...

• create the session
• creating the op to initializing all the variables
• create the tf.Saver() object, we'll call it saver
• run the init op
  • saver automatically ties ops to computational graph to save variables
• call saver.save()
• For restoring, between creating the
  • create initialize variables op
  and running that op,
  • create saver
  • run init op
  • call saver.restore()

Code

• A BatchNormalization class that keeps track of additional training ops: BatchNormalization.py
• Driver program: ex06_tf_batchnorm.py. Note:
  
  • use of a new boolean flag placeholder
  • model class keeps list of instances of the batch normalization classes
  • model returns trainOps by querying all the batch normalization instances
  • trainOps are added to ops used during training