for i, (features, labels) in enumerate(train_data_loader)什么意思
时间: 2024-01-27 21:04:29 浏览: 29
这段代码是一个for循环,用于遍历train_data_loader中的每一个数据样本。其中,i是当前样本的序号,features是样本的特征,labels是样本的标签。enumerate()函数将train_data_loader转换为一个可迭代对象,每次循环返回一个包含当前样本序号和对应特征标签的元组。
相关问题
现在我有三张DGL异质图,train_g_data = { ('drug', 'interacts', 'drug'): (train_edge_idx[0], train_edge_idx[1]), ('drug', 'interacts_reverse', 'drug'): (train_edge_idx[1], train_edge_idx[0]), ('drug', 'acts_on', 'protein'): (drug_protein[0], drug_protein[1]), } train_g = dgl.heterograph(train_g_data),val_g和test_g也是类似的结构,图的特征也已经添加,请你用python写一段代码,用DGL中的graphSAGE模型训练,用以预测interacts的属性DDI_feat即药物相互作用分数,这是一个回归任务,并解释一下原理
首先,需要导入相关的库,包括DGL和PyTorch:
```
import dgl
import torch
import torch.nn as nn
import dgl.function as fn
from dgl.nn.pytorch import GraphConv, GATConv
```
接下来就可以定义GraphSAGE模型了:
```
class GraphSAGE(nn.Module):
def __init__(self, in_feats, out_feats, n_layers, activation, aggregator_type):
super(GraphSAGE, self).__init__()
self.n_layers = n_layers
self.activation = activation
self.layers = nn.ModuleList()
self.aggregator_type = aggregator_type
# input layer
self.layers.append(GraphConv(in_feats, out_feats, aggregator_type=self.aggregator_type))
# hidden layers
for i in range(1, n_layers):
self.layers.append(GraphConv(out_feats, out_feats, aggregator_type=self.aggregator_type))
def forward(self, blocks, x):
h = x
for l, (layer, block) in enumerate(zip(self.layers, blocks)):
h_dst = h[:block.number_of_dst_nodes()]
h = layer(block, (h, h_dst))
if l != self.n_layers - 1:
h = self.activation(h)
return h
```
这里我们定义了一个GraphSAGE类,包含输入特征的维度、输出特征的维度、隐藏层的数量、激活函数以及aggregator类型。在初始化函数中,我们定义了一个ModuleList,包含了所有的图卷积层。第一层的输入维度为in_feats,输出维度为out_feats,aggregator类型为我们在定义图时指定的类型;其他的隐藏层的输入和输出维度都为out_feats。
在forward函数中,我们首先将输入特征x赋值给h,然后依次对每一层进行计算。对于每一层,我们先将h切分成两部分,分别对应于当前block中的源节点和目标节点。然后对切分后的h进行图卷积操作,并将结果再次赋值给h。如果当前不是最后一层,我们还需要对h进行激活函数的处理。最后返回最后一层的结果h。
接下来,我们可以定义训练函数,包括数据加载、模型训练、模型评估等步骤:
```
def train(model, optimizer, loss_fn, train_loader, val_loader, device, epochs):
best_val_loss = float('inf')
best_model = None
for epoch in range(epochs):
model.train()
train_loss = 0
for batch, data in enumerate(train_loader):
subgraph, features, labels = data
features = features.to(device)
labels = labels.to(device)
blocks = dgl.to_block(subgraph, subgraph.nodes())
output = model(blocks, features)
loss = loss_fn(output.squeeze(), labels.float())
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_loss /= (batch + 1)
val_loss = evaluate(model, loss_fn, val_loader, device)
print('Epoch {:d} | Train Loss {:.4f} | Val Loss {:.4f}'.format(epoch+1, train_loss, val_loss))
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = model
return best_model
def evaluate(model, loss_fn, loader, device):
model.eval()
loss = 0
with torch.no_grad():
for batch, data in enumerate(loader):
subgraph, features, labels = data
features = features.to(device)
labels = labels.to(device)
blocks = dgl.to_block(subgraph, subgraph.nodes())
output = model(blocks, features)
loss += loss_fn(output.squeeze(), labels.float()).item()
return loss / (batch + 1)
```
在训练函数中,我们首先定义了一个best_val_loss变量,用于记录最好的验证集误差和对应的模型。然后对于每个epoch,我们首先将模型设置为训练模式,然后遍历所有的训练数据,对于每个batch,我们首先将数据转移到设备上,然后将子图、特征和标签分别赋值给subgraph、features和labels变量。接下来,我们使用model对blocks和features进行计算,并计算loss。将梯度清零,执行反向传播和参数更新操作。最后,将该batch的loss加入train_loss中,并计算平均train_loss。
在每个epoch结束时,我们调用evaluate函数对模型进行验证集上的评估。在evaluate函数中,我们首先将模型设置为评估模式,然后遍历所有的验证数据,对于每个batch,我们首先将数据转移到设备上,然后将子图、特征和标签分别赋值给subgraph、features和labels变量。接下来,我们使用model对blocks和features进行计算,并计算loss。最后,将该batch的loss加入loss中,并计算平均loss。
接下来,我们可以加载数据集并进行训练:
```
from dgl.data.utils import load_graphs, save_graphs
