Python中利用object_detection.utils.learning_schedulesexponential_decay_with_burnin()函数设计的指数衰减学习率生成方法

object_detection.utils.learning_schedules模块中的exponential_decay_with_burnin()函数是一个用于生成指数衰减学习率的方法。它可以根据指定的参数设置，生成一个学习率变化曲线，以便在训练过程中逐渐降低学习率的大小。

这个函数的定义如下：

def exponential_decay_with_burnin(global_step,
                                  learning_rate_base,
                                  learning_rate_decay_steps,
                                  learning_rate_decay_factor,
                                  burnin_learning_rate=0.1,
                                  burnin_steps=0,
                                  name=None):
    """Applies exponential decay to the learning rate.

    This function applies exponential decay to the learning rate.
    This scheduling applies a burn-in factor to the learning rate for a fixed
    period at the beginning of training. In other words, during the burn-in
    stage, the learning rate starts at burnin_learning_rate, and then
    gradually increases to learning_rate_base. After the burn-in phase,
    the learning rate is decayed exponentially.

    Args:
      global_step: global_step tensor.
      learning_rate_base: base learning rate.
      learning_rate_decay_steps: learning rate decay steps.
      learning_rate_decay_factor: learning rate decay factor.
      burnin_learning_rate: burn-in learning rate.
      burnin_steps: burn-in steps.
      name: Optional name scope for the operations.

    Returns:
      a scalar tensor representing learning rate
    """

下面是一个使用exponential_decay_with_burnin()函数生成指数衰减学习率的例子：

import tensorflow as tf
from object_detection.utils.learning_schedules import exponential_decay_with_burnin

# 定义超参数
learning_rate_base = 0.1
learning_rate_decay_steps = 1000
learning_rate_decay_factor = 0.1
burnin_learning_rate = 0.01
burnin_steps = 100

# 定义global_step变量
global_step = tf.Variable(0, trainable=False)

# 调用exponential_decay_with_burnin()函数生成学习率变化曲线
learning_rate = exponential_decay_with_burnin(global_step,
                                              learning_rate_base,
                                              learning_rate_decay_steps,
                                              learning_rate_decay_factor,
                                              burnin_learning_rate,
                                              burnin_steps)

# 添加一个操作来增加global_step
increment_step = tf.assign_add(global_step, 1)

# 创建Session并初始化变量
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # 打印每个global_step和对应的学习率
    for step in range(2000):
        lr = sess.run(learning_rate)
        print("Step: {}, Learning Rate: {:.6f}".format(step, lr))
        sess.run(increment_step)

这个例子中，我们定义了一些超参数：learning_rate_base表示初始的学习率大小，learning_rate_decay_steps表示学习率衰减的步数，learning_rate_decay_factor表示学习率的衰减率。

我们还定义了burn-in阶段的学习率：burnin_learning_rate和burnin_steps，前者表示burn-in阶段的学习率大小，后者表示burn-in的步数。

然后，我们定义了一个global_step变量，并调用exponential_decay_with_burnin()函数来生成学习率变化曲线。在每一轮中，我们打印当前步数和对应的学习率，并增加global_step的值。

最后，我们创建了一个Session，并通过这个Session来运行程序。在每一次迭代中，我们都会打印出相应的步数和学习率。

这样就完成了一个使用exponential_decay_with_burnin()函数生成指数衰减学习率的例子。通过调整超参数，可以得到不同形状的学习率变化曲线，以满足具体的训练需求。