# How to do multitask learning in PyTorch.

In PyTorch, multitask learning can be achieved by using a multitask loss function to simultaneously optimize multiple tasks. One common approach is to use multiple loss functions, with each one corresponding to a task, and then combine these loss functions by weighting them to create the final loss function. Below is a simple example code:

```
import torch
import torch.nn as nn
import torch.optim as optim
# 定义多任务损失函数
class MultiTaskLoss(nn.Module):
def __init__(self, task_weights):
super(MultiTaskLoss, self).__init__()
self.task_weights = task_weights
def forward(self, outputs, targets):
loss = 0
for i in range(len(outputs)):
loss += self.task_weights[i] * nn.CrossEntropyLoss()(outputs[i], targets[i])
return loss
# 定义模型
class MultiTaskModel(nn.Module):
def __init__(self):
super(MultiTaskModel, self).__init__()
self.fc1 = nn.Linear(10, 5)
self.fc2 = nn.Linear(5, 2)
def forward(self, x):
x = self.fc1(x)
output1 = self.fc2(x)
output2 = self.fc2(x)
return [output1, output2]
# 定义数据和标签
data = torch.randn(1, 10)
target1 = torch.LongTensor([0])
target2 = torch.LongTensor([1])
# 创建模型和优化器
model = MultiTaskModel()
criterion = MultiTaskLoss([0.5, 0.5]) # 两个任务的损失函数权重均为0.5
optimizer = optim.SGD(model.parameters(), lr=0.01)
# 训练模型
optimizer.zero_grad()
outputs = model(data)
loss = criterion(outputs, [target1, target2])
loss.backward()
optimizer.step()
```

In the example above, we defined a multi-task model with two tasks and a corresponding multi-task loss function, where the weights for the loss functions of the two tasks are both 0.5. During training, we calculate the loss between the model’s output and the target values, and update the model parameters based on the total loss. This allows us to achieve multi-task learning.