pytorch迁移学习，使用预训练模型

# Download and load the pretrained ResNet-18.
resnet = torchvision.models.resnet18(pretrained=True)

# 将参数设置为不可修改
for param in resnet.parameters():
param.requires_grad = False

# 替换网络的顶层
resnet.fc = nn.Linear(resnet.fc.in_features, 100)  # resnet.fc.in_features是输入维度，100为输出维度，此时网络的顶层是可以训练的，实现了网络的微调

# Forward pass.
images = torch.randn(64, 3, 224, 224)
outputs = resnet(images)
print (outputs.size())     # (64, 100)