# learning_rate_schedule_inverse_time_decay

## A LearningRateSchedule that uses an inverse time decay schedule

## Description

A LearningRateSchedule that uses an inverse time decay schedule

## Usage

```
learning_rate_schedule_inverse_time_decay(
initial_learning_rate,
decay_steps,
decay_rate, staircase = FALSE,
..., name = NULL
)
```

## Arguments

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

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

decay_steps | A scalar `int32` or `int64` `Tensor` or an R number. How often to apply decay. |

decay_rate | An R number. The decay rate. |

staircase | Boolean. Whether to apply decay in a discrete staircase, as opposed to continuous, fashion. |

… | For backwards and forwards compatibility |

name | String. Optional name of the operation. Defaults to ‘InverseTimeDecay’. |

## Details

When training a model, it is often useful to lower the learning rate as the training progresses. This schedule applies the inverse decay function 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. It is computed as: ```

decayed_learning_rate <- function(step) {

initial_learning_rate / (1 + decay_rate * step / decay_step)

}

`or, if `staircase` is `TRUE`, as:`

decayed_learning_rate function(step) {

initial_learning_rate / (1 + decay_rate * floor(step / decay_step))

}

`You can pass this schedule directly into a keras Optimizer as the `learning_rate`. Example: Fit a Keras model when decaying `1/t` with a rate of `0.5`:`

…

initial_learning_rate <- 0.1

decay_steps <- 1.0

decay_rate <- 0.5

learning_rate_fn <- learning_rate_schedule_inverse_time_decay(

initial_learning_rate, decay_steps, decay_rate)

model %>%

compile(optimizer = optimizer_sgd(learning_rate = learning_rate_fn),

```
loss = 'sparse_categorical_crossentropy',
metrics = 'accuracy')
```

model %>% fit(data, labels, epochs = 5)

```