scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.milestones, gamma=0.5)
时间: 2024-04-20 10:25:02 浏览: 27
这段代码创建了一个学习率调度器(scheduler),用于在训练过程中动态地调整优化器的学习率。
参数说明:
- optimizer:优化器对象,用于更新模型的参数。
- milestones:一个列表,表示在哪些训练epoch时调整学习率。当当前epoch的索引在milestones列表中时,学习率将按照gamma的倍数进行调整。
- gamma:学习率调整的倍数,默认为0.5。当到达milestones中指定的epoch时,学习率将乘以gamma。
通过使用这个学习率调度器,可以在训练过程中根据指定的milestones和gamma来动态地调整模型的学习率。这种学习率的调整策略可以帮助模型更好地收敛并提高训练效果。
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scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.5)
这段代码的作用是创建一个指数衰减的学习率调度器(scheduler)。
`torch.optim.lr_scheduler.ExponentialLR`是PyTorch提供的一个学习率调度器类。它可以根据指定的衰减因子(gamma)对优化器(optimizer)中的学习率进行指数衰减。
在这段代码中,通过传递`optimizer`和`gamma=0.5`作为参数,创建了一个指数衰减的学习率调度器,并将其赋值给`scheduler`变量。
使用学习率调度器可以在训练过程中自动调整学习率,以提高模型的性能。指数衰减是一种常用的学习率调度策略,通过每个epoch将学习率乘以一个衰减因子来降低学习率的值。
在训练过程中,可以通过`scheduler.step()`方法来更新优化器中的学习率,例如在每个epoch结束时调用`scheduler.step()`。
请注意,这段代码假设已经创建了一个合适的优化器对象`optimizer`。你可以根据实际情况进行修改和适应。
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.