├── README.md ├── model ├── predict_model_parameter.txt ├── checkpoint ├── predict_model.index ├── predict_model.meta └── predict_model.data-00000-of-00001 ├── .gitattributes ├── .idea ├── .gitignore ├── inspectionProfiles │ ├── profiles_settings.xml │ └── Project_Default.xml ├── modules.xml ├── misc.xml └── BP_regression.iml ├── acc.txt ├── generate_data.py ├── dynamic_bp_network_regression.py ├── test.csv ├── validate.csv ├── test_predict.csv └── train.csv /README.md: -------------------------------------------------------------------------------- 1 | # BP_regression 2 | 通用的BP神经网络代码 3 | -------------------------------------------------------------------------------- /model/predict_model_parameter.txt: -------------------------------------------------------------------------------- 1 | all_floors_num:3 50 1 2 | activation:tanh -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /model/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "predict_model" 2 | all_model_checkpoint_paths: "predict_model" 3 | -------------------------------------------------------------------------------- /model/predict_model.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lixiang007666/BP_regression/HEAD/model/predict_model.index -------------------------------------------------------------------------------- /model/predict_model.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lixiang007666/BP_regression/HEAD/model/predict_model.meta -------------------------------------------------------------------------------- /model/predict_model.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lixiang007666/BP_regression/HEAD/model/predict_model.data-00000-of-00001 -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Datasource local storage ignored files 5 | /dataSources/ 6 | /dataSources.local.xml 7 | # Editor-based HTTP Client requests 8 | /httpRequests/ 9 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | -------------------------------------------------------------------------------- /.idea/BP_regression.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /acc.txt: -------------------------------------------------------------------------------- 1 | 0.021875 2 | 0.049375 3 | 0.08125 4 | 0.20125 5 | 0.39375 6 | 0.545625 7 | 0.653125 8 | 0.74125 9 | 0.799375 10 | 0.848125 11 | 0.885 12 | 0.91875 13 | 0.933125 14 | 0.94625 15 | 0.96 16 | 0.968125 17 | 0.974375 18 | 0.980625 19 | 0.98375 20 | 0.98625 21 | 0.99 22 | 0.99125 23 | 0.993125 24 | 0.995 25 | 0.99625 26 | 0.99625 27 | 0.99625 28 | 0.99625 29 | 0.996875 30 | 0.996875 31 | 0.996875 32 | 0.9975 33 | 0.9975 34 | 0.9975 35 | 0.9975 36 | 0.9975 37 | 0.9975 38 | 0.9975 39 | 0.9975 40 | 0.9975 41 | 0.9975 42 | 0.9975 43 | 0.998125 44 | 0.998125 45 | 0.999375 46 | 0.999375 47 | 0.999375 48 | 0.999375 49 | 0.999375 50 | 0.999375 51 | 0.999375 52 | 0.999375 53 | 0.999375 54 | 0.999375 55 | 0.999375 56 | 0.999375 57 | 0.999375 58 | 0.999375 59 | 0.999375 60 | 0.999375 61 | 0.999375 62 | 0.999375 63 | 0.999375 64 | 0.999375 65 | 1.0 66 | 1.0 67 | 1.0 68 | 1.0 69 | 1.0 70 | 1.0 71 | 1.0 72 | 1.0 73 | 1.0 74 | 1.0 75 | 1.0 76 | 1.0 77 | 1.0 78 | 1.0 79 | 1.0 80 | 1.0 81 | 1.0 82 | 1.0 83 | 1.0 84 | 1.0 85 | 1.0 86 | 1.0 87 | 1.0 88 | 1.0 89 | 1.0 90 | 1.0 91 | 1.0 92 | 1.0 93 | 1.0 94 | 1.0 95 | 1.0 96 | 1.0 97 | 1.0 98 | 1.0 99 | 1.0 100 | 1.0 101 | -------------------------------------------------------------------------------- /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 = 'train.csv' 7 | validate_data_path = 'validate.csv' 8 | predict_data_path = '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* 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 