inter-supervise是什么

时间: 2023-02-26 15:57:04 浏览: 64
"Inter-supervise" 是一个机器学习术语,指的是使用多种监督学习算法相互协同工作的方法。这种方法通常是在多个监督学习模型之间共享数据和特征,从而使它们可以相互辅助并弥补彼此的不足。这种方法可以提高学习模型的准确性和稳健性。
相关问题

# Adversarial Supervise Architecture E_Hat = self.generator_aux(Z) H_hat = self.supervisor(E_Hat) Y_fake = self.discriminator(H_hat) self.adversarial_supervised = Model(inputs=Z, outputs=Y_fake, name='AdversarialSupervised')

在这段代码中,实现了一个对抗监督架构(Adversarial Supervised Architecture)。 首先,通过将随机噪声输入 Z 传递给生成器模型 self.generator_aux,得到生成器的输出 E_Hat。接下来,将 E_Hat 作为输入传递给监督模型 self.supervisor,得到监督模型的输出 H_hat。然后,将 H_hat 作为输入传递给判别器模型 self.discriminator,得到判别器的输出 Y_fake。 最后,使用 Keras 的 Model 类创建了一个名为 adversarial_supervised 的模型对象,其输入为 Z,输出为 Y_fake。这个 adversarial_supervised 模型将生成器、监督模型和判别器连接在一起,形成了一个整体的对抗监督架构。 这种架构的目的是通过生成器和监督模型的协同训练,使生成器能够生成逼真的数据样本,并通过判别器对生成的样本进行判别和评估。这样可以实现对抗训练和生成器的优化。

2018.04-2019.09 比亚迪股份有限公司 规划院 HC车型EMS主管英文翻译

Position: HC Model EMS Supervisor, Planning Institute, BYD Co., Ltd. Responsibilities: • Supervise and manage the electronic manufacturing services (EMS) for the HC (hybrid and electric) model series. • Plan and coordinate the production schedules and resource allocation for the EMS process. • Develop and implement quality control measures to ensure the reliability and performance of the EMS components. • Collaborate with cross-functional teams to optimize the EMS design and production processes. • Analyze and report on the performance and efficiency of the EMS operations. • Ensure compliance with relevant regulations and standards for EMS production. Requirements: • Bachelor's degree or higher in electrical engineering, mechanical engineering, or related field. • Minimum 5 years of experience in EMS production management, preferably in the automotive industry. • Strong knowledge of electronic components and manufacturing processes. • Experience with quality control and process improvement methodologies. • Excellent communication skills in both English and Chinese. • Ability to work effectively in a cross-functional team environment. • Strong leadership and problem-solving skills.

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优化以下SQL select pao.id, pao.order_no, pao.apply_time, pao.purchase_user_id, pao.purchase_user_name, pao.apply_user_id, pao.apply_user_name, pao.apply_department_id, pao.apply_department_name, pao.apply_end_time, pao.create_user_id, pao.create_user_name, pao.approve_type, pao.approve_user_id, pao.approve_user_name, pao.approve_time, pao.description, pao.order_type, pao.purchase_type, pao.storage_type, pao.compose_order_no, pao.company_id, pao.delete, pao.create_time, pao.update_time, pao.supplier_id, pao.image_path, pao.contract_id, pao.status, pao.invoice_signer_name, pao.total_amount, pao.total_amount_tax, pao.purchase_status, pao.cancel_reason, pao.print_status, pao.demand_id, pao.arrival_status, pao.supervise_num, pao.supervise_date, pao.merge_apply_id, pao.deadline, pao.remind , s.name as supplierName, paod.amount, cm.return_status as returnStatus, cm.inventory_status as inventoryStatus, cm.stock_remark, cm.merge_flag, cm.signature_file, cm.department_pass, cm.receipt_file, cm.amount_paid, cm.amount_unpaid, cm.contract_name, cm.status as contractStatus, cm.contract_no, cm.contract_amount, paod.product_name, cm.advance_payment, cm.advance_ratio, cm.currency_unit from purchase_apply_order pao left join supplier s on pao.supplier_id = s.id left join ( SELECT GROUP_CONCAT(distinct p.product_name) product_name, sum(IFNULL(amount_tax, 0)) amount, apply_order_no from purchase_apply_order_details pa left join product p on p.pn_code = pa.product_code where p.company_id = 29 GROUP BY apply_order_no ) paod on paod.apply_order_no = pao.order_no left join contract_management cm on pao.contract_id = cm.id where pao.delete = 0 and pao.company_id = 29 and deadline <= '2023-05-25 15:34:00.01' and remind = 0 and arrival_status in( 0 , 1 ) order by pao.create_time desc;