train_g, val_g, test_g = load_graphs('data.dgl')
train_loader = dgl.dataloading.EdgeDataLoader(train_g, 'train', batch_size=1024, shuffle=True, drop_last=False)
val_loader = dgl.dataloading.EdgeDataLoader(val_g, 'val', batch_size=1024, shuffle=False, drop_last=False)
test_loader = dgl.dataloading.EdgeDataLoader(test_g, 'test', batch_size=1024, shuffle=False, drop_last=False)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GraphSAGE(256, 1, 2, nn.ReLU(), 'mean').to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.MSELoss()
best_model = train(model, optimizer, loss_fn, train_loader, val_loader, device, epochs=100)
test_loss = evaluate(best_model, loss_fn, test_loader, device)
print('Test Loss {:.4f}'.format(test_loss))
```
在加载数据集时,我们使用load_graphs函数从指定路径加载三张图,分别对应于训练集、验证集和测试集。接下来,我们使用EdgeDataLoader对训练集、验证集和测试集进行数据加载。我们使用cuda进行训练,如果不可用则使用cpu。接下来,我们定义了一个GraphSAGE模型,优化器和损失函数。然后调用train函数进行训练,并使用evaluate函数在测试集上进行评估。最后输出测试集的loss。
GraphSAGE模型的原理是通过对每个节点的邻居节点进行聚合,来生成每个节点的特征表示。在模型训练中,我们首先将每个子图切分成源节点和目标节点,然后对源节点和目标节点分别进行图卷积操作,得到它们的特征表示。接下来,我们对源节点和目标节点进行聚合,得到每个节点的邻居节点的特征表示。最后,我们将源节点和目标节点进行拼接,并将结果传递给下一层进行计算。在每一层中,我们还可以使用不同的聚合方式,例如mean、max或LSTM等。
import torch import torch.nn as nn import torch.optim as optim import numpy as np from torch.autograd import Variable from torchvision.datasets import ImageFolder from torchvision.transforms import transforms from torch.utils.data import DataLoader # 定义超参数 num_epochs = 10 batch_size = 32 learning_rate = 0.001 # 定义数据转换方式 transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.ToTensor(), transforms.Normalize(mean=[0.5], std=[0.5]) ]) # 加载数据集 train_dataset = ImageFolder(root='./ChineseStyle/train/', transform=transform) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_dataset = ImageFolder(root='./ChineseStyle/test/', transform=transform) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True) # 定义卷积神经网络结构 class Net(nn.Module): def init(self): super(Net, self).init() self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=2) self.pool = nn.MaxPool2d(kernel_size=2, stride=2) self.conv2 = nn.Conv2d(in_channels=6, out_channels=32, kernel_size=5, stride=1, padding=2) self.fc1 = nn.Linear(in_features=32 * 8 * 8, out_features=128) self.fc2 = nn.Linear(in_features=128, out_features=15) def forward(self, x): x = self.pool(torch.relu(self.conv1(x))) x = self.pool(torch.relu(self.conv2(x))) x = x.view(-1, 32 * 8 * 8) x = torch.relu(self.fc1(x)) x = self.fc2(x) return x # 实例化卷积神经网络 net = Net() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=learning_rate) # 训练模型 for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # 将输入和标签转换为变量 images = Variable(images) labels = Variable(labels) # 将梯度清零 optimizer.zero_grad() # 向前传递 outputs = net(images) # 计算损失函数 loss = criterion(outputs, labels) # 反向传播和优化 loss.backward() optimizer.step() # 打印统计信息 if (i + 1) % 100 == 0: print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f' % (epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, loss.item())) # 测试模型 correct = 0 total = 0 for images, labels in test_loader: # 向前传递 outputs = net(Variable(images)) # 获取预测结果 _, predicted = torch.max(outputs.data, 1) # 更新统计信息 total += labels.size(0) correct += (predicted == labels).sum() # 计算准确率 print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))有没有测试到测试集
是的,代码中有对测试集的测试。在训练模型之后,通过测试集的数据来检验模型的准确率。具体代码如下:
```
# 测试模型
correct = 0
total = 0
for images, labels in test_loader:
# 向前传递
outputs = net(Variable(images))
# 获取预测结果
_, predicted = torch.max(outputs.data, 1)
# 更新统计信息
total += labels.size(0)
correct += (predicted == labels).sum()
# 计算准确率
print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))
```