from torch.nn import CrossEntropyLoss什么意思
时间: 2023-11-24 13:05:25 浏览: 28
`from torch.nn import CrossEntropyLoss` 这行代码的意思是从 `torch.nn` 模块中导入 `CrossEntropyLoss` 类。`CrossEntropyLoss` 是一个用于计算交叉熵损失的函数,常用于分类问题中的模型训练。交叉熵损失是一种常见的损失函数,用于衡量模型输出与目标标签之间的差异。在深度学习中,通过最小化交叉熵损失来优化模型参数,以提高模型在分类任务中的性能。
相关问题
import torch import torch.nn.functional as F import torch.optim as optim from torchvision import transforms from torchvision import datasets from torch.utils. data import DataI oader 补全代码
下面是一个基本的 PyTorch MNIST 数据集训练代码示例,包括数据加载、模型定义、优化器和损失函数的定义以及训练过程。
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
# 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)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# 定义模型
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 = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
net = Net()
# 定义优化器和损失函数
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()
# 训练模型
def train(epoch):
net.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = net(data)
loss = criterion(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()))
def test():
net.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = net(data)
test_loss += criterion(output, target)
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)))
for epoch in range(1, 5):
train(epoch)
test()
```
这是一个简单的卷积神经网络模型,用于对 MNIST 手写数字进行分类。训练过程中使用了交叉熵损失函数和随机梯度下降优化器。
net = LeNet5() paddle.summary(net,(-1,1,img_size,img_size)) from paddle.metric import Accuracy save_dir = "model/lenet_2" epoch = 5 lr = 0.01 weight_decay = 5e-4 batch_size = 64 model = paddle.Model(net) optim = paddle.optimizer.Adam(learning_rate=lr,parameter=model.parameters(),weight_decay=weight_decay) model.prepare(optim,paddle.nn.CrossEntropyloss(),paddle.nn.Accuracy()) model.fit(train_dataset,epochs=epoch,batch_size=batch_size,save_dir=save_dir,verbose=1) best_model_path = "model/lenet_2/final.pdparams" net = LeNet5() model = paddle.Model(net) model.load(best_model_path) model.prepare(optim,paddle.nn.CrossEntropyloss(),Accuracy()) results = model.evaluate(test_dataset,batch_size=batch_size,verbose=1) print(results)在pytorch中如何表示
在 PyTorch 中,上述代码可以表示为:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
class LeNet5(nn.Module):
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.pool2 = nn.MaxPool2d(2)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool1(torch.relu(self.conv1(x)))
x = self.pool2(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
train_dataset = # 加载训练数据集
test_dataset = # 加载测试数据集
batch_size = 64
epoch = 5
lr = 0.01
weight_decay = 5e-4
save_dir = "model/lenet_2"
best_model_path = "model/lenet_2/final.pt"
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = LeNet5().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=weight_decay)
# 训练模型
for epoch in range(epoch):
net.train()
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch+1}, loss: {running_loss/len(train_loader):.4f}")
torch.save(net.state_dict(), best_model_path)
# 测试模型
net.load_state_dict(torch.load(best_model_path))
net.eval()
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = correct / total
print(f"Accuracy on test set: {accuracy:.4f}")
```
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