with torch.no_grad(): running_loss = 0.0

这段代码是用来控制 PyTorch 的梯度计算的。在这个上下文中，使用了"with torch.no_grad()"来确保在此范围内所做的所有计算都不会被记录在内，也就是不会影响模型的梯度计算。同时，"running_loss = 0.0"是初始化一个变量来计算模型的误差，以便在训练的每一步中更新模型的梯度。

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：

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))

详细分析一下python代码:import torch.optim as optim criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.01, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=True, min_lr=0) loss_hist, acc_hist = [], [] loss_hist_val, acc_hist_val = [], [] for epoch in range(140): running_loss = 0.0 correct = 0 for data in train_loader: batch, labels = data batch, labels = batch.to(device), labels.to(device) optimizer.zero_grad() outputs = net(batch) loss = criterion(outputs, labels) loss.backward() optimizer.step() # compute training statistics _, predicted = torch.max(outputs, 1) correct += (predicted == labels).sum().item() running_loss += loss.item() avg_loss = running_loss / len(train_set) avg_acc = correct / len(train_set) loss_hist.append(avg_loss) acc_hist.append(avg_acc) # validation statistics net.eval() with torch.no_grad(): loss_val = 0.0 correct_val = 0 for data in val_loader: batch, labels = data batch, labels = batch.to(device), labels.to(device) outputs = net(batch) loss = criterion(outputs, labels) _, predicted = torch.max(outputs, 1) correct_val += (predicted == labels).sum().item() loss_val += loss.item() avg_loss_val = loss_val / len(val_set) avg_acc_val = correct_val / len(val_set) loss_hist_val.append(avg_loss_val) acc_hist_val.append(avg_acc_val) net.train() scheduler.step(avg_loss_val) print('[epoch %d] loss: %.5f accuracy: %.4f val loss: %.5f val accuracy: %.4f' % (epoch + 1, avg_loss, avg_acc, avg_loss_val, avg_acc_val))

这段代码是一个基于PyTorch的神经网络训练过程。代码中使用了torch.optim模块中Adam优化器和ReduceLROnPlateau学习率调度器。其中，Adam优化器用于优化网络的参数，而ReduceLROnPlateau调度器用于自动调整学习率以提高训练效果。代码中使用nn.CrossEntropyLoss()作为损失函数，用于计算输出结果与标签之间的差距。

接下来的代码中使用了两个循环，一个是对训练数据集的循环，另一个是对验证数据集的循环。在训练数据集循环中，首先将数据集分成一个个batch，然后将batch和对应的标签传入网络进行前向传播，计算损失值，然后进行反向传播和参数更新。在这个过程中，记录了每个batch的正确预测个数和损失值，最后计算平均损失和准确率，并将其保存在loss_hist和acc_hist列表中。

在验证数据集循环中，同样将数据集分成一个个batch，然后将batch和对应的标签传入网络进行前向传播，计算损失值，并计算正确预测个数。最后将每个batch的平均损失和准确率记录在loss_hist_val和acc_hist_val列表中。

在每个epoch结束后，调用scheduler.step(avg_loss_val)方法来更新学习率，并打印出当前epoch的训练和验证结果。其中，avg_loss和avg_acc记录了该epoch的训练结果，avg_loss_val和avg_acc_val记录了该epoch的验证结果。