tf.compat.v1.train.noisy_linear_cosine_decay

Applies noisy linear cosine decay to the learning rate.

tf.compat.v1.train.noisy_linear_cosine_decay(
    learning_rate, global_step, decay_steps, initial_variance=1.0,
    variance_decay=0.55, num_periods=0.5, alpha=0.0, beta=0.001, name=None
)

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by num_periods, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a noisy linear 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: python global_step = min(global_step, decay_steps) linear_decay = (decay_steps - global_step) / decay_steps) cosine_decay = 0.5 * ( 1 + cos(pi * 2 * num_periods * global_step / decay_steps)) decayed = (alpha + linear_decay + eps_t) * cosine_decay + beta decayed_learning_rate = learning_rate * decayed where eps_t is 0-centered gaussian noise with variance initial_variance / (1 + global_step) ** variance_decay

Example usage:

decay_steps = 1000
lr_decayed = noisy_linear_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.
initial_variance: initial variance for the noise. See computation above.
variance_decay: decay for the noise's variance. See computation above.
num_periods: Number of periods in the cosine part of the decay. See computation above.
alpha: See computation above.
beta: See computation above.
name: String. Optional name of the operation. Defaults to 'NoisyLinearCosineDecay'.

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.