TensorFlow-方法train.exponential_decay()

这个方法提供指数级衰减，用来让模型在训练后期，变化的精度高一些

When training a model, it is often recommended to lower the learning rate asthe training progresses.

This function applies an exponential decay functionto a provided initial learning rate. It requires a


global_step value tocompute the decayed learning rate. You can just pass a TensorFlow variablethat

you increment at each training step.

exponential_decay(    learning_rate,    global_step,    decay_steps,    decay_rate,    staircase=False,    name=None)

公式：

decayed_learning_rate = learning_rate *                        decay_rate ^ (global_step / decay_steps)

If the argument staircase is True, then global_step / decay_steps is aninteger division and the decayed learning rate follows a staircase function.

如果staircase是True，那么 global_step/decay_steps会是整数。

例子：

learning_rate = 0.1

decay_rate = 0.96

decay_steps = 1000

global_step = 1

rate = 0.0999959179

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learning_rate = 0.1

decay_rate = 0.96

decay_steps = 1000

global_step = 900

rate = 0.0963926921