zoo/shufflenet/shufflenet_c.py
def learner(self, x, **metaparameters):
        ''' Construct the Learner
            x            : input to the learner
            groups       : number of shuffle blocks per shuffle group
            n_parttitions: number of groups to partition feature maps (channels) into.
            filters      : number of filters per shuffle group
            reduction    : dimensionality reduction on entry to a shuffle block
        '''
        groups  = metaparameters['groups']
        filters = metaparameters['filters']

        # Assemble the shuffle groups
        for group in groups:
            x = self.group(x, **group, **filters.pop(0), **metaparameters)
        return x
 Construct the Learner
            x            : input to the learner
            groups       : number of shuffle blocks per shuffle group
            n_parttitions: number of groups to partition feature maps (channels) into.
            filters      : number of filters per shuffle group
            reduction    : dimensionality reduction on entry to a shuffle block
        
Construct the Learner
x            : input to the learner
groups       : number of shuffle blocks per shuffle group
n_parttitions: number of groups to partition feature maps (channels) into.
filters      : number of filters per shuffle group
reduction    : dimensionality reduction on entry to a shuffle block
Construct the Learner x : input to the learner groups : number of shuffle blocks per shuffle group n_parttitions : number of groups to partition feature maps ( channels ) into . filters : number of filters per shuffle group reduction : dimensionality reduction on entry to a shuffle block
def learner(self, x, **metaparameters):
        groups  = metaparameters['groups']
        filters = metaparameters['filters']
        for group in groups:
            x = self.group(x, **group, **filters.pop(0), **metaparameters)
        return x
def learner ( self , x , ** metaparameters ) : groups = metaparameters [ 'groups' ] filters = metaparameters [ 'filters' ] for group in groups : x = self . group ( x , ** group , ** filters . pop ( 0 ) , ** metaparameters ) return x
Construct the Learner
x            : input to the learner
groups       : number of shuffle blocks per shuffle group
n_parttitions: number of groups to partition feature maps (channels) into.
Construct the Learner x : input to the learner groups : number of shuffle blocks per shuffle group n_parttitions : number of groups to partition feature maps ( channels ) into .
''' Construct the Learner
            x            : input to the learner
            groups       : number of shuffle blocks per shuffle group
            n_parttitions: number of groups to partition feature maps (channels) into.
            filters      : number of filters per shuffle group
            reduction    : dimensionality reduction on entry to a shuffle block
        ''' # Assemble the shuffle groups
self
x