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# tf.keras.optimizers.schedules.PolynomialDecay

A LearningRateSchedule that uses a polynomial decay schedule.

Inherits From: `LearningRateSchedule`

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:

``````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 `tf.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 = tf.keras.optimizers.schedules.PolynomialDecay(
starter_learning_rate,
decay_steps,
end_learning_rate,
power=0.5)

model.compile(optimizer=tf.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 `tf.keras.optimizers.schedules.serialize` and `tf.keras.optimizers.schedules.deserialize`.

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`.

`initial_learning_rate` A scalar `float32` or `float64` `Tensor` or a Python number. The initial learning rate.
`decay_steps` A scalar `int32` or `int64` `Tensor` or a Python number. Must be positive. See the decay computation above.
`end_learning_rate` A scalar `float32` or `float64` `Tensor` or a Python number. The minimal end learning rate.
`power` A scalar `float32` or `float64` `Tensor` or a Python number. The power of the polynomial. Defaults to linear, 1.0.
`cycle` A boolean, whether or not it should cycle beyond decay_steps.
`name` String. Optional name of the operation. Defaults to 'PolynomialDecay'.

## Methods

### `from_config`

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Instantiates a `LearningRateSchedule` from its config.

Args
`config` Output of `get_config()`.

Returns
A `LearningRateSchedule` instance.

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### `__call__`

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Call self as a function.