|View source on GitHub|
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
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
cycle is True then a multiple of
decay_steps is used, the first one
that is bigger than
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) )
learning_rate: A scalar
Tensoror a Python number. The initial learning rate.
global_step: A scalar
Tensoror a Python number. Global step to use for the decay computation. Must not be negative.
decay_steps: A scalar
Tensoror a Python number. Must be positive. See the decay computation above.
end_learning_rate: A scalar
Tensoror a Python number. The minimal end learning rate.
power: A scalar
Tensoror a Python number. The power of the polynomial. Defaults to linear, 1.0.
cycle: A boolean, whether or not it should cycle beyond decay_steps.
name: String. Optional name of the operation. Defaults to 'PolynomialDecay'.
Tensor of the same type as
learning_rate. The decayed
global_stepis not supplied.
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.