在PyTorch中進(jìn)行模型無監(jiān)督學(xué)習(xí)通常涉及訓(xùn)練一個自編碼器或生成對抗網(wǎng)絡(luò)(GAN)等模型。下面是一個簡單的示例,展示如何使用PyTorch訓(xùn)練一個簡單的自編碼器:
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
# 定義一個簡單的自編碼器模型
class Autoencoder(nn.Module):
def __init__(self):
super(Autoencoder, self).__init__()
self.encoder = nn.Sequential(
nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.Linear(64, 128),
nn.ReLU(),
nn.Linear(128, 784),
nn.Sigmoid()
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x
# 加載MNIST數(shù)據(jù)集
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
# 初始化模型和優(yōu)化器
model = Autoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 訓(xùn)練模型
num_epochs = 10
for epoch in range(num_epochs):
for data in train_loader:
img, _ = data
img = img.view(img.size(0), -1)
optimizer.zero_grad()
recon = model(img)
loss = criterion(recon, img)
loss.backward()
optimizer.step()
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}')
# 使用訓(xùn)練好的模型重建輸入圖像
test_img, _ = next(iter(train_loader))
test_img = test_img.view(test_img.size(0), -1)
output_img = model(test_img)
在上面的示例中,我們首先定義了一個簡單的自編碼器模型,然后加載了MNIST數(shù)據(jù)集并初始化了模型和優(yōu)化器。接下來,我們訓(xùn)練模型并輸出每個epoch的損失值。最后,我們使用訓(xùn)練好的模型對輸入圖像進(jìn)行重建。你可以根據(jù)自己的需求和數(shù)據(jù)集來調(diào)整模型結(jié)構(gòu)和超參數(shù),以獲得更好的無監(jiān)督學(xué)習(xí)效果。