pred=torch.sigmoid(pred)
时间: 2024-01-29 09:05:01 浏览: 31
This line of code applies the sigmoid function to the tensor or variable 'pred'. The sigmoid function is a common activation function used in neural networks that maps any input value to a value between 0 and 1. It is often used to convert the output of a model into probabilities or to squash the output of a model into a range that is easier to work with.
相关问题
检查一下:import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.metrics import roc_auc_score # 定义神经网络模型 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(10, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 1) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.sigmoid(x) return x # 加载数据集 data = torch.load('data.pt') x_train, y_train, x_test, y_test = data train_dataset = TensorDataset(x_train, y_train) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset = TensorDataset(x_test, y_test) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) # 定义损失函数和优化器 criterion = nn.BCELoss() optimizer = optim.Adam(net.parameters(), lr=0.01) # 训练模型 net = Net() for epoch in range(10): running_loss = 0.0 for i, data in enumerate(train_loader): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() # 在测试集上计算AUC y_pred = [] y_true = [] with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = net(inputs) y_pred += outputs.tolist() y_true += labels.tolist() auc = roc_auc_score(y_true, y_pred) print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))
在运行代码之前,需要将 `net = Net()` 放在 `optimizer = optim.Adam(net.parameters(), lr=0.01)` 的前面,因为在定义优化器时需要先传入模型的参数。另外,代码中没有定义 `net`,需要先将 `Net` 类定义好再进行实例化。正确的代码如下所示:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.metrics import roc_auc_score
# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(10, 64)
self.fc2 = nn.Linear(64, 32)
self.fc3 = nn.Linear(32, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
x = nn.functional.relu(x)
x = self.fc3(x)
x = self.sigmoid(x)
return x
# 加载数据集
data = torch.load('data.pt')
x_train, y_train, x_test, y_test = data
train_dataset = TensorDataset(x_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = TensorDataset(x_test, y_test)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 定义损失函数和优化器
criterion = nn.BCELoss()
net = Net()
optimizer = optim.Adam(net.parameters(), lr=0.01)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
# 在测试集上计算AUC
y_pred = []
y_true = []
with torch.no_grad():
for data in test_loader:
inputs, labels = data
outputs = net(inputs)
y_pred += outputs.tolist()
y_true += labels.tolist()
auc = roc_auc_score(y_true, y_pred)
print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))
```
解释每一句代码def structure_loss(pred, mask): weit = 1 + 5*torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask) wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none') wbce = (weit*wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3)) pred = torch.sigmoid(pred) inter = ((pred * mask)*weit).sum(dim=(2, 3)) union = ((pred + mask)*weit).sum(dim=(2, 3)) wiou = 1 - (inter + 1)/(union - inter+1) return (wbce + wiou).mean()
这段代码实现的是一个结构化损失函数,用于评估图像分割任务中预测结果和真实标签之间的差异。下面是每一句代码的解释:
```python
def structure_loss(pred, mask):
```
定义结构化损失函数,输入参数为预测结果和真实标签。
```python
weit = 1 + 5*torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)
```
计算权重,其中`weit`是加权系数,`mask`是真实标签。这里的加权系数是通过计算平均池化结果和真实标签之间的绝对差异来计算得到的。
```python
wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
```
计算二元交叉熵(Binary Cross Entropy,BCE)损失,用于评估预测结果和真实标签之间的相似度。此处使用了PyTorch中的`F.binary_cross_entropy_with_logits`函数,其中`pred`是预测结果,`mask`是真实标签,`reduction='none'`表示不进行降维。
```python
wbce = (weit*wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))
```
将二元交叉熵损失和权重相乘,然后在空间维度上求和,最后除以权重之和,得到加权二元交叉熵损失。
```python
pred = torch.sigmoid(pred)
```
将预测结果经过sigmoid函数进行归一化,得到概率值。
```python
inter = ((pred * mask)*weit).sum(dim=(2, 3))
```
计算交集(Intersection),即预测结果和真实标签之间的重叠部分。将预测结果、真实标签和权重相乘,然后在空间维度上求和,得到交集。
```python
union = ((pred + mask)*weit).sum(dim=(2, 3))
```
计算并集(Union),即预测结果和真实标签之间的总面积。将预测结果、真实标签和权重相加,然后在空间维度上求和,得到并集。
```python
wiou = 1 - (inter + 1)/(union - inter+1)
```
计算加权交并比(Weighted Intersection-over-Union,WIou)损失,用于评估预测结果和真实标签之间的重叠程度。此处使用了加权的交并比公式,其中加权系数为`1`,交集加`1`是为了防止分母为零的情况。
```python
return (wbce + wiou).mean()
```
将加权二元交叉熵损失和加权交并比损失相加,然后求平均值,得到最终的结构化损失函数结果。
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