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tf.compat.v1.train.cosine_decay_restarts

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Applies cosine decay with restarts to the learning rate.

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.

References:

Stochastic Gradient Descent with Warm Restarts: Loshchilov et al., 2017 (pdf)

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.