甘肃省集约化网站建设,北京建设工程交易服务中心网站,企业网站管理系统视频教程,无锡网站制作哪家便宜利用pytorch对堆叠自编码器进行训练和验证 一、数据生成二、定义自编码器模型三、训练函数四、训练堆叠自编码器五、将已训练的自编码器级联六、微调整个堆叠自编码器 一、数据生成
随机生成一些数据来模拟训练和验证数据集#xff1a;
import torch# 随机生成数据
n_sample… 利用pytorch对堆叠自编码器进行训练和验证 一、数据生成二、定义自编码器模型三、训练函数四、训练堆叠自编码器五、将已训练的自编码器级联六、微调整个堆叠自编码器 一、数据生成
随机生成一些数据来模拟训练和验证数据集
import torch# 随机生成数据
n_samples 1000
n_features 784 # 例如28x28图像的像素数
train_data torch.rand(n_samples, n_features)
val_data torch.rand(int(n_samples * 0.1), n_features)二、定义自编码器模型
import torch.nn as nnclass Autoencoder(nn.Module):def __init__(self, input_size, hidden_size):super(Autoencoder, self).__init__()self.encoder nn.Sequential(nn.Linear(input_size, hidden_size),nn.Tanh())self.decoder nn.Sequential(nn.Linear(hidden_size, input_size),nn.Tanh())def forward(self, x):x self.encoder(x)x self.decoder(x)return x
三、训练函数
定义一个函数来训练自编码器
def train_ae(model, train_loader, val_loader, num_epochs, criterion, optimizer):for epoch in range(num_epochs):# Trainingmodel.train()train_loss 0for batch_data in train_loader:optimizer.zero_grad()outputs model(batch_data)loss criterion(outputs, batch_data)loss.backward()optimizer.step()train_loss loss.item()train_loss / len(train_loader)print(fEpoch {epoch1}/{num_epochs}, Training Loss: {train_loss:.4f})# Validationmodel.eval()val_loss 0with torch.no_grad():for batch_data in val_loader:outputs model(batch_data)loss criterion(outputs, batch_data)val_loss loss.item()val_loss / len(val_loader)print(fEpoch {epoch1}/{num_epochs}, Validation Loss: {val_loss:.4f})
四、训练堆叠自编码器
使用上面定义的函数来训练自编码器
from torch.utils.data import DataLoader# DataLoader
batch_size 32
train_loader DataLoader(train_data, batch_sizebatch_size, shuffleTrue)
val_loader DataLoader(val_data, batch_sizebatch_size, shuffleFalse)# 训练第一个自编码器
ae1 Autoencoder(input_size784, hidden_size400)
optimizer torch.optim.Adam(ae1.parameters(), lr0.001)
criterion nn.MSELoss()
train_ae(ae1, train_loader, val_loader, 10, criterion, optimizer)# 使用第一个自编码器的编码器对数据进行编码
encoded_train_data []
for data in train_loader:encoded_train_data.append(ae1.encoder(data))
encoded_train_loader DataLoader(torch.cat(encoded_train_data), batch_sizebatch_size, shuffleTrue)encoded_val_data []
for data in val_loader:encoded_val_data.append(ae1.encoder(data))
encoded_val_loader DataLoader(torch.cat(encoded_val_data), batch_sizebatch_size, shuffleFalse)# 训练第二个自编码器
ae2 Autoencoder(input_size400, hidden_size200)
optimizer torch.optim.Adam(ae2.parameters(), lr0.001)
train_ae(ae2, encoded_train_loader, encoded_val_loader, 10, criterion, optimizer)# 使用第二个自编码器的编码器对数据进行编码
encoded_train_data []
for data in train_loader:encoded_train_data.append(ae2.encoder(data))
encoded_train_loader DataLoader(torch.cat(encoded_train_data), batch_sizebatch_size, shuffleTrue)encoded_val_data []
for data in val_loader:encoded_val_data.append(ae2.encoder(data))
encoded_val_loader DataLoader(torch.cat(encoded_val_data), batch_sizebatch_size, shuffleFalse)# 训练第三个自编码器
ae3 Autoencoder(input_size400, hidden_size200)
optimizer torch.optim.Adam(ae3.parameters(), lr0.001)
train_ae(ae3, encoded_train_loader, encoded_val_loader, 10, criterion, optimizer)# 使用第三个自编码器的编码器对数据进行编码
encoded_train_data []
for data in train_loader:encoded_train_data.append(ae3.encoder(data))
encoded_train_loader DataLoader(torch.cat(encoded_train_data), batch_sizebatch_size, shuffleTrue)encoded_val_data []
for data in val_loader:encoded_val_data.append(ae3.encoder(data))
encoded_val_loader DataLoader(torch.cat(encoded_val_data), batch_sizebatch_size, shuffleFalse)
五、将已训练的自编码器级联
class StackedAutoencoder(nn.Module):def __init__(self, ae1, ae2, ae3):super(StackedAutoencoder, self).__init__()self.encoder nn.Sequential(ae1.encoder, ae2.encoder, ae3.encoder)self.decoder nn.Sequential(ae3.decoder, ae2.decoder, ae1.decoder)def forward(self, x):x self.encoder(x)x self.decoder(x)return xsae StackedAutoencoder(ae1, ae2, ae3)
六、微调整个堆叠自编码器
在整个数据集上重新训练堆叠自编码器来完成。
train_autoencoder(sae, train_dataset)
