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Stephen Lee 2017-09-22 14:34:28 +08:00
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#-*- coding: utf-8 -*-
'''
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - CNN - Convolution Neural Network For Photo Recognizing
Goal - - Recognize Handing Writting Word Photo
Detail：Total 5 layers neural network
* Convolution layer
* Pooling layer
* Input layer layer of BP
* Hiden layer of BP
* Output layer of BP
Author: Stephen Lee
Program: PYTHON
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
'''
import numpy as np
import matplotlib.pyplot as plt
class CNN():
conv1 = []
w_conv1 = []
thre_conv1 = []
step_conv1 = 0
size_pooling1 = 0
num_bp1 = 0
num_bp2 = 0
num_bp3 = 0
thre_bp1 = []
thre_bp2 = []
wkj = np.mat([])
vji = np.mat([])
rate_weight = 0
rate_thre = 0
def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.2):
'''
:param conv1_get: [a,c,d]，size, number, step of convolution kernel
:param size_p1: pooling size
:param bp_num1: units number of flatten layer
:param bp_num2: units number of hidden layer
:param bp_num3: units number of output layer
:param rate_w: rate of weight learning
:param rate_t: rate of threshold learning
'''
self.num_bp1 = bp_num1
self.num_bp2 = bp_num2
self.num_bp3 = bp_num3
self.conv1 = conv1_get[:2]
self.step_conv1 = conv1_get[2]
self.size_pooling1 = size_p1
self.rate_weight = rate_w
self.rate_thre = rate_t
self.w_conv1 = [np.mat(-1*np.random.rand(self.conv1[0],self.conv1[0])+0.5) for i in range(self.conv1[1])]
self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
self.vji = np.mat(-1*np.random.rand(self.num_bp2, self.num_bp1)+0.5)
self.thre_conv1 = -2*np.random.rand(self.conv1[1])+1
self.thre_bp2 = -2*np.random.rand(self.num_bp2)+1
self.thre_bp3 = -2*np.random.rand(self.num_bp3)+1
def save_model(self,save_path):
#将模型保存
import pickle
model_dic = {'num_bp1':self.num_bp1,
'num_bp2':self.num_bp2,
'num_bp3':self.num_bp3,
'conv1':self.conv1,
'step_conv1':self.step_conv1,
'size_pooling1':self.size_pooling1,
'rate_weight':self.rate_weight,
'rate_thre':self.rate_thre,
'w_conv1':self.w_conv1,
'wkj':self.wkj,
'vji':self.vji,
'thre_conv1':self.thre_conv1,
'thre_bp2':self.thre_bp2,
'thre_bp3':self.thre_bp3}
with open(save_path, 'wb') as f:
pickle.dump(model_dic, f)
print('模型已经保存： %s'% save_path)
def paste_model(self,save_path):
#实例方法，
#虽然这么写一点也不简洁。。。。
#卸载这个里面的话，只是用于修改已经存在的模型，要根据读取的数据返回实例的模型，再写一个吧
import pickle
with open(save_path, 'rb') as f:
model_dic = pickle.load(f)
self.num_bp1 = model_dic.get('num_bp1')
self.num_bp2 = model_dic.get('num_bp2')
self.num_bp3 = model_dic.get('num_bp3')
self.conv1 = model_dic.get('conv1')
self.step_conv1 = model_dic.get('step_conv1')
self.size_pooling1 = model_dic.get('size_pooling1')
self.rate_weight = model_dic.get('rate_weight')
self.rate_thre = model_dic.get('rate_thre')
self.w_conv1 = model_dic.get('w_conv1')
self.wkj = model_dic.get('wkj')
self.vji = model_dic.get('vji')
self.thre_conv1 = model_dic.get('thre_conv1')
self.thre_bp2 = model_dic.get('thre_bp2')
self.thre_bp3 = model_dic.get('thre_bp3')
print('已经成功读取模型')
@classmethod
def ReadModel(cls,model_path):
#类方法，读取保存的模型，返回一个实例。
import pickle
with open(model_path, 'rb') as f:
model_dic = pickle.load(f)
conv_get= model_dic.get('conv1')
conv_get.append(model_dic.get('step_conv1'))
size_p1 = model_dic.get('size_pooling1')
bp1 = model_dic.get('num_bp1')
bp2 = model_dic.get('num_bp2')
bp3 = model_dic.get('num_bp3')
r_w = model_dic.get('rate_weight')
r_t = model_dic.get('rate_thre')
#创建实例
conv_ins = CNN(conv_get,size_p1,bp1,bp2,bp3,r_w,r_t)
#修改实例的参数
