# learning_rate_schedule_piecewise_constant_decay

## A LearningRateSchedule that uses a piecewise constant decay schedule

## Description

A LearningRateSchedule that uses a piecewise constant decay schedule

## Usage

```
learning_rate_schedule_piecewise_constant_decay(
boundaries,
values,
..., name = NULL
)
```

## Arguments

Arguments | Description |
---|---|

boundaries | A list of `Tensor` s or R numerics with strictly increasing entries, and with all elements having the same type as the optimizer step. |

values | A list of `Tensor` s or R numerics that specifies the values for the intervals defined by `boundaries` . It should have one more element than `boundaries` , and all elements should have the same type. |

… | For backwards and forwards compatibility |

name | A string. Optional name of the operation. Defaults to ‘PiecewiseConstant’. |

## Details

The function returns a 1-arg callable to compute the piecewise constant when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions. Example: use a learning rate that’s 1.0 for the first 100001 steps, 0.5 for the next 10000 steps, and 0.1 for any additional steps. ```

step <- tf$Variable(0, trainable=FALSE)

boundaries <- as.integer(c(100000, 110000))

values <- c(1.0, 0.5, 0.1)

learning_rate_fn <- learning_rate_schedule_piecewise_constant_decay(

`boundaries, values) `

# Later, whenever we perform an optimization step, we pass in the step.

learning_rate <- learning_rate_fn(step)

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

learning_rate`.