烟台建网站公司哪家好,南通网站推广公司哪家好,读书网站建设策划书摘要,网站建设属于广告费么遗传算法与深度学习实战——使用进化策略实现EvoLisa 0. 前言1. 使用进化策略实现 EvoLisa2. 运行结果相关链接 0. 前言
我们已经学习了进化策略 (Evolutionary Strategies, ES) 的基本原理#xff0c;并且尝试使用 ES 解决了函数逼近问题。函数逼近是一个很好的基准问题并且尝试使用 ES 解决了函数逼近问题。函数逼近是一个很好的基准问题但为了充分展示 ES 的作用本节中我们将重新思考 EvoLisa 问题采用 ES 作为解决策略以将 ES 和常规遗传算法进行对比。
1. 使用进化策略实现 EvoLisa
接下来使用进化策略 (Evolutionary Strategies, ES) 通过复现 EvoLisa 项目重建《蒙娜丽莎》图像。
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, colorTrue, sizeNone):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(srctarget, dsizesize, interpolationcv2.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, sizeSIZE)
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])),# radiusint(gene[2] * radius_avg),# colorcolor,# 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 rendererror (np.square(render - target)).mean(axisNone)return errordef fitness_ss(render):Calculated Structural Similiarity Fitnessindex ss(render, target, multichannelTrue)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, typecoded, fitnesscreator.FitnessMin, strategyNone)
creator.create(Strategy, list, typecoded)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, sizeNone):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, alpha0.5)
toolbox.register(mutate, tools.mutESLogNormal, c1.0, indpb0.06)
toolbox.register(select, tools.selTournament, tournsize5)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, mu0.0, 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(nMU)
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, muMU, lambda_LAMBDA, cxpbCXPB, mutpbMUTPB, ngenRGEN, statsstats, halloffamehof, verboseFalse)best hof[0]#pop, logbook algorithms.eaSimple(pop, toolbox, # cxpbCXPB, mutpbMUTPB, ngen100, statsstats, halloffamehof, verboseFalse)#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(fGen ({(g1)*RGEN}) : best fitness {fitness_mse(render)})2. 运行结果
下图显示了代码的运行结果作为对比图中还显示了使用经典遗传算法生成的结果。 相关链接
遗传算法与深度学习实战1——进化深度学习 遗传算法与深度学习实战4——遗传算法Genetic Algorithm详解与实现 遗传算法与深度学习实战5——遗传算法中常用遗传算子 遗传算法与深度学习实战6——遗传算法框架DEAP 遗传算法与深度学习实战7——DEAP框架初体验 遗传算法与深度学习实战10——使用遗传算法重建图像 遗传算法与深度学习实战14——进化策略详解与实现
