case class Plateau(monitor: String, factor: Float = 0.1f, patience: Int = 10, mode: String = "min", epsilon: Float = 1e-4f, cooldown: Int = 0, minLr: Float = 0) extends LearningRateSchedule with Product with Serializable
Plateau is the learning rate schedule when a metric has stopped improving. Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. It monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.
- monitor
quantity to be monitored, can be Loss or score
- factor
factor by which the learning rate will be reduced. new_lr = lr * factor
- patience
number of epochs with no improvement after which learning rate will be reduced.
- mode
one of {min, max}. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing
- epsilon
threshold for measuring the new optimum, to only focus on significant changes.
- cooldown
number of epochs to wait before resuming normal operation after lr has been reduced.
- minLr
lower bound on the learning rate.
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new
Plateau(monitor: String, factor: Float = 0.1f, patience: Int = 10, mode: String = "min", epsilon: Float = 1e-4f, cooldown: Int = 0, minLr: Float = 0)
- monitor
quantity to be monitored, can be Loss or score
- factor
factor by which the learning rate will be reduced. new_lr = lr * factor
- patience
number of epochs with no improvement after which learning rate will be reduced.
- mode
one of {min, max}. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing
- epsilon
threshold for measuring the new optimum, to only focus on significant changes.
- cooldown
number of epochs to wait before resuming normal operation after lr has been reduced.
- minLr
lower bound on the learning rate.
Value Members
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final
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!=(arg0: Any): Boolean
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- val cooldown: Int
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val
currentRate: Double
- Definition Classes
- LearningRateSchedule
- val epsilon: Float
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final
def
eq(arg0: AnyRef): Boolean
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- val factor: Float
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isInstanceOf[T0]: Boolean
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- val minLr: Float
- val mode: String
- val monitor: String
- var monitorOp: (Float, Float) ⇒ Boolean
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def
updateHyperParameter[T](optimMethod: SGD[T]): Unit
update learning rate by config table and state table
update learning rate by config table and state table
- optimMethod
init optiMethod.
- Definition Classes
- Plateau → LearningRateSchedule
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Deprecated Value Members
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def
updateHyperParameter(config: Table, state: Table): Unit
- Definition Classes
- LearningRateSchedule
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(Since version 0.2.0) Please input SGD instead of Table