# PolynomialDecay

[source]

### `PolynomialDecay` class

``````keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
name="PolynomialDecay",
)
``````

A `LearningRateSchedule` that uses a polynomial decay schedule.

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 backend 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:

``````def decayed_learning_rate(step):
step = min(step, decay_steps)
return ((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`.

``````def decayed_learning_rate(step):
decay_steps = decay_steps * ceil(step / decay_steps)
return ((initial_learning_rate - end_learning_rate) *
(1 - step / decay_steps) ^ (power)
) + end_learning_rate
``````

You can pass this schedule directly into a `keras.optimizers.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 = keras.optimizers.schedules.PolynomialDecay(
starter_learning_rate,
decay_steps,
end_learning_rate,
power=0.5)

model.compile(optimizer=keras.optimizers.SGD(
learning_rate=learning_rate_fn),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

model.fit(data, labels, epochs=5)
``````

The learning rate schedule is also serializable and deserializable using `keras.optimizers.schedules.serialize` and `keras.optimizers.schedules.deserialize`.

Arguments

• initial_learning_rate: A Python float. The initial learning rate.
• decay_steps: A Python integer. Must be positive. See the decay computation above.
• end_learning_rate: A Python float. The minimal end learning rate.
• power: A Python float. The power of the polynomial. Defaults to `1.0`.
• cycle: A boolean, whether it should cycle beyond decay_steps.
• name: String. Optional name of the operation. Defaults to `"PolynomialDecay"`.

Returns

A 1-arg callable learning rate schedule that takes the current optimizer step and outputs the decayed learning rate, a scalar tensor of the same type as `initial_learning_rate`.