lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
时间: 2024-05-19 18:11:48 浏览: 10
这行代码是用来创建学习率调度器的。具体来说,它使用了PyTorch中的ReduceLROnPlateau调度器,该调度器可以在训练过程中动态地调整学习率。参数optimizer是优化器,factor是学习率调整的因子,即将学习率乘以一个小于1的数以调整学习率。patience是指当指标在patience个epoch内没有提升时,学习率将被降低。例如,如果patience=4,则当4个epoch内指标没有提升时,学习率将被降低。
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=200)这行代码啥意思
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。
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.