def weight_parameters(self): return [param for name, param in self.named_parameters() if 'weight' in name]
时间: 2024-04-09 13:31:20 浏览: 9
这是一个用于获取模型中所有权重参数的方法。
方法中使用了`named_parameters()`函数来获取模型的所有参数,并遍历这些参数。对于每个参数,通过检查参数名称中是否包含关键字'weight',来判断该参数是否为权重参数。如果是权重参数,则将其添加到一个列表中。
最后,该方法返回包含所有权重参数的列表。
这个方法可以方便地获取模型中的权重参数,用于进行参数优化、正则化或其他与权重相关的操作。
相关问题
如何在下列代码中减小 Adam 优化器的学习率(lr),以防止步长过大;以及在模型中增加 Batch Normalization 层,以确保模型更稳定地收敛;class MLP(torch.nn.Module): def init(self, weight_decay=0.01): super(MLP, self).init() self.fc1 = torch.nn.Linear(178, 100) self.relu = torch.nn.ReLU() self.fc2 = torch.nn.Linear(100, 50) self.fc3 = torch.nn.Linear(50, 5) self.dropout = torch.nn.Dropout(p=0.1) self.weight_decay = weight_decay def forward(self, x): x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x def regularization_loss(self): reg_loss = torch.tensor(0.).to(device) for name, param in self.named_parameters(): if 'weight' in name: reg_loss += self.weight_decay * torch.norm(param) return reg_lossmodel = MLP() criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(train_loader): optimizer.zero_grad() outputs = model(inputs.to(device)) loss = criterion(outputs, labels.to(device)) loss += model.regularization_loss() loss.backward() optimizer.step()
要减小Adam 优化器的学习率(lr),可以通过设置optimizer的参数lr来实现:optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)。要在模型中增加 Batch Normalization 层以确保模型更稳定地收敛,可以在每个线性层(torch.nn.Linear)之后添加BatchNorm1d层(torch.nn.BatchNorm1d): class MLP(torch.nn.Module): def __init__(self, weight_decay=0.01): super(MLP, self).__init__() self.fc1 = torch.nn.Linear(178, 100) self.bn1 = torch.nn.BatchNorm1d(100) self.relu = torch.nn.ReLU() self.fc2 = torch.nn.Linear(100, 50) self.bn2 = torch.nn.BatchNorm1d(50) self.fc3 = torch.nn.Linear(50, 5) self.dropout = torch.nn.Dropout(p=0.1) self.weight_decay = weight_decay def forward(self, x): x = self.fc1(x) x = self.bn1(x) x = self.relu(x) x = self.fc2(x) x = self.bn2(x) x = self.relu(x) x = self.fc3(x) return x def regularization_loss(self): reg_loss = torch.tensor(0.).to(device) for name, param in self.named_parameters(): if 'weight' in name: reg_loss += self.weight_decay * torch.norm(param) return reg_loss model = MLP() criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.0001) for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(train_loader): optimizer.zero_grad() outputs = model(inputs.to(device)) loss = criterion(outputs, labels.to(device)) loss += model.regularization_loss() loss.backward() optimizer.step()
如何将其中测试集的loss函数收敛:class MLP(torch.nn.Module): def init(self, weight_decay=0.01): super(MLP, self).init() self.fc1 = torch.nn.Linear(178, 100) self.relu = torch.nn.ReLU() self.fc2 = torch.nn.Linear(100, 50) self.fc3 = torch.nn.Linear(50, 5) self.dropout = torch.nn.Dropout(p=0.1) self.weight_decay = weight_decay def forward(self, x): x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x def regularization_loss(self): reg_loss = torch.tensor(0.).to(device) for name, param in self.named_parameters(): if 'weight' in name: reg_loss += self.weight_decay * torch.norm(param) return reg_lossmodel = MLP() criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(train_loader): optimizer.zero_grad() outputs = model(inputs.to(device)) loss = criterion(outputs, labels.to(device)) loss += model.regularization_loss() loss.backward() optimizer.step()
根据给出的代码,下列是一些建议来使该模型的测试集的损失函数收敛:
1. 增加训练周期(num_epochs),以便为模型提供更多的训练时间;
2. 减小 Adam 优化器的学习率(lr),以防止步长过大;
3. 增加批量大小(batch_size),以减少计算中的噪声;
4. 在模型中增加 Batch Normalization 层,以确保模型更稳定地收敛;
5. 增加模型的复杂度,例如增加层数或增加节点数。
这是一些可能有用的泛化策略,并不能直接保证损失函数能够收敛。在实践中,我们应该根据对数据的理解和实验结果来调整这些超参数,以便使模型更准确地进行预测。