conv_ins.w_conv1 = model_dic.get('w_conv1')
conv_ins.wkj = model_dic.get('wkj')
conv_ins.vji = model_dic.get('vji')
conv_ins.thre_conv1 = model_dic.get('thre_conv1')
conv_ins.thre_bp2 = model_dic.get('thre_bp2')
conv_ins.thre_bp3 = model_dic.get('thre_bp3')
return conv_ins
def sig(self,x):
return 1 / (1 + np.exp(-1*x))
def do_round(self,x):
return round(x, 3)
#卷积
def Convolute(self,data,convs,w_convs,thre_convs,conv_step):
size_conv = convs[0]
num_conv =convs[1]
size_data = np.shape(data)[0]
#得到原图像滑动的小图，data_focus
data_focus = []
for i_focus in range(0, size_data - size_conv + 1, conv_step):
for j_focus in range(0, size_data - size_conv + 1, conv_step):
focus = data[i_focus:i_focus + size_conv, j_focus:j_focus + size_conv]
data_focus.append(focus)
#计算所有卷积核得到的特征图，每个特征图以矩阵形式，存储为一个列表data_featuremap
data_featuremap = []
Size_FeatureMap = int((size_data - size_conv) / conv_step + 1)
for i_map in range(num_conv):
featuremap = []
for i_focus in range(len(data_focus)):
net_focus = np.sum(np.multiply(data_focus[i_focus], w_convs[i_map])) - thre_convs[i_map]
featuremap.append(self.sig(net_focus))
featuremap = np.asmatrix(featuremap).reshape(Size_FeatureMap, Size_FeatureMap)
data_featuremap.append(featuremap)
#将data_focus中的focus展开为一维
focus1_list = []
for each_focus in data_focus:
focus1_list.extend(self.Expand_Mat(each_focus))
focus_list = np.asarray(focus1_list)
return focus_list,data_featuremap
# 池化
def Pooling(self,featuremaps,size_pooling):
size_map = len(featuremaps[0])
size_pooled = int(size_map/size_pooling)
featuremap_pooled = []
for i_map in range(len(featuremaps)):
map = featuremaps[i_map]
map_pooled = []
for i_focus in range(0,size_map,size_pooling):
for j_focus in range(0, size_map, size_pooling):
focus = map[i_focus:i_focus + size_pooling, j_focus:j_focus + size_pooling]
#平均池化
map_pooled.append(np.average(focus))
#最大池化
#map_pooled.append(np.max(focus))
map_pooled = np.asmatrix(map_pooled).reshape(size_pooled,size_pooled)
featuremap_pooled.append(map_pooled)
return featuremap_pooled
def Expand(self,datas):
#将三元的数据展开为1为的list
data_expanded = []
for i in range(len(datas)):
shapes = np.shape(datas[i])
data_listed = datas[i].reshape(1,shapes[0]*shapes[1])
data_listed = data_listed.getA().tolist()[0]
data_expanded.extend(data_listed)
#连接所有数据
data_expanded = np.asarray(data_expanded)
return data_expanded
def Expand_Mat(self,data_mat):
#用来展开矩阵为一维的list
data_mat = np.asarray(data_mat)
shapes = np.shape(data_mat)
data_expanded = data_mat.reshape(1,shapes[0]*shapes[1])
return data_expanded
def Getpd_From_Pool(self,out_map,pd_pool,num_map,size_map,size_pooling):
'''
误差反传，从pooled到前一个map, 例如将池化层6*6的误差矩阵扩大为12*12的误差矩阵
pd_pool: 是采样层的误差，list形式。。。。要改要改
out_map: 前面特征图的输出，数量*size*size的列表形式
return: pd_all:前面层所有的特征图的pd， num*size_map*size_map的列表形式
'''
pd_all = []
i_pool = 0
for i_map in range(num_map):
pd_conv1 = np.ones((size_map, size_map))
for i in range(0, size_map, size_pooling):
for j in range(0, size_map, size_pooling):
pd_conv1[i:i + size_pooling, j:j + size_pooling] = pd_pool[i_pool]
i_pool = i_pool + 1
pd_conv2 = np.multiply(pd_conv1,np.multiply(out_map[i_map],(1-out_map[i_map])))
pd_all.append(pd_conv2)
return pd_all
def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e = bool):
print('----------------------Start Training-------------------------')
print(' - - Shape: Train_Data ',np.shape(datas_train))
print(' - - Shape: Teach_Data ',np.shape(datas_teach))
rp = 0
all_mse = []
mse = 10000
while rp < n_repeat and mse >= error_accuracy:
alle = 0
print('-------------进行第%d次学习--------------'%rp)
for p in range(len(datas_train)):
