import tensorflow as tfimport pandas as pdimport numpy as npcreateVar = locals()
'''建立一个网络结构可变的BP神经网络通用代码：
在训练时各个参数的意义：hidden_floors_num：隐藏层的个数every_hidden_floor_num：每层隐藏层的神经元个数learning_rate：学习速率activation：激活函数regularization：正则化方式regularization_rate：正则化比率total_step：总的训练次数train_data_path：训练数据路径model_save_path：模型保存路径
利用训练好的模型对验证集进行验证时各个参数的意义：model_save_path：模型保存路径validate_data_path：验证集路径precision：精度
利用训练好的模型进行预测时各个参数的意义：model_save_path：模型的保存路径predict_data_path：预测数据路径predict_result_save_path：预测结果保存路径'''
# 训练模型全局参数hidden_floors_num = 1every_hidden_floor_num = [50]learning_rate = 0.00001activation = 'tanh'regularization = 'L1'regularization_rate = 0.0001total_step = 200000train_data_path = 'train.csv'model_save_path = 'model/predict_model'
# 利用模型对验证集进行验证返回正确率model_save_path = 'model/predict_model'validate_data_path = 'validate.csv'precision = 0.5
# 利用模型进行预测全局参数model_save_path = 'model/predict_model'predict_data_path = 'test.csv'predict_result_save_path = 'test_predict.csv'
def inputs(train_data_path): train_data = pd.read_csv(train_data_path) X = np.array(train_data.iloc[:, :-1]) Y = np.array(train_data.iloc[:, -1:]) return X, Y
def make_hidden_layer(pre_lay_num, cur_lay_num, floor): createVar['w' + str(floor)] = tf.Variable(tf.random_normal([pre_lay_num, cur_lay_num], stddev=1)) createVar['b' + str(floor)] = tf.Variable(tf.random_normal([cur_lay_num], stddev=1)) return eval('w'+str(floor)), eval('b'+str(floor))
def initial_w_and_b(all_floors_num): # 初始化隐藏层的w, b for floor in range(2, hidden_floors_num+3): pre_lay_num = all_floors_num[floor-2] cur_lay_num = all_floors_num[floor-1] w_floor, b_floor = make_hidden_layer(pre_lay_num, cur_lay_num, floor) createVar['w' + str(floor)] = w_floor createVar['b' + str(floor)] = b_floor
def cal_floor_output(x, floor): w_floor = eval('w'+str(floor)) b_floor = eval('b'+str(floor)) if activation == 'sigmoid': output = tf.sigmoid(tf.matmul(x, w_floor) + b_floor) if activation == 'tanh': output = tf.tanh(tf.matmul(x, w_floor) + b_floor) if activation == 'relu': output = tf.nn.relu(tf.matmul(x, w_floor) + b_floor) return output
def inference(x): output = x for floor in range(2, hidden_floors_num+2): output = cal_floor_output(output, floor)
floor = hidden_floors_num+2 w_floor = eval('w'+str(floor)) b_floor = eval('b'+str(floor)) output = tf.matmul(output, w_floor) + b_floor return output
def loss(x, y_real): y_pre = inference(x) if regularization == 'None': total_loss = tf.reduce_sum(tf.squared_difference(y_real, y_pre))
if regularization == 'L1': total_loss = 0 for floor in range(2, hidden_floors_num + 3): w_floor = eval('w' + str(floor)) total_loss = total_loss + tf.contrib.layers.l1_regularizer(regularization_rate)(w_floor) total_loss = total_loss + tf.reduce_sum(tf.squared_difference(y_real, y_pre))
if regularization == 'L2': total_loss = 0 for floor in range(2, hidden_floors_num + 3): w_floor = eval('w' + str(floor)) total_loss = total_loss + tf.contrib.layers.l2_regularizer(regularization_rate)(w_floor) total_loss = total_loss + tf.reduce_sum(tf.squared_difference(y_real, y_pre))
return total_loss
def train(total_loss): train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss) return train_op
# 训练模型def train_model(hidden_floors_num, every_hidden_floor_num, learning_rate, activation, regularization, regularization_rate, total_step, train_data_path, model_save_path): file_handle = open('acc.txt', mode='w') X, Y = inputs(train_data_path) X_dim = X.shape[1] all_floors_num = [X_dim] + every_hidden_floor_num + [1]
# 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 temp = model_save_path.split('/') model_name = temp[-1] parameter_path = '' for i in range(len(temp)-1): parameter_path = parameter_path + temp[i] + '/' parameter_path = parameter_path + model_name + '_parameter.txt' with open(parameter_path, 'w') as f: f.write("all_floors_num:") for i in all_floors_num: f.write(str(i) + ' ') f.write('\n') f.write('activation:') f.write(str(activation))
x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) y_real = tf.placeholder(dtype=tf.float32, shape=[None, 1]) initial_w_and_b(all_floors_num) y_pre = inference(x) total_loss = loss(x, y_real) train_op = train(total_loss)
