cycle-gan伪代码
时间: 2023-07-02 08:18:33 浏览: 88
以下是CycleGAN的伪代码:
```
# 定义两个域中的图像
domain1_images = ...
domain2_images = ...
# 定义生成器和判别器的网络结构
generator1 = ...
generator2 = ...
discriminator1 = ...
discriminator2 = ...
# 定义优化器和损失函数
optimizer_G = ...
optimizer_D1 = ...
optimizer_D2 = ...
criterion_GAN = ...
criterion_cycle = ...
criterion_identity = ...
# 训练生成器和判别器
for epoch in range(num_epochs):
for i, (real_domain1, real_domain2) in enumerate(zip(domain1_images, domain2_images)):
# 训练判别器1
optimizer_D1.zero_grad()
fake_domain1 = generator2(real_domain2)
loss_D1_real = criterion_GAN(discriminator1(real_domain1), True)
loss_D1_fake = criterion_GAN(discriminator1(fake_domain1.detach()), False)
loss_D1 = (loss_D1_real + loss_D1_fake) / 2
loss_D1.backward()
optimizer_D1.step()
# 训练判别器2
optimizer_D2.zero_grad()
fake_domain2 = generator1(real_domain1)
loss_D2_real = criterion_GAN(discriminator2(real_domain2), True)
loss_D2_fake = criterion_GAN(discriminator2(fake_domain2.detach()), False)
loss_D2 = (loss_D2_real + loss_D2_fake) / 2
loss_D2.backward()
optimizer_D2.step()
# 训练生成器
optimizer_G.zero_grad()
fake_domain1 = generator2(real_domain2)
loss_GAN_G = criterion_GAN(discriminator1(fake_domain1), True)
reconstructed_domain2 = generator1(fake_domain1)
loss_cycle_G = criterion_cycle(reconstructed_domain2, real_domain2)
identity_domain1 = generator2(real_domain1)
loss_identity_G = criterion_identity(identity_domain1, real_domain1)
loss_G = loss_GAN_G + lambda_cycle * loss_cycle_G + lambda_identity * loss_identity_G
loss_G.backward()
optimizer_G.step()
```
其中,`generator1` 和 `generator2` 是分别生成域1和域2中的图像的生成器网络,`discriminator1` 和 `discriminator2` 是分别对域1和域2中的图像进行判别的判别器网络。`criterion_GAN` 是对抗损失函数,`criterion_cycle` 是循环一致性损失函数,`criterion_identity` 是身份损失函数。在训练过程中,首先训练判别器1和判别器2,然后训练生成器,其中损失函数包括对抗损失、循环一致性损失和身份损失。