learning_rate_schedule_cosine_decay_restarts {keras}R Documentation

A LearningRateSchedule that uses a cosine decay schedule with restarts

Description

A LearningRateSchedule that uses a cosine decay schedule with restarts

Usage

learning_rate_schedule_cosine_decay_restarts(
  initial_learning_rate,
  first_decay_steps,
  t_mul = 2,
  m_mul = 1,
  alpha = 0,
  ...,
  name = NULL
)

Arguments

initial_learning_rate

A scalar float32 or float64 Tensor or an R number. The initial learning rate.

first_decay_steps

A scalar int32 or int64 Tensor or an R number. Number of steps to decay over.

t_mul

A scalar float32 or float64 Tensor or an R number. Used to derive the number of iterations in the i-th period.

m_mul

A scalar float32 or float64 Tensor or an R number. Used to derive the initial learning rate of the i-th period.

alpha

A scalar float32 or float64 Tensor or an R number. Minimum learning rate value as a fraction of the initial_learning_rate.

...

For backwards and forwards compatibility

name

String. Optional name of the operation. Defaults to 'SGDRDecay'.

Details

See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent with Warm Restarts.

When training a model, it is often useful to lower the learning rate as the training progresses. This schedule applies a cosine decay function with restarts to an optimizer step, given a provided initial learning rate. It requires a step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The schedule is a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions.

The learning rate multiplier first decays from 1 to alpha for first_decay_steps steps. Then, a warm restart is performed. Each new warm restart runs for t_mul times more steps and with m_mul times initial learning rate as the new learning rate.

You can pass this schedule directly into a keras Optimizer as the learning_rate.

See Also

[Package keras version 2.15.0 Index]