`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`

.