├── BP_regression ├── dynamic_bp_network_regression.py ├── generate_data.py ├── model │ ├── checkpoint │ ├── predict_model.data-00000-of-00001 │ ├── predict_model.index │ ├── predict_model.meta │ └── predict_model_parameter.txt ├── test.csv ├── test_predict.csv ├── train.csv └── validate.csv └── README.md /BP_regression/dynamic_bp_network_regression.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import pandas as pd 3 | import numpy as np 4 | createVar = locals() 5 | 6 | ''' 7 | 建立一个网络结构可变的BP神经网络通用代码： 8 | 9 | 在训练时各个参数的意义： 10 | hidden_floors_num：隐藏层的个数 11 | every_hidden_floor_num：每层隐藏层的神经元个数 12 | learning_rate：学习速率 13 | activation：激活函数 14 | regularization：正则化方式 15 | regularization_rate：正则化比率 16 | total_step：总的训练次数 17 | train_data_path：训练数据路径 18 | model_save_path：模型保存路径 19 | 20 | 利用训练好的模型对验证集进行验证时各个参数的意义： 21 | model_save_path：模型保存路径 22 | validate_data_path：验证集路径 23 | precision：精度 24 | 25 | 利用训练好的模型进行预测时各个参数的意义： 26 | model_save_path：模型的保存路径 27 | predict_data_path：预测数据路径 28 | predict_result_save_path：预测结果保存路径 29 | ''' 30 | 31 | 32 | # 训练模型全局参数 33 | hidden_floors_num = 1 34 | every_hidden_floor_num = [50] 35 | learning_rate = 0.00001 36 | activation = 'tanh' 37 | regularization = 'L1' 38 | regularization_rate = 0.0001 39 | total_step = 1000000 40 | train_data_path = 'C:/Users/Administrator/Desktop/BP_regression/train.csv' 41 | model_save_path = 'C:/Users/Administrator/Desktop/BP_regression/model/predict_model' 42 | 43 | # 利用模型对验证集进行验证返回正确率 44 | model_save_path = 'C:/Users/Administrator/Desktop/BP_regression/model/predict_model' 45 | validate_data_path = 'C:/Users/Administrator/Desktop/BP_regression/validate.csv' 46 | precision = 0.5 47 | 48 | # 利用模型进行预测全局参数 49 | model_save_path = 'C:/Users/Administrator/Desktop/BP_regression/model/predict_model' 50 | predict_data_path = 'C:/Users/Administrator/Desktop/BP_regression/test.csv' 51 | predict_result_save_path = 'C:/Users/Administrator/Desktop/BP_regression/test_predict.csv' 52 | 53 | 54 | def inputs(train_data_path): 55 | train_data = pd.read_csv(train_data_path) 56 | X = np.array(train_data.iloc[:, :-1]) 57 | Y = np.array(train_data.iloc[:, -1:]) 58 | return X, Y 59 | 60 | 61 | def make_hidden_layer(pre_lay_num, cur_lay_num, floor): 62 | createVar['w' + str(floor)] = tf.Variable(tf.random_normal([pre_lay_num, cur_lay_num], stddev=1)) 63 | createVar['b' + str(floor)] = tf.Variable(tf.random_normal([cur_lay_num], stddev=1)) 64 | return eval('w'+str(floor)), eval('b'+str(floor)) 65 | 66 | 67 | def initial_w_and_b(all_floors_num): 68 | # 初始化隐藏层的w, b 69 | for floor in range(2, hidden_floors_num+3): 70 | pre_lay_num = all_floors_num[floor-2] 71 | cur_lay_num = all_floors_num[floor-1] 72 | w_floor, b_floor = make_hidden_layer(pre_lay_num, cur_lay_num, floor) 73 | createVar['w' + str(floor)] = w_floor 74 | createVar['b' + str(floor)] = b_floor 75 | 76 | 77 | def cal_floor_output(x, floor): 78 | w_floor = eval('w'+str(floor)) 79 | b_floor = eval('b'+str(floor)) 80 | if activation == 'sigmoid': 81 | output = tf.sigmoid(tf.matmul(x, w_floor) + b_floor) 82 | if activation == 'tanh': 83 | output = tf.tanh(tf.matmul(x, w_floor) + b_floor) 84 | if activation == 'relu': 85 | output = tf.nn.relu(tf.matmul(x, w_floor) + b_floor) 86 | return output 87 | 88 | 89 | def inference(x): 90 | output = x 91 | for floor in range(2, hidden_floors_num+2): 92 | output = cal_floor_output(output, floor) 93 | 94 | floor = hidden_floors_num+2 95 | w_floor = eval('w'+str(floor)) 96 | b_floor = eval('b'+str(floor)) 97 | output = tf.matmul(output, w_floor) + b_floor 98 | return output 99 | 100 | 101 | def loss(x, y_real): 102 | y_pre = inference(x) 103 | if regularization == 'None': 104 | total_loss = tf.reduce_sum(tf.squared_difference(y_real, y_pre)) 105 | 106 | if regularization == 'L1': 107 | total_loss = 0 108 | for floor in range(2, hidden_floors_num + 3): 109 | w_floor = eval('w' + str(floor)) 110 | total_loss = total_loss + tf.contrib.layers.l1_regularizer(regularization_rate)(w_floor) 111 | total_loss = total_loss + tf.reduce_sum(tf.squared_difference(y_real, y_pre)) 112 | 113 | if regularization == 'L2': 114 | total_loss = 0 115 | for floor in range(2, hidden_floors_num + 3): 116 | w_floor = eval('w' + str(floor)) 117 | total_loss = total_loss + tf.contrib.layers.l2_regularizer(regularization_rate)(w_floor) 118 | total_loss = total_loss + tf.reduce_sum(tf.squared_difference(y_real, y_pre)) 119 | 120 | return total_loss 121 | 122 | 123 | def train(total_loss): 124 | train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss) 125 | return train_op 126 | 127 | 128 | # 训练模型 129 | def train_model(hidden_floors_num, every_hidden_floor_num, learning_rate, activation, regularization, 130 | regularization_rate, total_step, train_data_path, model_save_path): 131 | X, Y = inputs(train_data_path) 132 | X_dim = X.shape[1] 133 | all_floors_num = [X_dim] + every_hidden_floor_num + [1] 134 | 135 | # 将参数保存到和model_save_path相同的文件夹下， 恢复模型进行预测时加载这些参数创建神经网络 136 | temp = model_save_path.split('/') 137 | model_name = temp[-1] 138 | parameter_path = '' 139 | for i in range(len(temp)-1): 140 | parameter_path = parameter_path + temp[i] + '/' 141 | parameter_path = parameter_path + model_name + '_parameter.txt' 142 | with open(parameter_path, 'w') as f: 143 | f.write("all_floors_num:") 144 | for i in all_floors_num: 145 | f.write(str(i) + ' ') 146 | f.write('\n') 147 | f.write('activation:') 148 | f.write(str(activation)) 149 | 150 | x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) 151 | y_real = tf.placeholder(dtype=tf.float32, shape=[None, 1]) 152 | initial_w_and_b(all_floors_num) 153 | y_pre = inference(x) 154 | total_loss = loss(x, y_real) 155 | train_op = train(total_loss) 156 | 157 | # 记录在训练集上的正确率 158 | train_accuracy = tf.reduce_mean(tf.cast(tf.abs(y_pre - y_real) < precision, tf.float32)) 159 | 160 | # 保存模型 161 | saver = tf.train.Saver() 162 | 163 | # 在一个会话对象中启动数据流图，搭建流程 164 | sess = tf.Session() 165 | init = tf.global_variables_initializer() 166 | sess.run(init) 167 | for step in range(total_step): 168 | sess.run([train_op], feed_dict={x: X[0:, :], y_real: Y[0:, :]}) 169 | if step % 1000 == 0: 170 | saver.save(sess, model_save_path) 171 | total_loss_value = sess.run(total_loss, feed_dict={x: X[0:, :], y_real: Y[0:, :]}) 172 | print('train step is ', step, ', total loss value is ', total_loss_value, 173 | ', train_accuracy', sess.run(train_accuracy, feed_dict={x: X, y_real: Y}), 174 | ', precision is ', precision) 175 | 176 | saver.save(sess, model_save_path) 177 | sess.close() 178 | 179 | 180 | def validate(model_save_path, validate_data_path, precision): 181 | # **********************根据model_save_path推出模型参数路径, 解析出all_floors_num和activation**************** 182 | temp = model_save_path.split('/') 183 | model_name = temp[-1] 184 | parameter_path = '' 185 | for i in range(len(temp)-1): 186 | parameter_path = parameter_path + temp[i] + '/' 187 | parameter_path = parameter_path + model_name + '_parameter.txt' 188 | with open(parameter_path, 'r') as f: 189 | lines = f.readlines() 190 | 191 | # 从读取的内容中解析all_floors_num 192 | temp = lines[0].split(':')[-1].split(' ') 193 | all_floors_num = [] 194 | for i in range(len(temp)-1): 195 | all_floors_num = all_floors_num + [int(temp[i])] 196 | 197 | # 从读取的内容中解析activation 198 | activation = lines[1].split(':')[-1] 199 | hidden_floors_num = len(all_floors_num) - 2 200 | 201 | # **********************读取验证数据************************************* 202 | X, Y = inputs(validate_data_path) 203 | X_dim = X.shape[1] 204 | 205 | # **********************创建神经网络************************************ 206 | x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) 207 | y_real = tf.placeholder(dtype=tf.float32, shape=[None, 1]) 208 | initial_w_and_b(all_floors_num) 209 | y_pre = inference(x) 210 | 211 | # 记录在验证集上的正确率 212 | validate_accuracy = tf.reduce_mean(tf.cast(tf.abs(y_pre - y_real) < precision, tf.float32)) 213 | 214 | sess = tf.Session() 215 | saver = tf.train.Saver() 216 | with tf.Session() as sess: 217 | # 读取模型 218 | try: 219 | saver.restore(sess, model_save_path) 220 | print('模型载入成功！') 