#?coding:utf-8
#?by?chen?yh

import?caffe
import?numpy?as?np
import?matplotlib.pyplot?as?plt

def?weight_0（prototxtmodellayerthreshold）:
????caffe.set_mode_gpu（）
????net=caffe.Net（prototxtmodelcaffe.TEST）
????weight?=?net.params[layer][0].data
????bias?=?net.params[layer][1].data

????sum_l1=[]
????for?i?in?range（weight.shape[0]）:
????????for?j?in?range（weight.shape[1]）:
????????????sum_l1.append（（ijnp.sum（abs（weight[ij::]））））#i是核的順序j是每個(gè)卷積核與前面某個(gè)channel的連接順序求出每個(gè)連接的類似于L1范數(shù)的權(quán)重和加上ij是為了后續(xù)判斷weight的時(shí)候好直接處理到原weight

????display（sum_l1128）#從小到大排序后打印出前128個(gè)L1范數(shù)

????l1_plot（sum_l1）#畫出L1范數(shù)關(guān)于out*input的坐標(biāo)圖以確定多少個(gè)需要修剪.

????weight_l1?=?[]
????for?i?in?sum_l1:
????????weight_l1.append（i[2]）?#得到僅含有l(wèi)1范數(shù)的列表
????for?iweight_sum?in?enumerate（weight_l1）:
????????if?weight_sum?