# -*- 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 Github: 245885195@qq.com Date: 2017.9.20 - - - - - -- - - - - - - - - - - - - - - - - - - - - - - """ import pickle import numpy as np import matplotlib.pyplot as plt class CNN: 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): # save model dict with 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("Model saved： %s" % save_path) @classmethod def ReadModel(cls, model_path): # read saved model 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") # create model instance conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t) # 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") 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) 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] # 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) # 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): 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) # 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 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 = [] 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, ] 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): # 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) data_expanded = np.asarray(data_expanded) return data_expanded 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 _calculate_gradient_from_pool( self, out_map, pd_pool, num_map, size_map, size_pooling ): """ 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 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 train( 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))) rp = 0 all_mse = [] mse = 10000 while rp < n_repeat and mse >= error_accuracy: alle = 0 print("-------------Learning Time %d--------------" % rp) for p in range(len(datas_train)): # 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, 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) bp_out1 = data_bp_input bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2 bp_out2 = self.sig(bp_net_j) bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3 bp_out3 = self.sig(bp_net_k) # --------------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._calculate_gradient_from_pool( data_conved1, pd_conv1_pooled, shape_featuremap1[0], shape_featuremap1[1], self.size_pooling1, ) # 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]) 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 ) # all connected layer self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre # calculate the sum error of all single image 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 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)): 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) bp_out1 = data_bp_input bp_net_j = bp_out1 * self.vji.T - self.thre_bp2 bp_out2 = self.sig(bp_net_j) 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): # 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, self.thre_conv1, conv_step=self.step_conv1, ) data_pooled1 = self.pooling(data_conved1, self.size_pooling1) return data_conved1, data_pooled1 if __name__ == "__main__": """ I will put the example on other file """