loss_real = criterion_GAN(pred_real, real_labels)报错:pred_real为(64,1,5,5)real_labelsw为(64,1)
时间: 2024-05-19 08:17:01 浏览: 8
这个问题可能是因为pred_real和real_labels的尺寸不匹配导致的。你可以先检查一下实际上这两个变量的尺寸是否符合你的预期。如果不符合,你需要调整它们的尺寸使其相匹配。另外,你也可以检查一下数据类型是否正确,例如是否需要对real_labels进行类型转换,以确保计算的正确性。如果还有问题,建议你提供更多的信息和错误的详细信息以便更好地帮助你解决问题。
相关问题
检查一下:import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.metrics import roc_auc_score # 定义神经网络模型 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(10, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 1) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.sigmoid(x) return x # 加载数据集 data = torch.load('data.pt') x_train, y_train, x_test, y_test = data train_dataset = TensorDataset(x_train, y_train) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset = TensorDataset(x_test, y_test) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) # 定义损失函数和优化器 criterion = nn.BCELoss() optimizer = optim.Adam(net.parameters(), lr=0.01) # 训练模型 net = Net() for epoch in range(10): running_loss = 0.0 for i, data in enumerate(train_loader): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() # 在测试集上计算AUC y_pred = [] y_true = [] with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = net(inputs) y_pred += outputs.tolist() y_true += labels.tolist() auc = roc_auc_score(y_true, y_pred) print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))
在运行代码之前,需要将 `net = Net()` 放在 `optimizer = optim.Adam(net.parameters(), lr=0.01)` 的前面,因为在定义优化器时需要先传入模型的参数。另外,代码中没有定义 `net`,需要先将 `Net` 类定义好再进行实例化。正确的代码如下所示:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.metrics import roc_auc_score
# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(10, 64)
self.fc2 = nn.Linear(64, 32)
self.fc3 = nn.Linear(32, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
x = nn.functional.relu(x)
x = self.fc3(x)
x = self.sigmoid(x)
return x
# 加载数据集
data = torch.load('data.pt')
x_train, y_train, x_test, y_test = data
train_dataset = TensorDataset(x_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = TensorDataset(x_test, y_test)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 定义损失函数和优化器
criterion = nn.BCELoss()
net = Net()
optimizer = optim.Adam(net.parameters(), lr=0.01)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
# 在测试集上计算AUC
y_pred = []
y_true = []
with torch.no_grad():
for data in test_loader:
inputs, labels = data
outputs = net(inputs)
y_pred += outputs.tolist()
y_true += labels.tolist()
auc = roc_auc_score(y_true, y_pred)
print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))
```
def test(g, model, criterion, test_loader): model.eval() with torch.no_grad(): for input_nodes, output_nodes, blocks in test_loader: blocks = [b.to(torch.device('cuda')) for b in blocks] input_features = blocks[0].srcdata['feat'] output_labels = blocks[-1].dstdata['label'] output_labels = output_labels.to(torch.device('cuda')) # forward pred = model(blocks, input_features) loss = criterion(pred, output_labels) # accuracy _, indices = torch.max(pred, dim=1) correct = torch.sum(indices == output_labels) accuracy = correct.item() / len(output_labels) return loss.item(), accuracy解释一下
这段代码是一个测试函数,用于测试模型在测试集上的性能表现。具体来说,这个函数接收四个参数:
- g:DGLGraph 对象,这个对象表示图数据。
- model:模型,用于对图数据进行预测。
- criterion:损失函数,用于计算模型预测结果与真实标签之间的差距。
- test_loader:测试数据的 DataLoader 对象,用于将测试数据划分为批次进行测试。
这个函数首先将模型设为评估模式(model.eval()),然后使用 with torch.no_grad() 语句块来禁用梯度计算,以提高计算效率。接着,对于每个批次的测试数据,将数据移动到 GPU 上,并从第一个块(input_nodes)中获取节点特征,从最后一个块(output_nodes)中获取节点标签,并将标签移动到 GPU 上。然后,将块数据和节点特征作为输入,使用模型进行预测,并计算预测结果与真实标签之间的损失。接着,根据预测结果和真实标签计算预测结果的准确率,并将损失和准确率返回。