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 32 | -------------------------------------------------------------------------------- /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 = 100000 40 | train_data_path = 'train.csv' 41 | model_save_path = '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 = 'model/predict_model' 45 | validate_data_path = 'validate.csv' 46 | precision = 0.5 47 | 48 | # 利用模型进行预测全局参数 49 | model_save_path = 'model/predict_model' 50 | predict_data_path = 'test.csv' 51 | predict_result_save_path = '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 | file_handle = open('acc.txt', mode='w') 132 | X, Y = inputs(train_data_path) 133 | X_dim = X.shape[1] 134 | all_floors_num = [X_dim] + every_hidden_floor_num + [1] 135 | 136 | # 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 137 | temp = model_save_path.split('/') 138 | model_name = temp[-1] 139 | parameter_path = '' 140 | for i in range(len(temp)-1): 141 | parameter_path = parameter_path + temp[i] + '/' 142 | parameter_path = parameter_path + model_name + '_parameter.txt' 143 | with open(parameter_path, 'w') as f: 144 | f.write("all_floors_num:") 145 | for i in all_floors_num: 146 | f.write(str(i) + ' ') 147 | f.write('\n') 148 | f.write('activation:') 149 | f.write(str(activation)) 150 | 151 | x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) 152 | y_real = tf.placeholder(dtype=tf.float32, shape=[None, 1]) 153 | initial_w_and_b(all_floors_num) 154 | y_pre = inference(x) 155 | total_loss = loss(x, y_real) 156 | train_op = train(total_loss) 157 | 158 | # 记录在训练集上的正确率 159 | train_accuracy = tf.reduce_mean(tf.cast(tf.abs(y_pre - y_real) < precision, tf.float32)) 160 | print(y_pre) 161 | # 保存模型 162 | saver = tf.train.Saver() 163 | 164 | # 在一个会话对象中启动数据流图，搭建流程 165 | sess = tf.Session() 166 | init = tf.global_variables_initializer() 167 | sess.run(init) 168 | for step in range(total_step): 169 | sess.run([train_op], feed_dict={x: X[0:, :], y_real: Y[0:, :]}) 170 | if step % 1000 == 0: 171 | saver.save(sess, model_save_path) 172 | total_loss_value = sess.run(total_loss, feed_dict={x: X[0:, :], y_real: Y[0:, :]}) 173 | lxacc=sess.run(train_accuracy, feed_dict={x: X, y_real: Y}) 174 | print('train step is ', step, ', total loss value is ', total_loss_value, 175 | ', train_accuracy', lxacc, 176 | ', precision is ', precision) 177 | 178 | file_handle.write(str(lxacc)+"\n") 179 | 180 | 181 | saver.save(sess, model_save_path) 182 | sess.close() 183 | 184 | 185 | def validate(model_save_path, validate_data_path, precision): 186 | # **********************根据model_save_path推出模型参数路径, 解析出all_floors_num和activation**************** 187 | temp = model_save_path.split('/') 188 | model_name = temp[-1] 189 | parameter_path = '' 190 | for i in range(len(temp)-1): 191 | parameter_path = parameter_path + temp[i] + '/' 192 | parameter_path = parameter_path + model_name + '_parameter.txt' 193 | with open(parameter_path, 'r') as f: 194 | lines = f.readlines() 195 | 196 | # 从读取的内容中解析all_floors_num 197 | temp = lines[0].split(':')[-1].split(' ') 198 | all_floors_num = [] 199 | for i in range(len(temp)-1): 200 | all_floors_num = all_floors_num + [int(temp[i])] 201 | 202 | # 从读取的内容中解析activation 203 | activation = lines[1].split(':')[-1] 204 | hidden_floors_num = len(all_floors_num) - 2 205 | 206 | # **********************读取验证数据************************************* 207 | X, Y = inputs(validate_data_path) 208 | X_dim = X.shape[1] 209 | 210 | # **********************创建神经网络************************************ 211 | x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) 212 | y_real = tf.placeholder(dtype=tf.float32, shape=[None, 1]) 213 | initial_w_and_b(all_floors_num) 214 | y_pre = inference(x) 215 | 216 | # 记录在验证集上的正确率 217 | validate_accuracy = tf.reduce_mean(tf.cast(tf.abs(y_pre - y_real) < precision, tf.float32)) 218 | 219 | sess = tf.Session() 220 | saver = tf.train.Saver() 221 | with tf.Session() as sess: 222 | # 读取模型 223 | try: 224 | saver.restore(sess, model_save_path) 225 | print('模型载入成功！') 