scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=200)这行代码啥意思
时间: 2024-06-06 09:05:09 浏览: 18
这行代码定义了一个学习率调度程序,将根据模型在验证集上的表现来动态地调整学习率。
具体地说,`torch.optim.lr_scheduler.ReduceLROnPlateau` 是 PyTorch 提供的一种学习率调度程序,它会监测一个指标(在这里是验证集上的损失值),如果这个指标在连续 `patience` 个 epoch 上都没有改善,就将当前学习率乘以 `factor`。这个过程会一直持续下去,直到学习率下降到 `min_lr` 或者达到了最大的调整次数 `max_iter`。
在这里,`mode='min'` 表示我们希望监测的指标越小越好(即验证集上的损失值越小越好),`factor=0.1` 表示每次调整时将学习率乘以 0.1,`patience=200` 表示如果连续 200 个 epoch 都没有改善,就进行一次调整。`optimizer` 是我们定义的优化器,这个调度程序将根据优化器的状态来调整学习率。
相关问题
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau
The `scheduler` variable is an instance of the `ReduceLROnPlateau` class from the PyTorch `optim.lr_scheduler` module. This class implements a learning rate scheduler that monitors a specified metric and reduces the learning rate if the metric does not improve for a certain number of epochs.
The `ReduceLROnPlateau` scheduler takes the following parameters:
- `optimizer`: The optimizer that is being used to train the model.
- `mode`: Specifies whether the metric being monitored should be minimized or maximized. Possible values are `'min'`, `'max'`, or `'auto'` (which infers the mode based on the metric name).
- `factor`: The factor by which the learning rate is reduced. For example, if `factor=0.1`, the learning rate will be reduced by a factor of 0.1 (i.e., the new learning rate will be 0.1 times the old learning rate).
- `patience`: The number of epochs to wait before reducing the learning rate if the metric does not improve.
- `verbose`: Specifies whether to print information about the learning rate changes.
- `threshold`: The threshold for measuring the new optimum, to only focus on significant changes.
- `threshold_mode`: Specifies whether the threshold is relative (`'rel'`) or absolute (`'abs'`).
The `scheduler.step()` method is called at the end of each epoch to update the learning rate based on the monitored metric.
torch.optim.lr_scheduler.ReduceLROnPlateau
torch.optim.lr_scheduler.ReduceLROnPlateau是PyTorch的一个学习率调整器。它可以监测一个指标,比如验证集上的准确率或者损失,如果这个指标在一定的时间内不再改善,那么它会自动地将学习率降低一个因子,以便更好地训练模型。
该学习率调度器的使用很简单,只需要在训练过程中添加以下代码即可:
```python
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=True)
for epoch in range(num_epochs):
# 训练代码
# ...
# 验证代码
val_loss = validate(...)
scheduler.step(val_loss)
```
其中,optimizer是你定义的优化器,mode是衡量指标的方式,比如'min'表示指标越小越好,'max'表示指标越大越好;factor是衰减因子,即每次降低学习率的倍数;patience是指多少个epoch内没有提升时就触发学习率调整;verbose表示是否打印调整信息。
注意,ReduceLROnPlateau只能在optimizer.step()之后进行调用,而不能在optimizer.zero_grad()之后进行调用。