def deeper2net_conv2d(teacher_w):
 (nb_filter, nb_channel, kh, kw) = teacher_w.shape
 student_w = np.zeros((nb_filter, nb_filter, kh, kw))
 for i in xrange(nb_filter):
  student_w[(i, i, ((kh - 1) / 2), ((kw - 1) / 2))] = 1.0
 student_b = np.zeros(nb_filter)
 return (student_w, student_b)

def deeper2net_conv2d ( teacher_w ) : ( nb_filter , nb_channel , kh , kw ) = teacher_w . shape student_w = np . zeros ( ( nb_filter , nb_filter , kh , kw ) ) for i in xrange ( nb_filter ) : student_w [ ( i , i , ( ( kh - 1 ) / 2 ) , ( ( kw - 1 ) / 2 ) ) ] = 1.0 student_b = np . zeros ( nb_filter ) return ( student_w , student_b )
get initial weights for a deeper conv2d layer by net2deeper .