TF学习之DeepLabv3+代码阅读6（train_utils）

DeepLabv3+代码阅读之train_utils.py

一、get_model_learning_rate()

def get_model_learning_rate(learning_policy,# Learning rate policy for training.
                            base_learning_rate,# The base learning rate for model training.
                            learning_rate_decay_step, # Decay the base learning rate at a fixed step.
                            learning_rate_decay_factor,# The rate to decay the base learning rate.
                            training_number_of_steps,# Number of steps for training.
                            learning_power,# Power used for 'poly' learning policy.
                            slow_start_step,# Training model with small learning rate for the 
                            				# first few steps.
                            slow_start_learning_rate,# The learning rate employed during slow start.
                            slow_start_burnin_type='none'):# The burnin type for the slow start stage. Can be
      													   #`none` which means no burnin or `linear` which 
      													   # means the learning rate increases linearly from 
      													   # slow_start_learning_rate and reaches
      													   # base_learning_rate after slow_start_steps.
  """Gets model's learning rate.

  Computes the model's learning rate for different learning policy.
  Right now, only "step" and "poly" are supported.
  (1) The learning policy for "step" is computed as follows:
    current_learning_rate = base_learning_rate *
      learning_rate_decay_factor ^ (global_step / learning_rate_decay_step)
  See tf.train.exponential_decay for details.
  (2) The learning policy for "poly" is computed as follows:
    current_learning_rate = base_learning_rate *
      (1 - global_step / training_number_of_steps) ^ learning_power

  """
  global_step = tf.train.get_or_create_global_step()
  adjusted_global_step = global_step

  if slow_start_burnin_type != 'none':
    adjusted_global_step -= slow_start_step

  if learning_policy == 'step':
    learning_rate = tf.train.exponential_decay(
        base_learning_rate,
        adjusted_global_step,
        learning_rate_decay_step,
        learning_rate_decay_factor,
        staircase=True)
  elif learning_policy == 'poly':
    learning_rate = tf.train.polynomial_decay(
        base_learning_rate,
        adjusted_global_step,
        training_number_of_steps,
        end_learning_rate=0,
        power=learning_power)
  else:
    raise ValueError('Unknown learning policy.')

  adjusted_slow_start_learning_rate = slow_start_learning_rate
  if slow_start_burnin_type == 'linear':
    # Do linear burnin. Increase linearly from slow_start_learning_rate and
    # reach base_learning_rate after (global_step >= slow_start_steps).
    adjusted_slow_start_learning_rate = (
        slow_start_learning_rate +
        (base_learning_rate - slow_st