# cosine_decay¶

paddle.fluid.layers.cosine_decay(learning_rate, step_each_epoch, epochs)[source]

Applies cosine decay to the learning rate.

when training a model, it is often recommended to lower the learning rate as the training progresses. By using this function, the learning rate will be decayed by following cosine decay strategy.

$decayed\_lr = learning\_rate * 0.5 * (math.cos * (epoch * \frac{math.pi}{epochs} ) + 1)$
Parameters
• learning_rate (Variable|float) – The initial learning rate.

• step_each_epoch (int) – the number of steps in an epoch.

• epochs (int) – the number of epochs.

Returns

The decayed learning rate.

Return type

Variable

Examples

import paddle.fluid as fluid
base_lr = 0.1
lr = fluid.layers.cosine_decay(
learning_rate = base_lr, step_each_epoch=10000, epochs=120)