在线服务。比如mock、fakename、randomuser和randomapi 各类编程语言的库 这篇文章会介绍Python的四个用于产生fake data的module lipsum- is a simple Lorem Ipsum generator library which can be used in your Python applications radar- Random date generation ...
Through use of the .unique property on the generator, you can guarantee that any generated values are unique for this specific instance.from faker import Faker fake = Faker() names = [fake.unique.first_name() for i in range(500)] assert len(set(names)) == len(names)...
faker - A Python package that generates fake data. fake2db - Fake database generator. genuine-fake - Genuine Fake means an imitation of a (usually) valuable object that is so good that it is, to all intents and purposes, identical. mimesis - A Python library that helps you generate fak...
fromfakerimportFaker#导入faker模块中的Faker方法fake = Faker()#初始化name = fake.name()#调用fake的方法address =fake.address()print(name)print(address)print(fake.text()) 输出: Ms. Michelle Phillips PSC7472, Box 2953APO AE48826 主要是导入Factory和Generator类,初始化时,实际调用的是Factory.create(...
fromfakerimportFaker fake=Faker()# first, import a similar Provider or use the default onefromfaker.providersimportBaseProvider# create new provider classclassMyProvider(BaseProvider):deffoo(self):return'bar'# then add new provider to faker instancefake.add_provider(MyProvider)# now you can use...
fake = Factory().create('zh_CN') # 产生随机手机号 print(fake.phone_number()) # 产生随机姓名 print(fake.name()) # 产生随机地址 print(fake.address()) # 随机产生国家名 print(fake.country()) # 随机产生国家代码 print(fake.country_code()) ...
生成md5print(fake.md5(raw_output=False))#随机生成女性名字print(fake.name_female())#男性名字print(fake.name_male())#随机生成名字print(fake.name())#生成基本信息print(fake.profile(fields=None, sex=None))print(fake.simple_profile(sex=None))#随机生成浏览器头user_agentprint(fake.user_agent())...
keys(), params)) parameters ={"learning_rate":[0.1, 1, 2], "penalty":[1, 2, 3]} for settings in grid_parameters(parameters): # Some random fake model that needs learning_rate & penalty as arguments result = model(**settings).train(X,y) ... Portfolio construction import scipy....
gen_imgs = generator.predict(z) # 训练鉴别器 d_loss_real = discriminator.train_on_batch(imgs, np.ones((batch_size, 1))) d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((batch_size, 1))) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) ...
gen_trg, feat = model.mixgenerator(org_image, 32, org_pose, trg_pose) out_trg = model.generator(feat, 32, trg_pose) #D_ab D_r, real_logit, real_pose = model.snpixdiscriminator(trg_image) D_f, fake_logit, fake_pose = model.snpixdiscriminator(gen_trg) ...