class_docstr: This is the Classification Model training class, that allows you to define a deep learning network
  from the 4 available networks types supported by ImageAI which are MobileNetv2, ResNet50,
  InceptionV3 and DenseNet121.
class_docstr_tok: ["This","is","the","Classification","Model","training","class",",","that","allows","you","to","define","a","deep","learning","network","from","the","4","available","networks","types","supported","by","ImageAI","which","are","MobileNetv2",",","ResNet50",",","InceptionV3","and","DenseNet121","."]
code: def lr_schedule(self, epoch):
      # Learning Rate Schedule
  
      lr = self.__initial_learning_rate
      total_epochs = self.__num_epochs
  
      check_1 = int(total_epochs * 0.9)
      check_2 = int(total_epochs * 0.8)
      check_3 = int(total_epochs * 0.6)
      check_4 = int(total_epochs * 0.4)
  
      if epoch > check_1:
          lr *= 1e-4
      elif epoch > check_2:
          lr *= 1e-3
      elif epoch > check_3:
          lr *= 1e-2
      elif epoch > check_4:
          lr *= 1e-1
  
      return lr
  
imports: import tensorflow
  from PIL import Image
  import time
  import numpy
  import os
  import warnings
  from matplotlib.cbook import deprecated
  import json
label_desc: Use image node_id 10 for calling the ClassificationModelTrainer obj's underlying member method code with example usage: obj.lr_schedule(epoch) and returns: lr