model.load_state_dict(torch.load(modelname))print('[INFO] Load Model complete')except:pass'训练模型'#准备mnist数据集 (数据会下载到py文件所在的data文件夹下)train_loader = torch.utils.data.DataLoader( datasets.MNIST('/data', train=True, download=True, transform=transforms.ToTensor()), batch_s...
AI代码解释 # 加载CelebA数据集transform=transforms.Compose([transforms.CenterCrop(148),transforms.Resize(64),transforms.ToTensor(),])celeba_dataset=datasets.CelebA(root='./data',split='train',download=True,transform=transform)celeba_loader=torch.utils.data.DataLoader(celeba_dataset,batch_size=128,shuffle...
1)参考代码 model.py # 定义变分自编码器VAEclass Variable_AutoEncoder(nn.Module):def __init__(self):super(Variable_AutoEncoder, self).__init__()# 定义编码器self.Encoder = nn.Sequential(nn.Linear(784, 256),nn.ReLU(),nn.Linear(256, 64),nn.ReLU())# 定义解码器self.Decoder = nn.Seque...
mu, logvar =self.encode(x.view(-1,28*28)) z =self.reparameterize(mu, logvar)returnself.decode(z), mu, logvar# 定义模型参数input_size =28*28# MNIST图像的维度hidden_size =400latent_size =20# 创建VAE模型实例model = VAE(input_size, hidden_size, latent_size) 步骤4:定义损失函数和优化...
train_dataset = datasets.MNIST(root=’./data’, train=True, transform=transform, download=True)train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True) 初始化模型和优化器 model = VAE(latent_dim=20)optimizer = optim.Adam(model.parameters(), lr=1e-3) 定义损失函数 def loss_...
# Create the autoencoder model and optimizer model = VAE() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # Define the loss function criterion = nn.MSELoss(reduction="sum") # Set the device to GPU if available, otherwise use CPU model.to(device) # Cr...
model.to(device) # Create a DataLoader to handle batching of the training data train_loader = torch.utils.data.DataLoader( X_train, batch_size=batch_size, shuffle=True ) 最后,训练循环也很标准: # Training loop for epoch in range(num_epochs): ...
model.to(device) # Create a DataLoader to handle batching of the training data train_loader = torch.utils.data.DataLoader( X_train, batch_size=batch_size, shuffle=True ) 最后,训练循环也很标准: # Training loop for epoch in range(num_epochs): ...
model=VAE().to(device) optimizer= torch.optim.Adam(model.parameters(), lr=1e-3)forepochinrange(10):fori, (x, _)inenumerate(data_loader): x= x.to(device).view(-1, 784) reconst_x, mu, log_var=model(x) loss=loss_function(reconst_x,x,mu,log_var) ...
data=MNIST("./data",transform=transformer,download=True)#载入数据 dataloader=DataLoader(data,batch_size=128,shuffle=True)#写入加载器 model=VAE(784,512,20).to(device)#初始化模型 optimer=torch.optim.Adam(model.parameters(),lr=1e-3)#初始化优化器 ...