tf.compat.v1.train.polynomial_decay

Applies a polynomial decay to the learning rate.

tf.compat.v1.train.polynomial_decay(
    learning_rate, global_step, decay_steps, end_learning_rate=0.0001, power=1.0,
    cycle=False, name=None
)

It is commonly observed that a monotonically decreasing learning rate, whose degree of change is carefully chosen, results in a better performing model. This function applies a polynomial decay function to a provided initial learning_rate to reach an end_learning_rate in the given decay_steps.

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:

global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
                        (1 - global_step / decay_steps) ^ (power) +
                        end_learning_rate

If cycle is True then a multiple of decay_steps is used, the first one that is bigger than global_steps.

decay_steps = decay_steps * ceil(global_step / decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
                        (1 - global_step / decay_steps) ^ (power) +
                        end_learning_rate

Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):

...
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate = tf.compat.v1.train.polynomial_decay(starter_learning_rate,
global_step,
                                          decay_steps, end_learning_rate,
                                          power=0.5)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
    tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
    .minimize(...my loss..., global_step=global_step)
)

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.