使用pytorch 写一个对抗自编码器 模型的代码
时间: 2024-04-30 18:24:00 浏览: 9
Sure, 我可以帮您回答这个问题!以下是一个基于 PyTorch 的对抗自编码器模型的代码,供您参考:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(Encoder, self).__init__()
self.fc = nn.Linear(input_dim, hidden_dim)
self.out = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = torch.relu(self.fc(x))
x = self.out(x)
return x
class Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(Decoder, self).__init__()
self.fc = nn.Linear(input_dim, hidden_dim)
self.out = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = torch.relu(self.fc(x))
x = self.out(x)
return x
class AE(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(AE, self).__init__()
self.encoder = Encoder(input_dim, hidden_dim, output_dim)
self.decoder = Decoder(output_dim, hidden_dim, input_dim)
def forward(self, x):
x = self.encoder(x)
x = torch.sigmoid(self.decoder(x))
return x
class Discriminator(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.sigmoid(self.fc2(x))
return x
def train_AE():
# Load data
# ...
# Set up models
ae_model = AE(input_dim, hidden_dim, output_dim)
d_model = Discriminator(input_dim, hidden_dim, 1)
# Set up optimizers and loss functions
optimizer_ae = optim.Adam(ae_model.parameters(), lr=lr_ae)
optimizer_d = optim.Adam(d_model.parameters(), lr=lr_d)
criterion_ae = nn.MSELoss()
criterion_d = nn.BCELoss()
# Train the models alternately
for epoch in range(num_epochs):
for i, x in enumerate(data):
# Train the AE
optimizer_ae.zero_grad()
x_hat = ae_model(x)
loss_ae = criterion_ae(x_hat, x)
loss_ae.backward()
optimizer_ae.step()
# Train the discriminator
optimizer_d.zero_grad()
real_labels = torch.ones(batch_size, 1)
fake_labels = torch.zeros(batch_size, 1)
z = torch.randn(batch_size, output_dim)
x_hat = ae_model.decoder(z)
real_scores = d_model(x)
fake_scores = d_model(x_hat)
loss_d = criterion_d(real_scores, real_labels) + criterion_d(fake_scores, fake_labels)
loss_d.backward()
optimizer_d.step()
# Print the loss every 1000 iterations
if i % 1000 == 0:
print(f"Epoch [{epoch}/{num_epochs}], Iteration [{i}/{len(data)}], Loss: {loss_ae.item()}")
```
希望对您有所帮助!