利用Python中的object_detection.utils.learning_schedulesmanual_stepping()手动调整学习率的方法

在目标检测任务中，调整学习率是优化模型训练过程中的一个重要步骤。在Python的TensorFlow库中，可以使用object_detection.utils.learning_schedules.manual_stepping()函数来手动调整学习率。该函数的签名如下：

def manual_stepping(global_step, boundaries, rates, warmup=False):
    """Manually stepped learning rate schedule.
  
    This function provides fine grained control over learning rates.  One must
    specify a sequence of learning rates as well as a set of integer steps
    at which the current learning rate must transition to the next.  For example,
    if boundaries = [5, 10] and rates = [.1, .01, .001], then the learning
    rate returned by this function is .1 for global_step=0,...,4, .01 for
    global_step=5...9, and .001 for global_step=10 and onward.
  
    Args:
      global_step: int64 (scalar) tensor representing global step.
      boundaries: a list of global steps at which to switch learning
        rates.  This list is assumed to consist of increasing positive integers.
      rates: a list of (float) learning rates corresponding to intervals between
        the boundaries.  The length of this list must be exactly
         len(boundaries) + 1.
      warmup: whether to linearly interpolate learning rate for steps between
          zero and the first boundary. Default is False.

    Returns:
      a (scalar) float tensor representing learning rate.
      """

该函数的参数解释如下：

- global_step：全局的训练步数，用以确定当前学习率的值。

- boundaries：由整数构成的列表，表示学习率需要调整的步数。这个列表必须是递增的正整数序列。

- rates：由浮点数构成的列表，表示每个学习率区间的学习率值。这个列表的长度必须是len(boundaries) + 1。

- warmup：是否进行热身训练，即在0到 个边界之间对学习率进行线性插值。默认值为False。

下面是一个使用manual_stepping()函数的示例：

import tensorflow as tf
from object_detection.utils import learning_schedules

global_step = tf.Variable(0, trainable=False)

# 设置学习率的边界
boundaries = [500, 1000]
# 设置每个区间的学习率
rates = [0.1, 0.01, 0.001]
# 调用manual_stepping()函数获取学习率
learning_rate = learning_schedules.manual_stepping(global_step, boundaries, rates)

# 定义优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)

# 在训练循环中更新学习率
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for step in range(num_steps):
        _, lr = sess.run([train_op, learning_rate])
        if step % 100 == 0:
            print("Step: {}, Learning Rate: {}".format(step, lr))

在上面的示例中，我们定义了一个全局的训练步数global_step，并设置了学习率的边界和对应的学习速率。然后，我们使用manual_stepping()函数来获取当前的学习率。在训练循环中，我们使用获取的学习率来进行模型的训练，并打印出当前的训练步数和学习率。