pytorch accuracy函数怎么用，请写一段代码

PyTorch中的accuracy函数可以通过torchmetrics库中的accuracy函数来实现。以下是一个示例代码：

import torch
from torchmetrics.functional import accuracy

# 预测结果
preds = torch.tensor([0, 1, 1, 0, 2, 1])
# 真实标签
targets = torch.tensor([0, 1, 0, 0, 2, 2])

# 计算准确率
acc = accuracy(preds, targets)

print(acc)

在这个示例中，我们首先构造了一个预测结果和真实标签，然后使用torchmetrics库中的accuracy函数计算准确率。最后将计算结果打印出来。

相关问题

pytorch猫狗分类写一段代码

在PyTorch中，我们通常会使用深度学习库来进行图像分类任务，例如猫狗分类。这里是一个简单的例子，展示如何使用ResNet18模型对CIFAR-10数据集进行训练。请注意，这只是一个基本框架，实际项目中需要更完整的数据预处理、模型配置等步骤。

# 导入必要的模块
import torch
import torch.nn as nn
from torchvision import datasets, transforms

# 数据加载和预处理
transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32, padding=4),
    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_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)

test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False)

# 使用预训练的ResNet18模型作为基础网络
model = models.resnet18(pretrained=True)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 2)  # 将最后一层改为我们的分类层，这里是2类：猫和狗

# 模型选择GPU运行（如果有可用）
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练过程
for epoch in range(10):  # 设置几个epoch
    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)
        
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{10}], Loss: {loss.item()}')

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Test Accuracy of the model on the 10000 test images: {100 * correct / total}%')

用 Pytorch 写一段模型训练的代码

### 回答1：

下面是在 Pytorch 中写的一段模型训练的代码：# 加载数据
train_data = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)# 初始化模型参数
model = Model(input_size, hidden_size, num_classes)# 定义loss和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)# 开始训练
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# 获取图片及其对应标签
images = images.reshape(-1, input_size).to(device)
labels = labels.to(device)

# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)

# 后向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step() if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

### 回答2：
import torch
import torch.nn as nn
import torch.optim as optim

# 定义一个简单的神经网络模型
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.fc1 = nn.Linear(10, 20) # 全连接层1
self.fc2 = nn.Linear(20, 1) # 全连接层2

def forward(self, x):
x = torch.relu(self.fc1(x)) # relu激活函数
x = self.fc2(x)
return x

# 模型训练数据准备
input_data = torch.randn(100, 10) # 输入数据（100个训练样本，每个样本10个特征）
target = torch.randn(100, 1) # 目标值

# 创建模型对象
model = SimpleModel()

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 模型训练
for epoch in range(100):
optimizer.zero_grad() # 清零梯度
output = model(input_data) # 前向传播
loss = criterion(output, target) # 计算损失
loss.backward() # 反向传播，计算梯度
optimizer.step() # 更新权重

if epoch % 10 == 0:
print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch, 100, loss.item()))

# 测试模型
test_input = torch.randn(10, 10) # 测试输入（10个样本，每个样本10个特征）
test_output = model(test_input) # 模型输出
print("测试输出:", test_output)

### 回答3：
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# 准备数据集
train_dataset = ... # 定义训练数据集
test_dataset = ... # 定义测试数据集

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# 定义模型
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.fc1 = nn.Linear(10, 100)
self.fc2 = nn.Linear(100, 10)

def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x

model = MyModel()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(10): # 迭代10次
running_loss = 0.0
for i, data in enumerate(train_loader):
inputs, labels = data

optimizer.zero_grad() # 清零梯度

outputs = model(inputs) # 前向传播
loss = criterion(outputs, labels) # 计算损失
loss.backward() # 反向传播
optimizer.step() # 更新参数

running_loss += loss.item()

print(f"Epoch {epoch+1}, Loss: {running_loss / len(train_loader)}")

# 测试模型
total_correct = 0
total_samples = 0
with torch.no_grad():
for data in test_loader:
inputs, labels = data
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
total_correct += (predicted == labels).sum().item()
total_samples += labels.size(0)

accuracy = total_correct / total_samples
print(f"Test Accuracy: {accuracy}")