p-NET训练过程中的损失图像代码

以下是使用Python代码绘制p-NET训练过程中的损失图像的示例：

import matplotlib.pyplot as plt

# 假设losses是一个包含每个epoch的训练损失和验证损失的列表
train_losses = losses[0]
val_losses = losses[1]

# 绘制训练和验证损失图像
plt.plot(train_losses, label='Training Loss')
plt.plot(val_losses, label='Validation Loss')
plt.legend()
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('p-NET Training and Validation Loss')
plt.show()

这将生成一个图像，其中x轴是训练的epoch数，y轴是损失值。您可以根据需要调整代码以适应您的数据格式和绘图样式。

相关问题

googlenet图像分类cifar-10代码

以下是使用GoogleNet进行CIFAR-10图像分类的代码（使用PyTorch实现）：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义GoogleNet模型
class GoogLeNet(nn.Module):
    def __init__(self):
        super(GoogLeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)
        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(p=0.4)
        self.fc1 = nn.Linear(1024, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(x, kernel_size=3, stride=2, padding=1)
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.max_pool2d(x, kernel_size=3, stride=2, padding=1)
        x = self.inception3a(x)
        x = self.inception3b(x)
        x = nn.functional.max_pool2d(x, kernel_size=3, stride=2, padding=1)
        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = nn.functional.max_pool2d(x, kernel_size=3, stride=2, padding=1)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.dropout(x)
        x = self.fc1(x)
        return x

# 定义Inception模块
class Inception(nn.Module):
    def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
        super(Inception, self).__init__()
        # 1x1卷积层
        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, ch1x1, kernel_size=1),
            nn.BatchNorm2d(ch1x1),
            nn.ReLU(inplace=True)
        )
        # 1x1卷积层 + 3x3卷积层
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, ch3x3red, kernel_size=1),
            nn.BatchNorm2d(ch3x3red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch3x3red, ch3x3, kernel_size=3, padding=1),
            nn.BatchNorm2d(ch3x3),
            nn.ReLU(inplace=True)
        )
        # 1x1卷积层 + 5x5卷积层
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, ch5x5red, kernel_size=1),
            nn.BatchNorm2d(ch5x5red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch5x5red, ch5x5, kernel_size=5, padding=2),
            nn.BatchNorm2d(ch5x5),
            nn.ReLU(inplace=True)
        )
        # 3x3最大池化层 + 1x1卷积层
        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels, pool_proj, kernel_size=1),
            nn.BatchNorm2d(pool_proj),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)
        outputs = [branch1, branch2, branch3, branch4]
        return torch.cat(outputs, 1)

# 导入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))
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

# 定义损失函数和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = GoogLeNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 训练模型
for epoch in range(200):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data[0].to(device), data[1].to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss/100))
            running_loss = 0.0

    # 在测试集上测试模型
    correct, total = 0, 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data[0].to(device), data[1].to(device)
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (100*correct/total))

print('Finished Training')

注意：该代码可能需要在GPU上运行，因为GoogleNet比较深，计算量较大。如果没有GPU，可能需要更长的训练时间。

googlenet图像分类代码

以下是使用PyTorch实现GoogleNet图像分类的代码示例：

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

class Inception(nn.Module):
    def __init__(self, in_channels, n1x1, n3x3_reduce, n3x3, n5x5_reduce, n5x5, pool_proj):
        super(Inception, self).__init__()
        
        # 1x1 convolution branch
        self.conv1x1 = nn.Sequential(
            nn.Conv2d(in_channels, n1x1, kernel_size=1),
            nn.ReLU(inplace=True)
        )
        
        # 3x3 convolution branch
        self.conv3x3 = nn.Sequential(
            nn.Conv2d(in_channels, n3x3_reduce, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(n3x3_reduce, n3x3, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        
        # 5x5 convolution branch
        self.conv5x5 = nn.Sequential(
            nn.Conv2d(in_channels, n5x5_reduce, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(n5x5_reduce, n5x5, kernel_size=5, padding=2),
            nn.ReLU(inplace=True)
        )
        
        # Max pooling branch
        self.max_pool = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels, pool_proj, kernel_size=1),
            nn.ReLU(inplace=True)
        )
        
    def forward(self, x):
        x1 = self.conv1x1(x)
        x2 = self.conv3x3(x)
        x3 = self.conv5x5(x)
        x4 = self.max_pool(x)
        return torch.cat([x1, x2, x3, x4], 1)
        
class GoogleNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(GoogleNet, self).__init__()
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        
        self.conv2 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 192, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        
        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.max_pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.max_pool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
        self.avg_pool = nn.AdaptiveAvgPool2d((1,1))
        self.dropout = nn.Dropout(p=0.4)
        self.fc = nn.Linear(1024, num_classes)
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.max_pool1(x)
        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.max_pool2(x)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avg_pool(x)
        x = torch.flatten(x, 1)
        x = self.dropout(x)
        x = self.fc(x)
        return x

# Load the data
data_path = 'path/to/data/'
train_dataset = datasets.ImageFolder(
    root=data_path + 'train/',
    transform=transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

test_dataset = datasets.ImageFolder(
    root=data_path + 'test/',
    transform=transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# Define the model, loss function, and optimizer
model = GoogleNet(num_classes=10)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# Train the model
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(train_loader), loss.item()))
            
# Test the model
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
        
    print('Test Accuracy of the model on the test images: {} %'.format(100 * correct / total))

在这个示例中，我们使用了ImageNet数据集的一部分来进行训练和测试。您需要将`data_path`变量设置为包含`train`和`test`文件夹的根目录。此外，我们使用了PyTorch的内置数据增强方法来对图像进行预处理，并使用了交叉熵损失和随机梯度下降（SGD）优化器来训练模型。最后，我们使用测试集来评估模型的准确性。