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# tf.compat.v1.train.cosine_decay_restarts

Applies cosine decay with restarts to the learning rate.

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 with restarts 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 while taking into account possible warm restarts. The learning rate multiplier first decays from 1 to `alpha` for `first_decay_steps` steps. Then, a warm restart is performed. Each new warm restart runs for `t_mul` times more steps and with `m_mul` times smaller initial learning rate.

#### Example usage:

``````first_decay_steps = 1000
lr_decayed = cosine_decay_restarts(learning_rate, global_step,
first_decay_steps)
``````

`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.
`first_decay_steps` A scalar `int32` or `int64` `Tensor` or a Python number. Number of steps to decay over.
`t_mul` A scalar `float32` or `float64` `Tensor` or a Python number. Used to derive the number of iterations in the i-th period
`m_mul` A scalar `float32` or `float64` `Tensor` or a Python number. Used to derive the initial learning rate of the i-th period:
`alpha` A scalar `float32` or `float64` Tensor or a Python number. Minimum learning rate value as a fraction of the learning_rate.
`name` String. Optional name of the operation. Defaults to 'SGDRDecay'.

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

`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.

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