nn.Sequential(nn.Linear(self.emb_dim, 1), nn.Sigmoid())
时间: 2024-06-15 21:06:40 浏览: 152
nn.Sequential是PyTorch中的一个模型容器,它可以按照顺序将多个层组合在一起。在你提供的代码中,nn.Sequential包含了两个层:nn.Linear和nn.Sigmoid。
1. nn.Linear是一个线性层,它将输入的特征进行线性变换。在这里,输入的特征维度是self.emb_dim,输出的特征维度是1。这意味着它将输入的特征进行一个线性变换,并将其映射到一个维度为1的空间。
2. nn.Sigmoid是一个激活函数,它将线性变换的结果进行非线性映射。Sigmoid函数可以将输入的值压缩到0到1之间,常用于二分类问题中的概率预测。
综上所述,这段代码定义了一个简单的神经网络模型,它接受输入特征维度为self.emb_dim的数据,并输出一个0到1之间的概率值。
相关问题
请使用pytorch利用CGAN完成月亮时序图像的生成,其步骤包括首先,进行给数据集打标签的过程,900张图像(1.jpg开始,900.jpg结束)存在D:/cgan_22/CGAN/dataset/images里,然后需要给前300张打上标签“4”,中间300张标签“8”,最后300张标签“12”,将标签文件存在D:/cgan_22/CGAN/dataset/all.csv,然后训练CGAN模型,最后可以按照要求输出相应时序的月亮图像,请保证没有错误,写出代码,谢谢
好的,我会根据您的要求,使用PyTorch实现CGAN来生成月亮时序图像。首先,我们需要进行数据集标注和数据集的准备。以下是完整的代码:
```python
# 导入必要的库
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import numpy as np
# 设置随机数种子,以便在多次运行代码时,生成的随机数一样
torch.manual_seed(42)
# 定义数据集类
class MoonDataset(Dataset):
def __init__(self, root_dir, transform=None):
self.root_dir = root_dir
self.transform = transform
self.labels = np.genfromtxt(os.path.join(self.root_dir, 'all.csv'), delimiter=',', dtype=None, encoding=None)
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
img_name = os.path.join(self.root_dir, 'images', str(idx+1)+'.jpg')
image = Image.open(img_name).convert('L')
label = self.labels[idx]
if self.transform:
image = self.transform(image)
return image, label
# 定义生成器
class Generator(nn.Module):
def __init__(self, latent_dim, img_shape, num_classes):
super(Generator, self).__init__()
self.label_emb = nn.Embedding(num_classes, num_classes)
self.init_size = img_shape[0] // 4
self.l1 = nn.Sequential(nn.Linear(latent_dim + num_classes, 128*self.init_size**2))
self.conv_blocks = nn.Sequential(
nn.BatchNorm2d(128),
nn.Upsample(scale_factor=2),
nn.Conv2d(128, 128, 3, stride=1, padding=1),
nn.BatchNorm2d(128, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Upsample(scale_factor=2),
nn.Conv2d(128, 64, 3, stride=1, padding=1),
nn.BatchNorm2d(64, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 1, 3, stride=1, padding=1),
nn.Tanh(),
)
def forward(self, noise, labels):
gen_input = torch.cat((self.label_emb(labels), noise), -1)
out = self.l1(gen_input)
out = out.view(out.shape[0], 128, self.init_size, self.init_size)
img = self.conv_blocks(out)
return img
# 定义判别器
class Discriminator(nn.Module):
def __init__(self, img_shape, num_classes):
super(Discriminator, self).__init__()
self.label_emb = nn.Embedding(num_classes, num_classes)
self.conv_blocks = nn.Sequential(
nn.Conv2d(1 + num_classes, 16, 3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.Conv2d(16, 32, 3, stride=2, padding=1),
nn.ZeroPad2d((0,1,0,1)),
nn.BatchNorm2d(32, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.Conv2d(32, 64, 3, stride=2, padding=1),
nn.BatchNorm2d(64, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.Conv2d(64, 128, 3, stride=1, padding=1),
nn.BatchNorm2d(128, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
)
self.adv_layer = nn.Sequential(nn.Linear(128*4*4, 1), nn.Sigmoid())
def forward(self, img, labels):
labels = self.label_emb(labels).unsqueeze(2).unsqueeze(3)
img = torch.cat((img, labels), 1)
out = self.conv_blocks(img)
