`CosineDecay`

class```
keras.optimizers.schedules.CosineDecay(
initial_learning_rate,
decay_steps,
alpha=0.0,
name="CosineDecay",
warmup_target=None,
warmup_steps=0,
)
```

A `LearningRateSchedule`

that uses a cosine decay with optional warmup.

See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent with Warm Restarts.

For the idea of a linear warmup of our learning rate, see Goyal et al..

When we begin training a model, we often want an initial increase in our
learning rate followed by a decay. If `warmup_target`

is an int, this
schedule applies a linear increase per optimizer step to our learning rate
from `initial_learning_rate`

to `warmup_target`

for a duration of
`warmup_steps`

. Afterwards, it applies a cosine decay function taking our
learning rate from `warmup_target`

to `alpha`

for a duration of
`decay_steps`

. If `warmup_target`

is None we skip warmup and our decay
will take our learning rate from `initial_learning_rate`

to `alpha`

.
It requires a `step`

value to compute the 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 warmup followed by 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.

Our warmup is computed as:

```
def warmup_learning_rate(step):
completed_fraction = step / warmup_steps
total_delta = target_warmup - initial_learning_rate
return completed_fraction * total_delta
```

And our decay is computed as:

```
if warmup_target is None:
initial_decay_lr = initial_learning_rate
else:
initial_decay_lr = warmup_target
def decayed_learning_rate(step):
step = min(step, decay_steps)
cosine_decay = 0.5 * (1 + cos(pi * step / decay_steps))
decayed = (1 - alpha) * cosine_decay + alpha
return initial_decay_lr * decayed
```

Example usage without warmup:

```
decay_steps = 1000
initial_learning_rate = 0.1
lr_decayed_fn = keras.optimizers.schedules.CosineDecay(
initial_learning_rate, decay_steps)
```

Example usage with warmup:

```
decay_steps = 1000
initial_learning_rate = 0
warmup_steps = 1000
target_learning_rate = 0.1
lr_warmup_decayed_fn = keras.optimizers.schedules.CosineDecay(
initial_learning_rate, decay_steps, warmup_target=target_learning_rate,
warmup_steps=warmup_steps
)
```

You can pass this schedule directly into a `keras.optimizers.Optimizer`

as the learning rate. 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 int. Number of steps to decay over.**alpha**: A Python float. Minimum learning rate value for decay as a fraction of`initial_learning_rate`

.**name**: String. Optional name of the operation. Defaults to`"CosineDecay"`

.**warmup_target**: A Python float. The target learning rate for our warmup phase. Will cast to the`initial_learning_rate`

datatype. Setting to`None`

will skip warmup and begins decay phase from`initial_learning_rate`

. Otherwise scheduler will warmup from`initial_learning_rate`

to`warmup_target`

.**warmup_steps**: A Python int. Number of steps to warmup over.

**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`

.