pytorch用于ai绘画代码举例
时间: 2023-07-07 11:36:38 浏览: 74
以下是使用PyTorch实现基本的GAN模型,用于生成艺术作品的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# 定义生成器
class Generator(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Generator, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
# 定义判别器
class Discriminator(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, output_size)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.sigmoid(self.fc1(x))
x = self.sigmoid(self.fc2(x))
x = self.sigmoid(self.fc3(x))
return x
# 定义训练函数
def train(generator, discriminator, train_loader, optimizer_g, optimizer_d, criterion):
for batch_idx, (real_data, _) in enumerate(train_loader):
batch_size = real_data.size(0)
real_data = real_data.view(-1, 28 * 28)
label_real = torch.ones(batch_size, 1)
label_fake = torch.zeros(batch_size, 1)
# 训练判别器
optimizer_d.zero_grad()
output_real = discriminator(real_data)
loss_real = criterion(output_real, label_real)
loss_real.backward()
noise = torch.randn(batch_size, 100)
fake_data = generator(noise)
output_fake = discriminator(fake_data.detach())
loss_fake = criterion(output_fake, label_fake)
loss_fake.backward()
optimizer_d.step()
# 训练生成器
optimizer_g.zero_grad()
output_fake = discriminator(fake_data)
loss_g = criterion(output_fake, label_real)
loss_g.backward()
optimizer_g.step()
# 定义主函数
def main():
# 加载MNIST数据集
batch_size = 100
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_set = datasets.MNIST('../data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True)
# 定义模型及优化器
generator = Generator(100, 256, 784)
discriminator = Discriminator(784, 256, 1)
optimizer_g = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))
criterion = nn.BCELoss()
# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
train(generator, discriminator, train_loader, optimizer_g, optimizer_d, criterion)
print('Epoch [{}/{}], Loss_G: {:.4f}, Loss_D: {:.4f}'.format(epoch+1, num_epochs, loss_g.item(), loss_d.item()))
if __name__ == '__main__':
main()
```
这是一个基于MNIST数据集的简单GAN模型,可以用于生成手写数字的艺术作品。
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