# tf.train.cosine_decay

Applies cosine decay to the learning rate.

### Aliases:

``````tf.train.cosine_decay(
learning_rate,
global_step,
decay_steps,
alpha=0.0,
name=None
)
``````

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a cosine decay function to a provided initial learning rate. It requires a `global_step` value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as:

``````global_step = min(global_step, decay_steps)
cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps))
decayed = (1 - alpha) * cosine_decay + alpha
decayed_learning_rate = learning_rate * decayed
``````

#### Example usage:

``````decay_steps = 1000
lr_decayed = cosine_decay(learning_rate, global_step, decay_steps)
``````

#### Args:

• `learning_rate`: A scalar `float32` or `float64` Tensor or a Python number. The initial learning rate.
• `global_step`: A scalar `int32` or `int64` `Tensor` or a Python number. Global step to use for the decay computation.
• `decay_steps`: A scalar `int32` or `int64` `Tensor` or a Python number. Number of steps to decay over.
• `alpha`: A scalar `float32` or `float64` Tensor or a Python number. Minimum learning rate value as a fraction of learning_rate.
• `name`: String. Optional name of the operation. Defaults to 'CosineDecay'.

#### Returns:

A scalar `Tensor` of the same type as `learning_rate`. The decayed learning rate.

#### Raises:

• `ValueError`: if `global_step` is not supplied.

#### Eager Compatibility

When eager execution is enabled, this function returns a function which in turn returns the decayed learning rate Tensor. This can be useful for changing the learning rate value across different invocations of optimizer functions.