From 52ee9a1e128c137793f08f69f4835b67f3b9c5d1 Mon Sep 17 00:00:00 2001 From: Stephen Lee <245885195@qq.com> Date: Fri, 22 Sep 2017 14:34:28 +0800 Subject: [PATCH 1/2] commit convolution_neural_network.py --- Neural_Network/convolution_neural_network.py | 343 +++++++++++++++++++ 1 file changed, 343 insertions(+) create mode 100644 Neural_Network/convolution_neural_network.py diff --git a/Neural_Network/convolution_neural_network.py b/Neural_Network/convolution_neural_network.py new file mode 100644 index 000000000..539c315a0 --- /dev/null +++ b/Neural_Network/convolution_neural_network.py @@ -0,0 +1,343 @@ +#-*- 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 + + From 6e61ac19cd50288175b7b2c94d93133adc69492d Mon Sep 17 00:00:00 2001 From: Stephen Lee <245885195@qq.com> Date: Fri, 22 Sep 2017 14:56:20 +0800 Subject: [PATCH 2/2] commit convolution_neural_network.py --- Neural_Network/convolution_neural_network.py | 158 +++++++------------ 1 file changed, 60 insertions(+), 98 deletions(-) diff --git a/Neural_Network/convolution_neural_network.py b/Neural_Network/convolution_neural_network.py index 539c315a0..d8ab0d2e5 100644 --- a/Neural_Network/convolution_neural_network.py +++ b/Neural_Network/convolution_neural_network.py @@ -11,30 +11,15 @@ * Hiden layer of BP * Output layer of BP Author: Stephen Lee - Program: PYTHON + Github: 245885195@qq.com 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): ''' @@ -63,7 +48,7 @@ class CNN(): def save_model(self,save_path): - #将模型保存 + #save model dict with pickle import pickle model_dic = {'num_bp1':self.num_bp1, 'num_bp2':self.num_bp2, @@ -82,35 +67,11 @@ class CNN(): 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') + print('Model saved： %s'% save_path) @classmethod def ReadModel(cls,model_path): - #类方法，读取保存的模型，返回一个实例。 + #read saved model import pickle with open(model_path, 'rb') as f: model_dic = pickle.load(f) @@ -123,9 +84,9 @@ class CNN(): bp3 = model_dic.get('num_bp3') r_w = model_dic.get('rate_weight') r_t = model_dic.get('rate_thre') - #创建实例 + #create model instance conv_ins = CNN(conv_get,size_p1,bp1,bp2,bp3,r_w,r_t) - #修改实例的参数 + #modify model parameter conv_ins.w_conv1 = model_dic.get('w_conv1') conv_ins.wkj = model_dic.get('wkj') conv_ins.vji = model_dic.get('vji') @@ -137,20 +98,22 @@ class CNN(): 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): + + def convolute(self,data,convs,w_convs,thre_convs,conv_step): + #convolution process 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 + #get the data slice of original image data, 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 + #caculate the feature map of every single kernel, and saved as list of matrix data_featuremap = [] Size_FeatureMap = int((size_data - size_conv) / conv_step + 1) for i_map in range(num_conv): @@ -161,15 +124,15 @@ class CNN(): featuremap = np.asmatrix(featuremap).reshape(Size_FeatureMap, Size_FeatureMap) data_featuremap.append(featuremap) - #将data_focus中的focus展开为一维 + #expanding the data slice to One dimenssion 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): + def pooling(self,featuremaps,size_pooling,type='average_pool'): + #pooling process size_map = len(featuremaps[0]) size_pooled = int(size_map/size_pooling) featuremap_pooled = [] @@ -179,39 +142,40 @@ class CNN(): 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)) + if type == 'average_pool': + #average pooling + map_pooled.append(np.average(focus)) + elif type == 'max_pooling': + #max pooling + 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 + def _expand(self,datas): + #expanding three dimension data to one dimension 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 + def _expand_mat(self,data_mat): + #expanding matrix to one dimension 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): + def _calculate_gradient_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 + calcluate the gradient from the data slice of pool layer + pd_pool: list of matrix + out_map: the shape of data slice(size_map*size_map) + return: pd_all: list of matrix, [num, size_map, size_map] ''' pd_all = [] i_pool = 0 @@ -226,6 +190,7 @@ class CNN(): return pd_all def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e = bool): + #model traning print('----------------------Start Training-------------------------') print(' - - Shape: Train_Data ',np.shape(datas_train)) print(' - - Shape: Teach_Data ',np.shape(datas_teach)) @@ -234,58 +199,53 @@ class CNN(): mse = 10000 while rp < n_repeat and mse >= error_accuracy: alle = 0 - print('-------------进行第%d次学习--------------'%rp) + print('-------------Learning Time %d--------------'%rp) for p in range(len(datas_train)): - #print('------------学习第%d个图像--------------'%p) + #print('------------Learning Image: %d--------------'%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, + 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) + 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 + data_bp_input = self._expand(data_pooled1) 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 + #--------------Model Leaning ------------------------ + # calcluate error and gradient--------------- 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], + pd_conv1_all = self._calculate_gradient_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 + #weight and threshold learning process--------- + #convolution layer for k_conv in range(self.conv1[1]): - pd_conv_list = self.Expand_Mat(pd_conv1_all[k_conv]) + 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 - + #all connected layer 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 + # calculate the sum error of all single image errors = np.sum(abs((data_teach - bp_out3))) alle = alle + errors #print(' ----Teach ',data_teach) @@ -307,24 +267,21 @@ class CNN(): draw_error() return mse - def produce(self,datas_test): - #对验证和测试数据集进行输出 + def predict(self,datas_test): + #model predict 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, + 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 + data_pooled1 = self.pooling(data_conved1, self.size_pooling1) + data_bp_input = self._expand(data_pooled1) + 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()) @@ -332,12 +289,17 @@ class CNN(): 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 + #return the data of image after convoluting process so we can check it out data_test = np.asmatrix(data) - data_focus1, data_conved1 = self.Convolute(data_test, self.conv1, self.w_conv1, + 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_pooled1 = self.pooling(data_conved1, self.size_pooling1) return data_conved1,data_pooled1 +if __name__ == '__main__': + pass + ''' + I will put the example on other file + ''' \ No newline at end of file