在深度學習中學習率的往往根據自己的數據集驗證最好的超參數，然而更好的學習策略可以幫助你不通過交叉驗證來獲得一個較好的學習率超參數。

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

所有學習率策略文件的完整定義在 [tensorflow/python/training/learning_rate_decay.py]
Applies a polynomial decay to the learning rate.

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 initiallearning_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

實例: 學習率在10000次迭代內從0.1衰減到0.01，使用開平方指數

global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate = tf.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.train.GradientDescentOptimizer(learning_rate)
    .minimize(...my loss..., global_step=global_step)
)