def convresblock(x, nfeats=8, ksize=3, nskipped=2):
 y0 = Convolution2D(nfeats, ksize, ksize, border_mode='same')(x)
 y = y0
 for i in range(nskipped):
  y = BatchNormalization(mode=0, axis=1)(y)
  y = Activation('relu')(y)
  y = Convolution2D(nfeats, ksize, ksize, border_mode='same')(y)
 return merge([y0, y], mode='sum')

def convresblock ( x , nfeats = 8 , ksize = 3 , nskipped = 2 ) : y0 = Convolution2D ( nfeats , ksize , ksize , border_mode = 'same' ) ( x ) y = y0 for i in range ( nskipped ) : y = BatchNormalization ( mode = 0 , axis = 1 ) ( y ) y = Activation ( 'relu' ) ( y ) y = Convolution2D ( nfeats , ksize , ksize , border_mode = 'same' ) ( y ) return merge ( [ y0 , y ] , mode = 'sum' )
the proposed residual block from [4] .