Allow uers to specify the name of moving mean and variance in batch_norm interface.

[qingqing01]

Fix #8068

[chengduoZH]

LGTM!
V2 supports share parameters by name.

[chengduoZH]

helper.moving_mean_name ==> moving_mean_name

helper doesn't have this property.

Paddle/python/paddle/v2/fluid/layer_helper.py

Lines 24 to 107 in 8894c67

	class LayerHelper(object):
	def __init__(self, layer_type, **kwargs):
	self.kwargs = kwargs
	self.layer_type = layer_type
	name = self.kwargs.get('name', None)
	if name is None:
	self.kwargs['name'] = unique_name(self.layer_type)
	@property
	def name(self):
	return self.kwargs['name']
	@property
	def main_program(self):
	return default_main_program()
	@property
	def startup_program(self):
	return default_startup_program()
	def append_op(self, *args, **kwargs):
	return self.main_program.current_block().append_op(*args, **kwargs)
	def multiple_input(self, input_param_name='input'):
	inputs = self.kwargs.get(input_param_name, [])
	type_error = TypeError(
	"Input of {0} layer should be Variable or sequence of Variable".
	format(self.layer_type))
	if isinstance(inputs, Variable):
	inputs = [inputs]
	elif not isinstance(inputs, list) and not isinstance(inputs, tuple):
	raise type_error
	else:
	for each in inputs:
	if not isinstance(each, Variable):
	raise type_error
	return inputs
	def input(self, input_param_name='input'):
	inputs = self.multiple_input(input_param_name)
	if len(inputs) != 1:
	raise "{0} layer only takes one input".format(self.layer_type)
	return inputs[0]
	@property
	def param_attr(self):
	return ParamAttr.to_attr(self.kwargs.get('param_attr', None))
	@property
	def bias_attr(self):
	return ParamAttr.to_attr(self.kwargs.get('bias_attr', None))
	def multiple_param_attr(self, length):
	param_attr = self.param_attr
	if isinstance(param_attr, ParamAttr):
	param_attr = [param_attr]
	if len(param_attr) != 1 and len(param_attr) != length:
	raise ValueError("parameter number mismatch")
	elif len(param_attr) == 1 and length != 1:
	tmp = [None] * length
	for i in xrange(length):
	tmp[i] = copy.deepcopy(param_attr[0])
	param_attr = tmp
	return param_attr
	def iter_inputs_and_params(self, input_param_name='input'):
	inputs = self.multiple_input(input_param_name)
	param_attrs = self.multiple_param_attr(len(inputs))
	for ipt, param_attr in itertools.izip(inputs, param_attrs):
	yield ipt, param_attr
	def input_dtype(self, input_param_name='input'):
	inputs = self.multiple_input(input_param_name)
	dtype = None
	for each in inputs:
	if dtype is None:
	dtype = each.dtype
	elif dtype != each.dtype:
	raise ValueError("Data Type mismatch: %d to %d" %
	(dtype, each.dtype))
	return dtype
	def _create_weight_normalize(self, attr, shape, dtype):

[qingqing01]

@chengduoZH

In line 1488:

helper = LayerHelper('batch_norm', **locals())

 class LayerHelper(object): def __init__(self, layer_type, **kwargs): self.kwargs = kwargs 

So helper.moving_mean_name is no problem.

[qingqing01]

@chengduoZH Done. change helper.moving_mean_name to moving_mean_name

helper doesn't moving_mean_name .

[jacquesqiao]

if this name is still used?

[jacquesqiao]

LGTM!