__eq__的使用情况以及实例代码

__eq__是Python中的一个特殊方法，用于比较两个对象是否相等。在Python中，使用==运算符时，实际上就是调用了__eq__方法。下面是一个__eq__方法的实例代码：

class Person:
def __init__(self, name, age):
self.name = name
self.age = age

def __eq__(self, other):
if isinstance(other, Person):
return self.name == other.name and self.age == other.age
return False

p1 = Person("Tom", 18)
p2 = Person("Tom", 18)
p3 = Person("Jerry", 20)

print(p1 == p2) # True
print(p1 == p3) # False

注意，__eq__方法的返回值必须是布尔值True或False，表示两个对象是否相等。

相关问题

pytorch代码实例

以下是一个简单的PyTorch代码实例，用于训练一个简单的神经网络来分类手写数字图像：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

# 加载数据集
train_dataset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('data', train=False, download=True, transform=transform)

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 实例化模型和优化器
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

# 训练模型
def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 测试模型
def test():
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += nn.functional.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 运行训练和测试
for epoch in range(1, 11):
    train(epoch)
    test()

这个代码实例使用了PyTorch来定义一个简单的卷积神经网络，然后使用MNIST数据集来训练和测试这个模型。在训练过程中，模型使用随机梯度下降优化器来最小化交叉熵损失函数。在测试过程中，模型计算了测试集上的平均损失和准确率。

pytorch训练时使用tqdm，给出完整实例代码

好的，以下是一个使用tqdm的PyTorch训练示例代码。在这个例子中，我们使用CIFAR-10数据集进行图像分类任务。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from tqdm import tqdm

# 定义超参数
num_epochs = 10
batch_size = 128
learning_rate = 0.001

# 加载数据集并进行预处理
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.fc1 = nn.Linear(4*4*128, 512)
        self.fc2 = nn.Linear(512, 10)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(2, 2)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.maxpool(x)
        x = self.relu(self.conv2(x))
        x = self.maxpool(x)
        x = self.relu(self.conv3(x))
        x = self.maxpool(x)
        x = x.view(-1, 4*4*128)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 实例化模型和损失函数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = CNN().to(device)
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    # 训练阶段
    model.train()
    train_loss = 0.0
    train_correct = 0
    for data, target in tqdm(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
        pred = output.argmax(dim=1, keepdim=True)
        train_correct += pred.eq(target.view_as(pred)).sum().item()
    train_loss /= len(train_loader.dataset)
    train_acc = train_correct / len(train_loader.dataset)

    # 测试阶段
    model.eval()
    test_loss = 0.0
    test_correct = 0
    with torch.no_grad():
        for data, target in tqdm(test_loader):
            data, target = data.to(device), target.to(device)
            output = model(data)
            loss = criterion(output, target)
            test_loss += loss.item() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            test_correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    test_acc = test_correct / len(test_loader.dataset)

    # 输出训练和测试结果
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'
          .format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))

在上述代码中，我们使用了tqdm包装了训练和测试的数据加载器，从而实现了进度条的显示。