帮我把这个对抗生成网络转成条件对抗生成网络,添加的条件为16维数据,你可以先随机生成一下
时间: 2024-04-20 17:27:40 浏览: 18
好的,我会将这个对抗生成网络转换为条件对抗生成网络,并添加一个16维的随机条件数据。以下是转换后的代码示例:
```python
import torch
import torch.nn as nn
# 定义生成器
class Generator(nn.Module):
def __init__(self, input_dim, output_dim, condition_dim):
super(Generator, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.condition_dim = condition_dim
self.model = nn.Sequential(
nn.Linear(input_dim + condition_dim, 128),
nn.ReLU(),
nn.Linear(128, 256),
nn.ReLU(),
nn.Linear(256, output_dim),
nn.Tanh()
)
def forward(self, z, c):
x = torch.cat((z, c), dim=1)
x = self.model(x)
return x
# 定义判别器
class Discriminator(nn.Module):
def __init__(self, input_dim, condition_dim):
super(Discriminator, self).__init__()
self.input_dim = input_dim
self.condition_dim = condition_dim
self.model = nn.Sequential(
nn.Linear(input_dim + condition_dim, 256),
nn.LeakyReLU(0.2),
nn.Linear(256, 128),
nn.LeakyReLU(0.2),
nn.Linear(128, 1),
nn.Sigmoid()
)
def forward(self, x, c):
x = torch.cat((x, c), dim=1)
x = self.model(x)
return x
# 随机生成条件数据
def generate_condition_data(batch_size):
condition_data = torch.randn(batch_size, 16)
return condition_data
# 设置网络参数
input_dim = 100
output_dim = 64
condition_dim = 16
# 初始化生成器和判别器
generator = Generator(input_dim, output_dim, condition_dim)
discriminator = Discriminator(output_dim, condition_dim)
# 随机生成输入数据和条件数据
z = torch.randn(batch_size, input_dim)
c = generate_condition_data(batch_size)
# 使用生成器生成样本
generated_samples = generator(z, c)
# 判别器判断样本真假
discriminator_output = discriminator(generated_samples, c)
```
这段代码将原本的对抗生成网络转换为条件对抗生成网络,并且添加了16维的条件数据。你可以根据实际情况进行调整和使用。