# 记录在训练集上的正确率 train_accuracy = tf.reduce_mean(tf.cast(tf.abs(y_pre - y_real) < precision, tf.float32)) print(y_pre) # 保存模型 saver = tf.train.Saver()
# 在一个会话对象中启动数据流图，搭建流程 sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) for step in range(total_step): sess.run([train_op], feed_dict={x: X[0:, :], y_real: Y[0:, :]}) if step % 1000 == 0: saver.save(sess, model_save_path) total_loss_value = sess.run(total_loss, feed_dict={x: X[0:, :], y_real: Y[0:, :]}) lxacc=sess.run(train_accuracy, feed_dict={x: X, y_real: Y}) print('train step is ', step, ', total loss value is ', total_loss_value, ', train_accuracy', lxacc, ', precision is ', precision)
file_handle.write(str(lxacc)+"\n")
saver.save(sess, model_save_path) sess.close()
def validate(model_save_path, validate_data_path, precision): # **********************根据model_save_path推出模型参数路径, 解析出all_floors_num和activation**************** temp = model_save_path.split('/') model_name = temp[-1] parameter_path = '' for i in range(len(temp)-1): parameter_path = parameter_path + temp[i] + '/' parameter_path = parameter_path + model_name + '_parameter.txt' with open(parameter_path, 'r') as f: lines = f.readlines()
# 从读取的内容中解析all_floors_num temp = lines[0].split(':')[-1].split(' ') all_floors_num = [] for i in range(len(temp)-1): all_floors_num = all_floors_num + [int(temp[i])]
# 从读取的内容中解析activation activation = lines[1].split(':')[-1] hidden_floors_num = len(all_floors_num) - 2
# **********************读取验证数据************************************* X, Y = inputs(validate_data_path) X_dim = X.shape[1]
# **********************创建神经网络************************************ x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) y_real = tf.placeholder(dtype=tf.float32, shape=[None, 1]) initial_w_and_b(all_floors_num) y_pre = inference(x)
# 记录在验证集上的正确率 validate_accuracy = tf.reduce_mean(tf.cast(tf.abs(y_pre - y_real) < precision, tf.float32))
sess = tf.Session() saver = tf.train.Saver() with tf.Session() as sess: # 读取模型 try: saver.restore(sess, model_save_path) print('模型载入成功！') except: print('模型不存在，请先训练模型！') return validate_accuracy_value = sess.run(validate_accuracy, feed_dict={x: X, y_real: Y}) print('validate_accuracy is ', validate_accuracy_value)
return validate_accuracy_value
def predict(model_save_path, predict_data_path, predict_result_save_path): # **********************根据model_save_path推出模型参数路径, 解析出all_floors_num和activation**************** temp = model_save_path.split('/') model_name = temp[-1] parameter_path = '' for i in range(len(temp)-1): parameter_path = parameter_path + temp[i] + '/' parameter_path = parameter_path + model_name + '_parameter.txt' with open(parameter_path, 'r') as f: lines = f.readlines()
# 从读取的内容中解析all_floors_num temp = lines[0].split(':')[-1].split(' ') all_floors_num = [] for i in range(len(temp)-1): all_floors_num = all_floors_num + [int(temp[i])]
# 从读取的内容中解析activation activation = lines[1].split(':')[-1] hidden_floors_num = len(all_floors_num) - 2
# **********************读取预测数据************************************* predict_data = pd.read_csv(predict_data_path) X = np.array(predict_data.iloc[:, :]) X_dim = X.shape[1]
# **********************创建神经网络************************************ x = tf.placeholder(dtype=tf.float32, shape=[None, X_dim]) initial_w_and_b(all_floors_num) y_pre = inference(x)
sess = tf.Session() saver = tf.train.Saver() with tf.Session() as sess: # 读取模型 try: saver.restore(sess, model_save_path) print('模型载入成功！') except: print('模型不存在，请先训练模型！') return y_pre_value = sess.run(y_pre, feed_dict={x: X[0:, :]})
# 将预测结果写入csv文件 predict_data_columns = list(predict_data.columns) + ['predict'] data = np.column_stack([X, y_pre_value]) result = pd.DataFrame(data, columns=predict_data_columns) result.to_csv(predict_result_save_path, index=False) print('预测结果保存在：', predict_result_save_path)
if __name__ == '__main__': mode = "train"
if mode == 'train': # 训练模型 train_model(hidden_floors_num, every_hidden_floor_num, learning_rate, activation, regularization, regularization_rate, total_step, train_data_path, model_save_path)
if mode == 'validate': # 利用模型对验证集进行正确性测试 validate(model_save_path, validate_data_path, precision)
if mode == 'predict': # 利用模型进行预测 predict(model_save_path, predict_data_path, predict_result_save_path)
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