训练好的ResNet-18模型的代码
时间: 2023-09-11 15:10:09 浏览: 49
以下是使用PyTorch框架加载预训练的ResNet-18模型的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import datasets, models, transforms
# 加载预训练好的ResNet-18模型
model = models.resnet18(pretrained=True)
# 输出网络结构
print(model)
# 冻结所有卷积层的参数
for param in model.parameters():
param.requires_grad = False
# 替换最后一层全连接层
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 10)
# 输出网络结构
print(model)
```
这段代码中,我们使用了`models.resnet18(pretrained=True)`来加载预训练好的ResNet-18模型。接着,我们将所有卷积层的参数都冻结,只训练最后一层全连接层。最后,我们用`nn.Linear`替换了ResNet-18模型的最后一层全连接层,以适应我们的分类任务。您可以根据自己的需求进行修改和调整。
相关问题
训练ResNet-18模型的代码
以下是使用PyTorch训练ResNet-18模型的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义数据预处理
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))
])
# 加载CIFAR-10数据集
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=100,
shuffle=False, num_workers=2)
# 定义ResNet-18模型
class ResNet18(nn.Module):
def __init__(self):
super(ResNet18, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64)
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128)
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256)
)
self.layer4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(512)
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, 10)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x) + x
x = self.layer2(x) + x
x = self.layer3(x) + x
x = self.layer4(x) + x
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
# 定义损失函数和优化器
net = ResNet18()
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
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
# 在测试集上评估模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
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))
```
注意:以上代码仅供参考,具体实现可能需要根据实际情况进行调整。
python生成resnet-lstm模型代码
以下是一个简单的示例,使用PyTorch生成ResNet-LSTM模型的代码:
```python
import torch
import torch.nn as nn
import torchvision.models as models
class ResNetLSTM(nn.Module):
def __init__(self, num_classes, hidden_size=512, num_layers=1, bidirectional=False):
super(ResNetLSTM, self).__init__()
self.resnet = models.resnet18(pretrained=True)
self.resnet.fc = nn.Identity()
self.lstm = nn.LSTM(input_size=512, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, bidirectional=bidirectional)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
with torch.no_grad():
x = self.resnet(x)
x = x.unsqueeze(1)
x, _ = self.lstm(x)
x = x[:, -1, :]
x = self.fc(x)
return x
```
在这个模型中,我们使用了一个预训练的ResNet18模型来提取输入图像的特征,然后将这些特征输入到一个LSTM层中进行时间序列建模。最后,我们使用一个全连接层将LSTM的输出映射到所需的输出类别数。
您可以根据自己的需求调整模型的超参数,例如隐藏层大小、LSTM层数、是否双向等。同时,您还可以使用不同的预训练ResNet模型来提取图像特征,例如ResNet50或ResNet101。