226 | except: 227 | print('模型不存在，请先训练模型！') 228 | return 229 | validate_accuracy_value = sess.run(validate_accuracy, feed_dict={x: X, y_real: Y}) 230 | print('validate_accuracy is ', validate_accuracy_value) 231 | 232 | return validate_accuracy_value 233 | 234 | 235 | def predict(model_save_path, predict_data_path, predict_result_save_path): 236 | # **********************根据model_save_path推出模型参数路径, 解析出all_floors_num和activation**************** 237 | temp = model_save_path.split('/') 238 | model_name = temp[-1] 239 | parameter_path = '' 240 | for i in range(len(temp)-1): 241 | parameter_path = parameter_path + temp[i] + '/' 242 | parameter_path = parameter_path + model_name + '_parameter.txt' 243 | with open(parameter_path, 'r') as f: 244 | lines = f.readlines() 245 | 246 | # 从读取的内容中解析all_floors_num 247 | temp = lines[0].split(':')[-1].split(' ') 248 | all_floors_num = [] 249 | for i in range(len(temp)-1): 250 | all_floors_num = all_floors_num + [int(temp[i])] 251 | 252 | # 从读取的内容中解析activation 253 | activation = lines[1].split(':')[-1] 254 | hidden_floors_num = len(all_floors_num) - 2 255 | 256 | # **********************读取预测数据************************************* 257 | predict_data = pd.read_csv(predict_data_path) 258 | X = np.array(predict_data.iloc[:, :]) 259 | X_dim = X.shape[1] 260 | 261 | # **********************创建神经网络************************************ 262 | x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) 263 | initial_w_and_b(all_floors_num) 264 | y_pre = inference(x) 265 | 266 | sess = tf.Session() 267 | saver = tf.train.Saver() 268 | with tf.Session() as sess: 269 | # 读取模型 270 | try: 271 | saver.restore(sess, model_save_path) 272 | print('模型载入成功！') 273 | except: 274 | print('模型不存在，请先训练模型！') 275 | return 276 | y_pre_value = sess.run(y_pre, feed_dict={x: X[0:, :]}) 277 | 278 | # 将预测结果写入csv文件 279 | predict_data_columns = list(predict_data.columns) + ['predict'] 280 | data = np.column_stack([X, y_pre_value]) 281 | result = pd.DataFrame(data, columns=predict_data_columns) 282 | result.to_csv(predict_result_save_path, index=False) 283 | print('预测结果保存在：', predict_result_save_path) 284 | 285 | 286 | if __name__ == '__main__': 287 | mode = "predict" 288 | 289 | if mode == 'train': 290 | # 训练模型 291 | train_model(hidden_floors_num, every_hidden_floor_num, learning_rate, activation, regularization, 292 | regularization_rate, total_step, train_data_path, model_save_path) 293 | 294 | if mode == 'validate': 295 | # 利用模型对验证集进行正确性测试 296 | validate(model_save_path, validate_data_path, precision) 297 | 298 | if mode == 'predict': 299 | # 利用模型进行预测 300 | predict(model_save_path, predict_data_path, predict_result_save_path) 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | -------------------------------------------------------------------------------- /test.csv: -------------------------------------------------------------------------------- 1 | x1,x2,x3 2 | 1.6431179418637412,1.3539569000529939,2.1494174561516 3 | -0.8237009662964438,1.0598148427906442,4.636725944065761 4 | -0.8414631207181558,3.0132174072249316,2.1583761753893866 5 | 2.304735226337434,2.8566094402059647,6.5283925184317715 6 | 3.3620201231202147,3.3829635544487306,1.2976767035891157 7 | 2.348508643522476,2.291063355321953,1.4458241723209353 8 | 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