TensorFlow-方法train.exponential_decay()-优快云博客

本文介绍了一种在深度学习中使用的指数衰减学习率方法，该方法能够随着训练的进行而逐渐降低学习率，有助于提高模型训练后期的精度。文章详细解释了如何使用此方法，并给出了具体的数学公式和实例。

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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

///////////////

learning_rate = 0.1

decay_rate = 0.96

decay_steps = 1000

global_step = 900

rate = 0.0963926921