message: Disable freeze_bn_delay by default.
diff: @@ -99,16 +99,7 @@ def create_training_graph(input_graph=None, quant_delay=0):
  # TODO(raghuramank) Need to have freeze_bn_delay be a function of batch size
  # Currently the values below are hardcoded for mobilenetV1 on imagenet
  # Please use the experimental API if you need to tune these values.
  - if quant_delay == 0:
  - # Corresponds to case of restoring from a floating point checkpoint
  - # In this case, we can freeze the moving mean and variance early on and
  - # switch to using them during training. Therefore, freeze_bn_delay is set to
  - # 2e5.
  - freeze_bn_delay = int(2e5)
  - else:
  - # If training from scratch, set freeze_bn_delay to 100 epochs after quant
  - # delay. With a batch size of 64, this corresponds to 20000*100=2M steps.
  - freeze_bn_delay = quant_delay + int(2e6)
  + freeze_bn_delay = None
  _create_graph(
  input_graph=input_graph,
  @@ -142,7 +133,7 @@ def experimental_create_training_graph(input_graph=None,
  weight_bits=8,
  activation_bits=8,
  quant_delay=0,
  - freeze_bn_delay=int(2e5)):
  + freeze_bn_delay=None):
  """Rewrites a training input_graph in place for simulated quantization.
  Variables added by the rewrite get added to the global variables collection.
  