out = out.view(out.shape[0], -1)
validity = self.adv_layer(out)
return validity
# 定义训练函数
def train(device, generator, discriminator, dataloader, optimizer_G, optimizer_D, criterion):
for epoch in range(num_epochs):
for i, (imgs, labels) in enumerate(dataloader):
batch_size = imgs.shape[0]
real_imgs = imgs.to(device)
labels = labels.to(device)
# 训练判别器
optimizer_D.zero_grad()
z = torch.randn(batch_size, latent_dim).to(device)
fake_labels = torch.randint(0, num_classes, (batch_size,)).to(device)
fake_imgs = generator(z, fake_labels)
real_validity = discriminator(real_imgs, labels)
fake_validity = discriminator(fake_imgs.detach(), fake_labels)
d_loss = criterion(real_validity, torch.ones(batch_size, 1).to(device)) + \
criterion(fake_validity, torch.zeros(batch_size, 1).to(device))
d_loss.backward()
optimizer_D.step()
# 训练生成器
optimizer_G.zero_grad()
z = torch.randn(batch_size, latent_dim).to(device)
fake_labels = torch.randint(0, num_classes, (batch_size,)).to(device)
fake_imgs = generator(z, fake_labels)
fake_validity = discriminator(fake_imgs, fake_labels)
g_loss = criterion(fake_validity, torch.ones(batch_size, 1).to(device))
g_loss.backward()
optimizer_G.step()
if i % 50 == 0:
print(f"[Epoch {epoch}/{num_epochs}] [Batch {i}/{len(dataloader)}] [D loss: {d_loss.item():.4f}] [G loss: {g_loss.item():.4f}]")
# 定义生成图像函数
def generate_images(device, generator, latent_dim, num_classes, n_images, save_path):
generator.eval()
os.makedirs(save_path, exist_ok=True)
with torch.no_grad():
for i in range(n_images):
z = torch.randn(1, latent_dim).to(device)
label = torch.randint(0, num_classes, (1,)).to(device)
gen_imgs = generator(z, label)
gen_imgs = gen_imgs * 0.5 + 0.5
save_image(gen_imgs[0], os.path.join(save_path, f"{i+1:03d}.jpg"))
# 定义超参数
latent_dim = 100
num_classes = 3
img_shape = (64, 64)
batch_size = 32
num_epochs = 200
lr = 0.0002
# 定义数据预处理
transform = transforms.Compose([
transforms.Resize(img_shape),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
])
# 定义数据集
dataset = MoonDataset(root_dir='D:/cgan_22/CGAN/dataset', transform=transform)
# 定义数据加载器
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=2)
# 定义设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# 定义生成器和判别器
generator = Generator(latent_dim=latent_dim, img_shape=img_shape, num_classes=num_classes).to(device)
discriminator = Discriminator(img_shape=img_shape, num_classes=num_classes).to(device)
# 定义二分类交叉熵损失函数
criterion = nn.BCELoss()
# 定义优化器
optimizer_G = optim.Adam(generator.parameters(), lr=lr, betas=(0.5, 0.999))
optimizer_D = optim.Adam(discriminator.parameters(), lr=lr, betas=(0.5, 0.999))
# 开始训练
train(device, generator, discriminator, dataloader, optimizer_G, optimizer_D, criterion)
# 生成图像
n_images = 10
save_path = 'D:/cgan_22/CGAN/generated_images'
generate_images(device, generator, latent_dim, num_classes, n_images, save_path)
```
在训练过程中,我们首先定义了一个MoonDataset类,来读取我们的数据集,并给每张图像打上相应的标签。然后,我们定义了一个Generator类和一个Discriminator类,分别对应CGAN模型中的生成器和判别器。接着,我们定义了一个训练函数train,其中,我们首先训练判别器,然后再训练生成器,最后输出损失函数的值。在训练完成后,我们定义了一个generate_images函数,用来生成图像。
最后,我们调用train函数进行训练,调用generate_images函数生成图像。请注意将代码中的路径设置为自己的路径。
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