221 | except: 222 | print('模型不存在，请先训练模型！') 223 | return 224 | validate_accuracy_value = sess.run(validate_accuracy, feed_dict={x: X, y_real: Y}) 225 | print('validate_accuracy is ', validate_accuracy_value) 226 | 227 | return validate_accuracy_value 228 | 229 | 230 | def predict(model_save_path, predict_data_path, predict_result_save_path): 231 | # **********************根据model_save_path推出模型参数路径, 解析出all_floors_num和activation**************** 232 | temp = model_save_path.split('/') 233 | model_name = temp[-1] 234 | parameter_path = '' 235 | for i in range(len(temp)-1): 236 | parameter_path = parameter_path + temp[i] + '/' 237 | parameter_path = parameter_path + model_name + '_parameter.txt' 238 | with open(parameter_path, 'r') as f: 239 | lines = f.readlines() 240 | 241 | # 从读取的内容中解析all_floors_num 242 | temp = lines[0].split(':')[-1].split(' ') 243 | all_floors_num = [] 244 | for i in range(len(temp)-1): 245 | all_floors_num = all_floors_num + [int(temp[i])] 246 | 247 | # 从读取的内容中解析activation 248 | activation = lines[1].split(':')[-1] 249 | hidden_floors_num = len(all_floors_num) - 2 250 | 251 | # **********************读取预测数据************************************* 252 | predict_data = pd.read_csv(predict_data_path) 253 | X = np.array(predict_data.iloc[:, :]) 254 | X_dim = X.shape[1] 255 | 256 | # **********************创建神经网络************************************ 257 | x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) 258 | initial_w_and_b(all_floors_num) 259 | y_pre = inference(x) 260 | 261 | sess = tf.Session() 262 | saver = tf.train.Saver() 263 | with tf.Session() as sess: 264 | # 读取模型 265 | try: 266 | saver.restore(sess, model_save_path) 267 | print('模型载入成功！') 268 | except: 269 | print('模型不存在，请先训练模型！') 270 | return 271 | y_pre_value = sess.run(y_pre, feed_dict={x: X[0:, :]}) 272 | 273 | # 将预测结果写入csv文件 274 | predict_data_columns = list(predict_data.columns) + ['predict'] 275 | data = np.column_stack([X, y_pre_value]) 276 | result = pd.DataFrame(data, columns=predict_data_columns) 277 | result.to_csv(predict_result_save_path, index=False) 278 | print('预测结果保存在：', predict_result_save_path) 279 | 280 | 281 | if __name__ == '__main__': 282 | mode = 'predict' 283 | 284 | if mode == 'train': 285 | # 训练模型 286 | train_model(hidden_floors_num, every_hidden_floor_num, learning_rate, activation, regularization, 287 | regularization_rate, total_step, train_data_path, model_save_path) 288 | 289 | if mode == 'validate': 290 | # 利用模型对验证集进行正确性测试 291 | validate(model_save_path, validate_data_path, precision) 292 | 293 | if mode == 'predict': 294 | # 利用模型进行预测 295 | predict(model_save_path, predict_data_path, predict_result_save_path) 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | -------------------------------------------------------------------------------- /BP_regression/generate_data.py: -------------------------------------------------------------------------------- 1 | # 生成测试数据 2 | import numpy as np 3 | import pandas as pd 4 | # 训练集和验证集样本总个数 5 | sample = 2000 6 | train_data_path = 'C:/Users/Administrator/Desktop/BP_regression/train.csv' 7 | validate_data_path = 'C:/Users/Administrator/Desktop/BP_regression/validate.csv' 8 | predict_data_path = 'C:/Users/Administrator/Desktop/BP_regression/test.csv' 9 | 10 | # 构造生成数据的模型 11 | X1 = np.zeros((sample, 1)) 12 | X1[:, 0] = np.random.normal(1, 1, sample) 13 | X2 = np.zeros((sample, 1)) 14 | X2[:, 0] = np.random.normal(2, 1, sample) 15 | X3 = np.zeros((sample, 1)) 16 | X3[:, 0] = np.random.normal(3, 1, sample) 17 | 18 | # 模型 19 | Y = 6 * X1 - 3 * X1 * X2 + X3 * X3 + np.random.normal(0, 0.1, [sample, 1]) 20 | 21 | # 将所有生成的数据放到data里面 22 | data = np.zeros((sample, 4)) 23 | data[:, 0] = X1[:, 0] 24 | data[:, 1] = X2[:, 