def get_batch_normalization_updates(training_graph, allow_duplicates=False):
 from ..bricks import BatchNormalization
 from ..filter import VariableFilter, get_application_call
 var_filter = VariableFilter(bricks=[BatchNormalization], roles=[OUTPUT])
 all_app_calls = map(get_application_call, var_filter(training_graph))
 train_app_calls = _training_mode_application_calls(all_app_calls)
 if (len(train_app_calls) == 0):
  raise ValueError('no training mode BatchNormalization applications found in graph')
 bricks = [c.application.brick for c in train_app_calls]
 if ((not allow_duplicates) and (not isdistinct(bricks))):
  raise ValueError('multiple applications of the same BatchNormalization brick; pass allow_duplicates = True to override this check')
 def extract_pair(brick_attribute, metadata_key, app_call):
  return (getattr(app_call.application.brick, brick_attribute), app_call.metadata[metadata_key])
 mean_pair = partial(extract_pair, 'population_mean', 'offset')
 stdev_pair = partial(extract_pair, 'population_stdev', 'divisor')
 return sum([([mean_pair(a), stdev_pair(a)] if (not a.application.brick.mean_only) else [mean_pair(a)]) for a in train_app_calls], [])

def get_batch_normalization_updates ( training_graph , allow_duplicates = False ) : from . . bricks import BatchNormalization from . . filter import VariableFilter , get_application_call var_filter = VariableFilter ( bricks = [ BatchNormalization ] , roles = [ OUTPUT ] ) all_app_calls = map ( get_application_call , var_filter ( training_graph ) ) train_app_calls = _training_mode_application_calls ( all_app_calls ) if ( len ( train_app_calls ) == 0 ) : raise ValueError ( 'no training mode BatchNormalization applications found in graph' ) bricks = [ c . application . brick for c in train_app_calls ] if ( ( not allow_duplicates ) and ( not isdistinct ( bricks ) ) ) : raise ValueError ( 'multiple applications of the same BatchNormalization brick; pass allow_duplicates = True to override this check' ) def extract_pair ( brick_attribute , metadata_key , app_call ) : return ( getattr ( app_call . application . brick , brick_attribute ) , app_call . metadata [ metadata_key ] ) mean_pair = partial ( extract_pair , 'population_mean' , 'offset' ) stdev_pair = partial ( extract_pair , 'population_stdev' , 'divisor' ) return sum ( [ ( [ mean_pair ( a ) , stdev_pair ( a ) ] if ( not a . application . brick . mean_only ) else [ mean_pair ( a ) ] ) for a in train_app_calls ] , [ ] )
extract correspondences for learning bn population statistics .