import sys import random import pygame from dust import Dust def check_keydown_events(event, robot): if event.key == pygame.K_RIGHT: # move right robot.moving_right = True elif event.key == pygame.K_LEFT: # move left robot.moving_left = True def check_keyup_events(event, robot): if event.key == pygame.K_RIGHT: robot.moving_right = False elif event.key == pygame.K_LEFT: robot.moving_left = False def check_events(robot): # respond to keyboard and mouse item # supervise keyboard and mouse item for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() elif event.type == pygame.KEYDOWN: check_keydown_events(event, robot) elif event.type == pygame.KEYUP: check_keyup_events(event, robot) def update_screen(ai_settings, screen, dusts, robot,detector): # fill color 填充颜色 screen.fill(ai_settings.bg_color) # check robot and dust collisions check_robot_dust_collisions(robot, dusts) # draw the dusts dusts.draw(screen) # draw the robot robot.blitme() # draw the detector detector.blitme() # visualiaze the window pygame.display.flip() def create_dust(ai_settings, screen, dusts): """Create dust, and place it in the room.""" dust = Dust(ai_settings, screen) dust.rect.x = random.randint(50, ai_settings.screen_width - 50) dust.rect.y = random.randint(50, ai_settings.screen_height - 50) dusts.add(dust) def create_room(ai_settings, screen, dusts): """Create a full room of dusts.""" for mine_number in range(ai_settings.dust_number): create_dust(ai_settings, screen, dusts) def check_robot_dust_collisions(robot, dusts): """Respond to robot-dust collisions.""" # Remove any robot and dusts that have collided. pygame.sprite.spritecollide(robot, dusts, True, None)

def define_gan(self): self.generator_aux=Generator(self.hidden_dim).build(input_shape=(self.seq_len, self.n_seq)) self.supervisor=Supervisor(self.hidden_dim).build(input_shape=(self.hidden_dim, self.hidden_dim)) self.discriminator=Discriminator(self.hidden_dim).build(input_shape=(self.hidden_dim, self.hidden_dim)) self.recovery = Recovery(self.hidden_dim, self.n_seq).build(input_shape=(self.hidden_dim, self.hidden_dim)) self.embedder = Embedder(self.hidden_dim).build(input_shape=(self.seq_len, self.n_seq)) X = Input(shape=[self.seq_len, self.n_seq], batch_size=self.batch_size, name='RealData') Z = Input(shape=[self.seq_len, self.n_seq], batch_size=self.batch_size, name='RandomNoise') # AutoEncoder H = self.embedder(X) X_tilde = self.recovery(H) self.autoencoder = Model(inputs=X, outputs=X_tilde) # Adversarial Supervise Architecture E_Hat = self.generator_aux(Z) H_hat = self.supervisor(E_Hat) Y_fake = self.discriminator(H_hat) self.adversarial_supervised = Model(inputs=Z, outputs=Y_fake, name='AdversarialSupervised') # Adversarial architecture in latent space Y_fake_e = self.discriminator(E_Hat) self.adversarial_embedded = Model(inputs=Z, outputs=Y_fake_e, name='AdversarialEmbedded') #Synthetic data generation X_hat = self.recovery(H_hat) self.generator = Model(inputs=Z, outputs=X_hat, name='FinalGenerator') # Final discriminator model Y_real = self.discriminator(H) self.discriminator_model = Model(inputs=X, outputs=Y_real, name="RealDiscriminator") # Loss functions self._mse=MeanSquaredError() self._bce=BinaryCrossentropy()

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