message: Make the default values for experimental and non experimental apis match.
diff: @@ -69,7 +69,7 @@ def _create_graph(input_graph=None,
  activation_bits=activation_bits)
  -def create_training_graph(input_graph=None, quant_delay=250000):
  +def create_training_graph(input_graph=None, quant_delay=0):
  """Rewrites a training input_graph in place for simulated quantization.
  The graph has fake quantization ops inserted to simulate the error
  @@ -77,6 +77,14 @@ def create_training_graph(input_graph=None, quant_delay=250000):
  the expected behavior of previously held references to nodes and tensors may
  change.
  + The default value of quant_delay is suitable for finetuning an already trained
  + floating point model (recommended).
  + If one wants to train a quantized model from scratch, quant_delay should be
  + set to the number of steps it take the floating point model to converge.
  + Quantization will be activated at this point and effectively finetune the
  + model. If quant_delay is not provided when training from scratch, training can
  + often fail.
  +
  Args:
  input_graph: The tf.Graph to be transformed.
  quant_delay: Number of steps after which weights and activations are
  @@ -93,12 +101,12 @@ def create_training_graph(input_graph=None, quant_delay=250000):
  # 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
  - # 200000
  - freeze_bn_delay = 200000
  + # 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 + 2000000
  + freeze_bn_delay = quant_delay + int(2e6)
  _create_graph(
  input_graph=input_graph,
  @@ -129,8 +137,8 @@ def create_eval_graph(input_graph=None):
  def experimental_create_training_graph(input_graph=None,
  weight_bits=8,
  activation_bits=8,
  - quant_delay=250000,
  - freeze_bn_delay=500000):
  + quant_delay=0,
  + freeze_bn_delay=int(2e5)):
  """Rewrites a training input_graph in place for simulated quantization.
  This function has additional experimental options not (yet) available to
  @@ -141,6 +149,14 @@ def experimental_create_training_graph(input_graph=None,
  the expected behavior of previously held references to nodes and tensors may
  change.
  + The default value of quant_delay is suitable for finetuning an already trained
  + floating point model (recommended).
  + If one wants to train a quantized model from scratch, quant_delay should be
  + set to the number of steps it take the floating point model to converge.
  + Quantization will be activated at this point and effectively finetune the
  + model. If quant_delay is not provided when training from scratch, training can
  + often fail.
  +
  Args:
  input_graph: The tf.Graph to be transformed,if None then defaults to the
  default graph.
  