vgg16仅加载非全连接层参数训练测试的代码

答案：以下是使用 PyTorch 框架加载 VGG16 预训练模型，仅加载非全连接层参数进行训练和测试的代码：

import torch
import torchvision.models as models

# Load pre-trained VGG16 model
vgg = models.vgg16(pretrained=True)

# Remove last fully connected layers
vgg.classifier = torch.nn.Sequential(*list(vgg.classifier.children())[:-2])

# Freeze non-linear layers
for param in vgg.features.parameters():
    param.requires_grad = False

# Define new linear classifier layer
classifier = torch.nn.Linear(25088, 10)

# Replace last fully connected layer with the new linear layer
vgg.classifier.add_module('new_layer', classifier)

# Define loss function and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(vgg.classifier.parameters(), lr=0.001)

# Train the model
# ...

# Test the model
# ...

注意，这段代码仅用于演示如何加载预训练的 VGG16 模型并进行非全连接层参数的训练和测试。具体使用时还需根据具体情况进行修改和调整。