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)
```