#print('------------学习第%d个图像--------------'%p)
data_train = np.asmatrix(datas_train[p])
data_teach = np.asarray(datas_teach[p])
data_focus1,data_conved1 = self.Convolute(data_train,self.conv1,self.w_conv1,
self.thre_conv1,conv_step=self.step_conv1)
data_pooled1 = self.Pooling(data_conved1,self.size_pooling1)
shape_featuremap1 = np.shape(data_conved1)
'''
print(' -----original shape ', np.shape(data_train))
print(' ---- after convolution ',np.shape(data_conv1))
print(' -----after pooling ',np.shape(data_pooled1))
'''
data_bp_input = self.Expand(data_pooled1)
# 计算第一层输入输出
bp_out1 = data_bp_input
# 计算第二层输入输出
bp_net_j = np.dot(bp_out1,self.vji.T) - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
# 计算第三层输入输出
bp_net_k = np.dot(bp_out2 ,self.wkj.T) - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
# 计算一般化误差
pd_k_all = np.multiply((data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3)))
pd_j_all = np.multiply(np.dot(pd_k_all,self.wkj), np.multiply(bp_out2, (1 - bp_out2)))
pd_i_all = np.dot(pd_j_all,self.vji)
pd_conv1_pooled = pd_i_all / (self.size_pooling1*self.size_pooling1)
pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
pd_conv1_all = self.Getpd_From_Pool(data_conved1,pd_conv1_pooled,shape_featuremap1[0],
shape_featuremap1[1],self.size_pooling1)
#卷积层1的权重和阈值修正，每个卷积核的权重需要修正 12*12(map) 次
#修正量为featuremap中点的偏导值 乘以 前一层图像focus， 整个权重模板一起更新
for k_conv in range(self.conv1[1]):
pd_conv_list = self.Expand_Mat(pd_conv1_all[k_conv])
delta_w = self.rate_weight * np.dot(pd_conv_list,data_focus1)
self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape((self.conv1[0],self.conv1[0]))
self.thre_conv1[k_conv] = self.thre_conv1[k_conv] - np.sum(pd_conv1_all[k_conv]) * self.rate_thre
# 更新kj层的权重
self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
# 更新ji层的权重
self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
# 更新阈值
self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
# 计算总误差
errors = np.sum(abs((data_teach - bp_out3)))
alle = alle + errors
#print(' ----Teach ',data_teach)
#print(' ----BP_output ',bp_out3)
rp = rp + 1
mse = alle/patterns
all_mse.append(mse)
def draw_error():
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
plt.plot(all_mse, '+-')
plt.plot(yplot, 'r--')
plt.xlabel('Learning Times')
plt.ylabel('All_mse')
plt.grid(True, alpha=0.5)
plt.show()
print('------------------Training Complished---------------------')
print(' - - Training epoch: ', rp, ' - - Mse: %.6f' % mse)
if draw_e:
draw_error()
return mse
def produce(self,datas_test):
#对验证和测试数据集进行输出
produce_out = []
print('-------------------Start Testing-------------------------')
print(' - - Shape: Test_Data ',np.shape(datas_test))
for p in range(len(datas_test)):
print('--------测试第%d个图像----------' % p)
data_test = np.asmatrix(datas_test[p])
data_focus1, data_conved1 = self.Convolute(data_test, self.conv1, self.w_conv1,
self.thre_conv1, conv_step=self.step_conv1)
data_pooled1 = self.Pooling(data_conved1, self.size_pooling1)
data_bp_input = self.Expand(data_pooled1)
# 计算第一层输入输出
bp_out1 = data_bp_input
# 计算第二层输入输出
bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
# 计算第三层输入输出
bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
produce_out.extend(bp_out3.getA().tolist())
res = [list(map(self.do_round,each)) for each in produce_out]
return np.asarray(res)
def convolution(self,data):
#返回卷积和池化后的数据,用于查看图像
data_test = np.asmatrix(data)
data_focus1, data_conved1 = self.Convolute(data_test, self.conv1, self.w_conv1,
self.thre_conv1, conv_step=self.step_conv1)
data_pooled1 = self.Pooling(data_conved1, self.size_pooling1)
return data_conved1,data_pooled1