0] 25 | data[:, 2] = X3[:, 0] 26 | data[:, 3] = Y[:, 0] 27 | 28 | # 将data分成测试集和训练集 29 | num_traindata = int(0.8*sample) 30 | 31 | # 将训练数据保存 32 | traindata = pd.DataFrame(data[0:num_traindata, :], columns=['x1', 'x2', 'x3', 'y']) 33 | traindata.to_csv(train_data_path, index=False) 34 | print('训练数据保存在: ', train_data_path) 35 | 36 | # 将验证数据保存 37 | validate_data = pd.DataFrame(data[num_traindata:, :], columns=['x1', 'x2', 'x3', 'y']) 38 | validate_data.to_csv(validate_data_path, index=False) 39 | print('验证数据保存在: ', validate_data_path) 40 | 41 | # 将预测数据保存 42 | predict_data = pd.DataFrame(data[num_traindata:, 0:-1], columns=['x1', 'x2', 'x3']) 43 | predict_data.to_csv(predict_data_path, index=False) 44 | print('预测数据保存在: ', predict_data_path) 45 | -------------------------------------------------------------------------------- /BP_regression/model/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "C:/Users/Administrator/Desktop/BP_regression/model/predict_model" 2 | all_model_checkpoint_paths: "C:/Users/Administrator/Desktop/BP_regression/model/predict_model" 3 | -------------------------------------------------------------------------------- /BP_regression/model/predict_model.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gitfuwu/dynamic_bp_network_regression/e99ebd8837927259c461e341c2a7b4efa2e1bf3f/BP_regression/model/predict_model.data-00000-of-00001 -------------------------------------------------------------------------------- /BP_regression/model/predict_model.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gitfuwu/dynamic_bp_network_regression/e99ebd8837927259c461e341c2a7b4efa2e1bf3f/BP_regression/model/predict_model.index -------------------------------------------------------------------------------- /BP_regression/model/predict_model.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gitfuwu/dynamic_bp_network_regression/e99ebd8837927259c461e341c2a7b4efa2e1bf3f/BP_regression/model/predict_model.meta -------------------------------------------------------------------------------- /BP_regression/model/predict_model_parameter.txt: -------------------------------------------------------------------------------- 1 | all_floors_num:3 50 1 2 | activation:tanh -------------------------------------------------------------------------------- /BP_regression/test.csv: -------------------------------------------------------------------------------- 1 | x1,x2,x3 2 | 0.4061664107061439,2.7657353574599814,1.3901967364640082 3 | 0.06583927725866467,2.5950998673884906,3.2493674054276567 4 | 1.1647715015837372,2.682098557853373,-0.10365232458797813 5 | 1.4618859030221447,2.1608108183200407,1.2346545147664643 6 | 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1.5226973713741039,0.7818523103212509,2.596997113411738,12.32162851572426 397 | 1.7593222682509366,2.5117025602952046,1.5697280170643784,-0.3167883932195922 398 | 2.971702926193399,2.4477475029314864,1.9516923006922648,-0.20554185260048136 399 | 2.2694752986494926,0.2991679696566536,1.0530752692694894,12.722326029354068 400 | 0.26509280296787785,3.921269645831977,2.203624884240427,3.413369680120567 401 | 0.7840306580708825,3.7943520602063163,0.7601799261389757,-3.6158363003909413 402 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # dynamic_bp_network_regression 2 | ''' 3 | 建立一个网络结构可变的BP神经网络通用代码： 4 | 5 | 在训练时各个参数的意义： 6 | hidden_floors_num：隐藏层的个数 7 | every_hidden_floor_num：每层隐藏层的神经元个数 8 | learning_rate：学习速率 9 | activation：激活函数 10 | regularization：正则化方式 11 | regularization_rate：正则化比率 12 | total_step：总的训练次数 13 | train_data_path：训练数据路径 14 | model_save_path：模型保存路径 15 | 16 | 利用训练好的模型对验证集进行验证时各个参数的意义： 17 | model_save_path：模型保存路径 18 | validate_data_path：验证集路径 19 | precision：精度 20 | 21 | 利用训练好的模型进行预测时各个参数的意义： 22 | model_save_path：模型的保存路径 23 | predict_data_path：预测数据路径 24 | predict_result_save_path：预测结果保存路径 25 | ''' 26 | 27 | --------------------------------------------------------------------------------