1.strategy[i] = (1. - gamma) * s1 + gamma * s2ind2.strategy[i] = gamma * s1 + (1. - gamma) * s2return ind1, ind2toolbox = base.Toolbox()
toolbox.register("individual", generateES, creator.Individual, creator.Strategy,IND_SIZE, MIN_VALUE, MAX_VALUE, MIN_STRATEGY, MAX_STRATEGY)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", custom_blend, alpha=0.5)
toolbox.register("mutate", tools.mutESLogNormal, c=1.0, indpb=0.06)
toolbox.register("select", tools.selTournament, tournsize=5)toolbox.decorate("mate", checkStrategy(MIN_STRATEGY))
toolbox.decorate("mutate", checkStrategy(MIN_STRATEGY))def evaluate(individual):render = render_individual(individual)print('.', end='')
return fitness_mse(render),  #using MSE for fitness#toolbox.register("mutate", tools.mutGaussian, mu=0.0, sigma=.1, indpb=.25)
toolbox.register("evaluate", evaluate)NGEN = number_generations
RGEN = report_every_gen
CXPB = .6
MUTPB = .3
MU, LAMBDA = 100, 250
pop = toolbox.population(n=MU)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max) best = None
history = []for g in range(NGEN):pop, logbook = algorithms.eaMuCommaLambda(pop, toolbox, mu=MU, lambda_=LAMBDA, cxpb=CXPB, mutpb=MUTPB, ngen=RGEN, stats=stats, halloffame=hof, verbose=False)best = hof[0]#pop, logbook = algorithms.eaSimple(pop, toolbox, #         cxpb=CXPB, mutpb=MUTPB, ngen=100, stats=stats, halloffame=hof, verbose=False)#best = hof[0] clear_output()  render = render_individual(best) history.extend([clamp(0.0, 5000.0, l["min"]) for l in logbook])show_results(history, render, target)  print(f"Gen ({(g+1)*RGEN}) : best fitness = {fitness_mse(render)}")

2. 運行結果

下圖顯示了代碼的運行結果，作為對比，圖中還顯示了使用經(jīng)典遺傳算法生成的結果。

相關鏈接

遺傳算法與深度學習實戰(zhàn)（1）——進化深度學習
遺傳算法與深度學習實戰(zhàn)（4）——遺傳算法（Genetic Algorithm）詳解與實現(xiàn)
遺傳算法與深度學習實戰(zhàn)（5）——遺傳算法中常用遺傳算子
遺傳算法與深度學習實戰(zhàn)（6）——遺傳算法框架DEAP
遺傳算法與深度學習實戰(zhàn)（7）——DEAP框架初體驗
遺傳算法與深度學習實戰(zhàn)（10）——使用遺傳算法重建圖像
遺傳算法與深度學習實戰(zhàn)（14）——進化策略詳解與實現(xiàn)