learning_rate_schedule_polynomial_decay
A LearningRateSchedule that uses a polynomial decay schedule
Description
A LearningRateSchedule that uses a polynomial decay schedule
Usage
learning_rate_schedule_polynomial_decay(
initial_learning_rate,
decay_steps,
end_learning_rate = 1e-04,
power = 1,
cycle = 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. Must be positive. See the decay computation above. |
| end_learning_rate | A scalar float32 or float64 Tensor or an R number. The minimal end learning rate. |
| power | A scalar float32 or float64 Tensor or an R number. The power of the polynomial. Defaults to linear, 1.0. |
| cycle | A boolean, whether or not it should cycle beyond decay_steps. |
| … | For backwards and forwards compatibility |
| name | String. Optional name of the operation. Defaults to ‘PolynomialDecay’. |
Details
It is commonly observed that a monotonically decreasing learning rate, whose degree of change is carefully chosen, results in a better performing model. This schedule applies a polynomial decay function to an optimizer step, given a provided initial_learning_rate, to reach an end_learning_rate in the given decay_steps. 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) {
step <- min(step, decay_steps)
((initial_learning_rate - end_learning_rate) *
(1 - step / decay_steps) ^ (power)
) + end_learning_rate }
If `cycle` is `TRUE` then a multiple of `decay_steps` is used, the first one that is bigger than `step`.
decayed_learning_rate <- function(step) {
decay_steps <- decay_steps * ceiling(step / decay_steps)
((initial_learning_rate - end_learning_rate) *
(1 - step / decay_steps) ^ (power)
) + end_learning_rate }
You can pass this schedule directly into a keras Optimizer as the `learning_rate`. Example: Fit a model while decaying from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):
…
starter_learning_rate <- 0.1
end_learning_rate <- 0.01
decay_steps <- 10000
learning_rate_fn <- learning_rate_schedule_polynomial_decay(
starter_learning_rate, decay_steps, end_learning_rate, power = 0.5)
model %>%
compile(optimizer = optimizer_sgd(learning_rate = learning_rate_fn),
loss = 'sparse_categorical_crossentropy',
metrics = 'accuracy') model %>% fit(data, labels, epochs = 5)
```