loss = F.cross_entropy(outputs, labels) loss.backward()什么意思
时间: 2024-05-18 19:12:29 浏览: 9
这段代码使用 PyTorch 库中的函数计算 outputs 和 labels 之间的交叉熵损失(cross entropy loss),并将计算出的损失值存储在变量 loss 中。交叉熵损失常用于分类任务,可以衡量模型的预测结果与真实标签之间的差距。然后,使用反向传播算法计算损失值关于模型参数的梯度,并将梯度值存储在各自的张量中。最后,可以通过调用优化器的 step 函数来更新模型参数,以使模型的预测结果更加接近真实标签。这段代码可能用于训练神经网络模型,以便使其在给定的数据集上获得更好的分类性能。
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()
Focal 损失函数代码如下:def focal_loss(input_values, gamma): """Computes the focal loss""" p = torch.exp(-input_values) loss = (1 - p) ** gamma * input_values return loss.mean() class FocalLoss(nn.Module): def init(self, weight=None, gamma=0.): super(FocalLoss, self).init() assert gamma >= 0 self.gamma = gamma self.weight = weight def forward(self, input, target): return focal_loss(F.cross_entropy(input, target, reduction='none', weight=self.weight), self.gamma) LDAM损失函数代码如下:class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight) Large Margin aware Focal (LMF) 损失函数是 Focal 损失函数和 LDAM损失函数加权的线性组合,帮我用pytorch代码实现LMF损失函数并在模型中使用
LMF损失函数的代码如下:
```
class LMF_Loss(nn.Module):
def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30, gamma=2.0, alpha=0.25):
super(LMF_Loss, self).__init__()
self.focal_loss = FocalLoss(weight=weight, gamma=gamma)
self.ldam_loss = LDAMLoss(cls_num_list=cls_num_list, max_m=max_m, weight=weight, s=s)
self.alpha = alpha
def forward(self, x, target):
focal_loss = self.focal_loss(x, target)
ldam_loss = self.ldam_loss(x, target)
output = self.alpha * ldam_loss + (1 - self.alpha) * focal_loss
return output
```
在模型中使用时,可以将LMF_Loss作为损失函数进行指定。
例如,如果你的模型是一个分类模型,可以这样定义:
```
model = MyModel()
criterion = LMF_Loss(cls_num_list=[class0_num, class1_num, class2_num], max_m=0.5, weight=class_weights, s=30, gamma=2.0, alpha=0.25)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(num_epochs):
# Forward pass
outputs = model(inputs)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
其中,`cls_num_list`是一个列表,包含每个类别的样本数量,`max_m`是LDAM损失函数中的超参数,`weight`是样本权重,`s`是LDAM损失函数中的超参数,`gamma`是Focal损失函数中的超参数,`alpha`是LMF损失函数中的超参数,控制两个损失函数的加权。
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