根据loss_G = criterion(output, real_label)指令可以看出生成器的损失函数是交叉熵还是平方误差损失函数吗
时间: 2024-05-28 12:13:10 浏览: 11
根据 `loss_G = criterion(output, real_label)` 这个指令无法确定生成器的损失函数是交叉熵还是平方误差损失函数,需要查看 `criterion` 的定义。
一般来说,如果 `criterion` 定义为 `nn.CrossEntropyLoss()`,则损失函数是交叉熵;如果定义为 `nn.MSELoss()`,则损失函数是平方误差损失函数。
相关问题
判别器输入为真实图像数据集和带掩膜的破损图像数据集,生成器输入为带掩膜的破损图像数据集,请定义生成器和判别器循环训练的代码,并计算生成器损失、判别器损失、重建损失
循环训练的代码如下:
```
# 定义生成器
generator = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1),
nn.Tanh()
)
# 定义判别器
discriminator = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
nn.Flatten(),
nn.Linear(128 * 8 * 8, 1),
nn.Sigmoid()
)
# 定义损失函数
criterion = nn.BCELoss()
# 定义优化器
generator_optimizer = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
discriminator_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))
# 开始循环训练
for epoch in range(num_epochs):
for i, (real_image, masked_image, mask) in enumerate(dataloader):
real_image = real_image.to(device)
masked_image = masked_image.to(device)
mask = mask.to(device)
# 定义标签
real_label = torch.ones(real_image.size(0), 1, device=device)
fake_label = torch.zeros(real_image.size(0), 1, device=device)
# 训练判别器
discriminator_optimizer.zero_grad()
output = discriminator(torch.cat((real_image, masked_image), dim=1))
real_loss = criterion(output, real_label)
fake_image = generator(masked_image)
output = discriminator(torch.cat((fake_image, masked_image), dim=1))
fake_loss = criterion(output, fake_label)
d_loss = real_loss + fake_loss
d_loss.backward()
discriminator_optimizer.step()
# 训练生成器
generator_optimizer.zero_grad()
fake_image = generator(masked_image)
output = discriminator(torch.cat((fake_image, masked_image), dim=1))
g_loss = criterion(output, real_label)
reconstructed_image = fake_image * (1 - mask) + masked_image * mask
r_loss = torch.mean(torch.abs(real_image - reconstructed_image))
total_loss = g_loss + 0.01 * r_loss
total_loss.backward()
generator_optimizer.step()
# 输出损失
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], D Loss: {:.4f}, G Loss: {:.4f}, Reconstruct Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, len(dataloader), d_loss.item(), g_loss.item(), r_loss.item()))
```
其中,`real_image`为真实图像数据集,`masked_image`为带掩膜的破损图像数据集,`fake_image`为生成器生成的破损图像数据集。循环训练中,先训练判别器,再训练生成器。
生成器损失为 `g_loss`,判别器损失为 `d_loss`,重建损失为 `r_loss`。其中,`total_loss`为总损失,用于更新生成器。
图片生成函数代码
以下是使用 PyTorch 实现的简单版本的图片生成器代码,其中使用了 DCGAN (Deep Convolutional Generative Adversarial Networks) 的结构:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as vutils
from torchvision import datasets, transforms
# 设置随机种子
manualSeed = 999
torch.manual_seed(manualSeed)
# 设置超参数
batch_size = 64
image_size = 64
num_channels = 3
latent_dim = 100
num_epochs = 5
lr = 0.0002
beta1 = 0.5
# 加载数据集
dataset = datasets.ImageFolder(root='path/to/dataset',
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=True, num_workers=2)
# 定义生成器
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(latent_dim, 512, 4, 1, 0, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.ConvTranspose2d(128, num_channels, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
return self.main(input)
# 定义判别器
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(num_channels, 128, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, 4, 2, 1, bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 512, 4, 2, 1, bias=False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(512, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input).view(-1, 1)
# 初始化生成器和判别器
generator = Generator()
discriminator = Discriminator()
# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizer_g = optim.Adam(generator.parameters(), lr=lr, betas=(beta1, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=lr, betas=(beta1, 0.999))
# 训练网络
for epoch in range(num_epochs):
for i, data in enumerate(data_loader, 0):
# 更新判别器
discriminator.zero_grad()
real_data = data[0].to(device)
batch_size = real_data.size(0)
label = torch.full((batch_size,), 1, device=device)
output = discriminator(real_data).view(-1)
errD_real = criterion(output, label)
errD_real.backward()
D_x = output.mean().item()
noise = torch.randn(batch_size, latent_dim, 1, 1, device=device)
fake_data = generator(noise)
label.fill_(0)
output = discriminator(fake_data.detach()).view(-1)
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
optimizer_d.step()
# 更新生成器
generator.zero_grad()
label.fill_(1)
output = discriminator(fake_data).view(-1)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.mean().item()
optimizer_g.step()
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(data_loader),
errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
if i % 500 == 0:
vutils.save_image(real_data, '%s/real_samples.png' % './results', normalize=True)
fake = generator(fixed_noise)
vutils.save_image(fake.detach(), '%s/fake_samples_epoch_%03d.png' % ('./results', epoch), normalize=True)
```
在代码中,我们首先设置了一些超参数,包括图像的大小、通道数、潜在向量的维度、学习率等等。接下来,我们加载了数据集,并使用 PyTorch 的 DataLoader 将其转换为批量数据。然后,我们定义了一个生成器和一个判别器,使用 ConvTranspose2d 和 Conv2d 层来实现卷积和反卷积操作,使用 BatchNorm2d 和 LeakyReLU 来提高网络的稳定性和效果。我们还定义了损失函数和优化器,分别使用 BCELoss 和 Adam。在训练过程中,我们首先更新判别器,然后更新生成器,不断交替进行训练,直到达到预定的训练轮数。在每个训练迭代中,我们输出损失函数的值和判别器的输出结果,以及一些训练样本和生成样本的图像。最后,我们保存了一些生成样本的图像,以便在训练完成后进行可视化和评估。
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