tf.train.cosine_decay_restarts

tf.train.cosine_decay_restarts(
    learning_rate,
    global_step,
    first_decay_steps,
    t_mul=2.0,
    m_mul=1.0,
    alpha=0.0,
    name=None
)

Defined in tensorflow/python/training/learning_rate_decay.py.

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)

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.
  • 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'.

Returns:

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

Raises:

  • ValueError: if global_step is not supplied.