message: Fix some doc strings that were accidentally removed.
diff: @@ -29,7 +29,7 @@ def _create_graph(input_graph=None,
  activation_bits=8,
  quant_delay=None,
  freeze_bn_delay=None):
  - """Returns a transformed training input_graph for simulated quantization.
  + """Rewrites an input_graph in place for simulated quantization.
  The graph has fake quantization ops inserted to simulate the error
  introduced by quantization. Since the graph is transformed in place,
  @@ -45,10 +45,9 @@ def _create_graph(input_graph=None,
  quant_delay: Number of steps after which weights and activations are
  quantized during training.
  freeze_bn_delay: Number of steps after which moving mean and variance are
  - frozen and used instead of batch statistics during
  - training. freeze_bn_delay should be greater than
  - quant_delay and should correspond to the number of steps
  - when training has almost converged
  + frozen and used instead of batch statistics during training.
  + freeze_bn_delay should be greater than quant_delay and should correspond
  + to the number of steps when training has almost converged
  Raises:
  ValueError: If elements contains an element that isn't a tf.Tensor or
  @@ -120,7 +119,6 @@ def create_eval_graph(input_graph=None):
  input_graph: The tf.Graph to be transformed, if None then defaults to the
  default graph.
  -
  Raises:
  ValueError: If elements contains an element that isn't a tf.Tensor or
  tf.Operation.
  @@ -133,7 +131,10 @@ def experimental_create_training_graph(input_graph=None,
  activation_bits=8,
  quant_delay=250000,
  freeze_bn_delay=500000):
  - """Returns a transformed training input_graph for simulated quantization.
  + """Rewrites a training input_graph in place for simulated quantization.
  +
  + This function has additional experimental options not (yet) available to
  + create_training_graph. The resulting behavior may be undefined.
  The graph has fake quantization ops inserted to simulate the error
  introduced by quantization. Since the graph is transformed in place,
  @@ -145,14 +146,12 @@ def experimental_create_training_graph(input_graph=None,
  default graph.
  weight_bits: Number of bits to use for quantizing weights.
  activation_bits: Number of bits to use for quantizing activations.
  - quant_delay: Number of steps after which weights and
  - activations are quantized during training.
  + quant_delay: Number of steps after which weights and activations are
  + quantized during training.
  freeze_bn_delay: Number of steps after which moving mean and variance are
  - frozen and used instead of batch statistics during
  - training. freeze_bn_delay should be greater than
  - quant_delay and should correspond to when training
  - has almost converged
  -
  + frozen and used instead of batch statistics during training.
  + freeze_bn_delay should be greater than quant_delay and should correspond
  + to when training has almost converged
  Raises:
  ValueError: If elements contains an element that isn't a tf.Tensor or
  @@ -171,7 +170,10 @@ def experimental_create_training_graph(input_graph=None,
  def experimental_create_eval_graph(input_graph=None,
  weight_bits=8,
  activation_bits=8):
  - """Returns a transformed eval input_graph for simulated quantization.
  + """Rewrites an eval input_graph in place for simulated quantization.
  +
  + This function has additional experimental options not (yet) available to
  + create_eval_graph. The resulting behavior may be undefined.
  The graph has fake quantization ops inserted to simulate the error
  introduced by quantization. Since the graph is transformed in place,
  