The programs included with the Ubuntu system are free software; the exact distribution terms for each program are described in the individual files in /usr/share/doc/*/copyright. Ubuntu comes with ABSOLUTELY NO WARRANTY, to the extent permitted by applicable law. Last login: Mon Jun 5 10:57:16 2023 from 10.2.120.201 /usr/bin/xauth: timeout in locking authority file /home/yizhun/.Xauthority To run a command as administrator (user "root"), use "sudo <command>". See "man sudo_root" for details.
时间: 2024-04-27 19:21:32 浏览: 7
这段信息是登录 Ubuntu 系统后显示的提示信息,其中包含了以下几个方面的内容:
1. Ubuntu 系统中的程序都是自由软件,每个程序的发行条款都在 `/usr/share/doc/*/copyright` 文件中有详细说明。
2. Ubuntu 不提供任何明示或暗示的担保,除非适用法律允许的范围内。
3. 最后一次登录的时间和来源 IP 地址。
4. `/usr/bin/xauth` 命令在尝试锁定 X 权限文件 `/home/yizhun/.Xauthority` 时出现了超时错误。
5. 提示可以使用 `sudo` 命令以管理员身份运行命令,同时提供了 `man sudo_root` 命令来查看 `sudo` 命令的详细使用说明。
需要根据具体情况来分析和解决出现的问题。例如,可以尝试删除 `/home/yizhun/.Xauthority` 文件并重新登录系统,或者使用 `sudo` 命令以管理员身份运行需要的命令。
相关问题
The repository 'http://mirrors.aliyun.com/docker-ce/linux/ubuntu jammy InRelease' is not signed.
As an AI language model, I don't have access to the system logs so I cannot determine the exact cause of this issue. However, this error message typically means that the package repository you're trying to access is not properly signed or authenticated.
To fix this issue, you can try the following steps:
1. Check if your system's date and time are correct. An incorrect date and time can cause issues with package authentication.
2. Try to update the package list again using the following command:
```
sudo apt-get update
```
3. If the error persists, you can try changing the package repository by editing the `/etc/apt/sources.list` file. You can try using a different mirror, such as the official Docker repository:
```
deb [arch=amd64] https://download.docker.com/linux/ubuntu jammy stable
```
4. If none of the above steps work, you can try manually importing the GPG key for the repository using the following command:
```
sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys <KEY_ID>
```
Replace `<KEY_ID>` with the ID of the GPG key for the repository. You can usually find this on the repository's website.
If none of these steps work, you can try reaching out to the maintainers of the repository for further assistance.
Modify the above code so that the positive and negative weights are random and the exact values are obtained from the objective function after Back propagation.
To modify the code so that the positive and negative weights are learned through backpropagation, you can define the weights as trainable parameters in the neural network class. Here's an updated version of the code:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class RegressionNet(nn.Module):
def __init__(self, input_size, hidden_size):
super(RegressionNet, self).__init__()
# Positive sub-network
self.positive_net = nn.Sequential(
nn.Linear(input_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, 1)
)
# Negative sub-network
self.negative_net = nn.Sequential(
nn.Linear(input_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, 1)
)
# Initialize weights randomly
self.positive_weight = nn.Parameter(torch.randn(1))
self.negative_weight = nn.Parameter(torch.randn(1))
def forward(self, x):
positive_output = self.positive_weight * self.positive_net(x)
negative_output = -self.negative_weight * self.negative_net(x)
output = positive_output + negative_output
return output
# Example usage
input_size = 10
hidden_size = 20
model = RegressionNet(input_size, hidden_size)
# Generate dummy input data
batch_size = 32
input_data = torch.randn(batch_size, input_size)
target = torch.randn(batch_size, 1)
# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Training loop
num_epochs = 100
for epoch in range(num_epochs):
# Forward pass
output = model(input_data)
# Compute loss
loss = criterion(output, target)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Print loss for monitoring
print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}")
# After training, you can access the learned weights
positive_weight = model.positive_weight.item()
negative_weight = model.negative_weight.item()
print(f"Positive weight: {positive_weight}, Negative weight: {negative_weight}")
```
In this updated code, we define `positive_weight` and `negative_weight` as trainable parameters using `nn.Parameter`. These parameters are initialized randomly and will be learned during the training process.
Inside the forward pass, we multiply the positive sub-network output by `positive_weight` and the negative sub-network output by `-negative_weight`. The rest of the code remains the same, with the addition of a training loop that performs forward and backward passes, updates the weights using an optimizer (here, stochastic gradient descent), and computes the loss for monitoring purposes.
After training, you can access the learned weights using `model.positive_weight.item()` and `model.negative_weight.item()`.
I hope this helps! Let me know if you have any further questions.