ERROR: Cannot install tuning==2020.8.28.15.36.59 and tuning==2020.8.28.15.39.17 because these package versions have conflicting dependencies.

这个错误提示表明 `tuning==2020.8.28.15.36.59` 和 `tuning==2020.8.28.15.39.17` 这两个版本有冲突的依赖关系，因此无法同时安装。这可能是由于其他已安装的包依赖于其中一个版本而导致的。

你可以尝试使用以下命令安装一个兼容的版本：

pip install tuning==<compatible_version>

在 `<compatible_version>` 中，你可以尝试使用其他版本号来找到与你的其他依赖相兼容的版本。如果无法找到兼容的版本，你可以尝试联系包的开发者或者查看相关文档以获取更多支持。

另外，你还可以考虑使用虚拟环境来隔离项目的依赖关系，以避免冲突问题。虚拟环境可以创建一个独立的 Python 环境，使你能够在其中安装和管理特定于项目的包。

相关问题

parames_to_update=model_ft.parameters()

This line of code retrieves all the parameters of a pre-trained model called `model_ft` that need to be updated during fine-tuning.

During fine-tuning, we want to update only a subset of the parameters in the pre-trained model. This is because the lower layers of the pre-trained model have already learned useful features that we want to retain, so we don't want to update those parameters. On the other hand, the higher layers of the pre-trained model are more specific to the original task for which it was trained, so we want to update those parameters to better fit our new task.

By calling `model_ft.parameters()`, we obtain an iterator over all the parameters in the model. We can then select the parameters that need to be updated by filtering out the ones we want to keep fixed. This is typically done by setting the `requires_grad` attribute of each parameter to `False` or `True` depending on whether we want to freeze it or update it during fine-tuning.

self.base_layers = nn.Sequential(*layers)

这段代码是将上面提到的layers列表中的所有层按顺序组成一个新的神经网络模型，并将其存储在变量self.base_layers中。这个新的模型包含了所有原始模型中除最后一层以外的所有层，可以用于特征提取或fine-tuning。这里使用了Python中的unpacking语法（*）将layers列表中的层作为参数传递给nn.Sequential()函数。