zoo/wrn/wrn_c.py
def learner(self, x, **metaparameters):
        """ Construct the Learner
            x     : input to the learner
            groups: number of filters per group
            depth : number of convolutional layers
        """
        groups = metaparameters['groups']
        depth  = metaparameters['depth']

        # calculate the number of blocks from the depth   
        n_blocks = (depth - 4) // 6

        # first group, the projection block is not strided
        x = self.group(x, n_blocks=n_blocks, strides=(1, 1), **groups.pop(0), **metaparameters)
        
        # remaining groups
        for group in groups:
            x = self.group(x, n_blocks=n_blocks, strides=(2, 2), **group, **metaparameters)
        return x
 Construct the Learner
            x     : input to the learner
            groups: number of filters per group
            depth : number of convolutional layers
        
Construct the Learner
x     : input to the learner
groups: number of filters per group
depth : number of convolutional layers
Construct the Learner x : input to the learner groups : number of filters per group depth : number of convolutional layers
def learner(self, x, **metaparameters):
        groups = metaparameters['groups']
        depth  = metaparameters['depth']
        n_blocks = (depth - 4) // 6
        x = self.group(x, n_blocks=n_blocks, strides=(1, 1), **groups.pop(0), **metaparameters)
        for group in groups:
            x = self.group(x, n_blocks=n_blocks, strides=(2, 2), **group, **metaparameters)
        return x
def learner ( self , x , ** metaparameters ) : groups = metaparameters [ 'groups' ] depth = metaparameters [ 'depth' ] n_blocks = ( depth - 4 ) // 6 x = self . group ( x , n_blocks = n_blocks , strides = ( 1 , 1 ) , ** groups . pop ( 0 ) , ** metaparameters ) for group in groups : x = self . group ( x , n_blocks = n_blocks , strides = ( 2 , 2 ) , ** group , ** metaparameters ) return x
Construct the Learner
x     : input to the learner
groups: number of filters per group
depth : number of convolutional layers
Construct the Learner x : input to the learner groups : number of filters per group depth : number of convolutional layers
""" Construct the Learner
            x     : input to the learner
            groups: number of filters per group
            depth : number of convolutional layers
        """ # calculate the number of blocks from the depth    # first group, the projection block is not strided # remaining groups
self
x