在pytorch中冻结神经网络中的层答案

【问题标题】：freezing layers in a neural network in pytorch在pytorch中冻结神经网络中的层
【发布时间】：2021-12-30 06:15:02
【问题描述】：

我有一个级联神经网络，第一个网络的输出成为第二个网络的输入。第一个神经网络是预训练的，所以我只是用那些预训练的权重来初始化它。但是，我想冻结第一个神经网络，以便在训练它时只更新第二个神经网络的权重。我怎样才能做到这一点？我的网络如下所示：


###First network

class LambdaBase(nn.Sequential):
    def __init__(self, fn, *args):
        super(LambdaBase, self).__init__(*args)
        self.lambda_func = fn

    def forward_prepare(self, input):
        output = []
        for module in self._modules.values():
            output.append(module(input))
        return output if output else input

class Lambda(LambdaBase):
    def forward(self, input):
        return self.lambda_func(self.forward_prepare(input))

class LambdaMap(LambdaBase):
    def forward(self, input):
        return list(map(self.lambda_func,self.forward_prepare(input)))

class LambdaReduce(LambdaBase):
    def forward(self, input):
        return reduce(self.lambda_func,self.forward_prepare(input))

def get_first_model(load_weights = True):
    pretrained_model_reloaded_th = nn.Sequential( # Sequential,
        nn.Conv2d(4,300,(19, 1)),
        nn.BatchNorm2d(300),
        nn.ReLU(),
        nn.MaxPool2d((3, 1),(3, 1)),
        nn.Conv2d(300,200,(11, 1)),
        nn.BatchNorm2d(200),
        nn.ReLU(),
        nn.MaxPool2d((4, 1),(4, 1)),
        nn.Conv2d(200,200,(7, 1)),
        nn.BatchNorm2d(200),
        nn.ReLU(),
        nn.MaxPool2d((4, 1),(4, 1)),
        Lambda(lambda x: x.view(x.size(0),-1)), # Reshape,
        nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(2000,1000)), # Linear,
        nn.BatchNorm1d(1000,1e-05,0.1,True),#BatchNorm1d,
        nn.ReLU(),
        nn.Dropout(0.3),
        nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1000,1000)), # Linear,
        nn.BatchNorm1d(1000,1e-05,0.1,True),#BatchNorm1d,
        nn.ReLU(),
        nn.Dropout(0.3),
        nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1000,164)), # Linear,
        nn.Sigmoid(),
    )
    if load_weights:
        sd = torch.load('pretrained_model.pth')
        pretrained_model_reloaded_th.load_state_dict(sd)
    return  pretrained_model_reloaded_th

### second network

def next_model_architecture():
    next_model = nn.Sequential(
    nn.Linear(164, 64),
    nn.ReLU(),
    nn.Linear(64, 1),
    nn.Sigmoid())
    
    return next_model

### joining two networks
def cascading_model(first_model,next_model):
    network = nn.Sequential(first_model, next_model)
    return network


first_model = get_first_model(load_weights = True)
next_model = next_model_architecture()
network = cascading_model(first_model,next_model)

如果我这样做：


first_model = first_model.eval()

这会冻结我的第一个神经网络，并且只在训练期间更新第二个网络的权重吗？

【问题讨论】：

标签： python-3.x deep-learning pytorch

【解决方案1】：

通过将.requires_grad 设置为False 来冻结任何参数。通过迭代模块的所有参数（您要冻结的）来做到这一点

for p in first_model.parameters():
    p.requires_grad = False

【讨论】：