用tensorflow实现弹性网络回归算法_Python

用tensorflow实现弹性网络回归算法

2021-01-02 22:51xckkcxxck Python

这篇文章主要介绍了用tensorflow实现弹性网络回归算法

本文实例为大家分享了tensorflow实现弹性网络回归算法，供大家参考，具体内容如下

python代码：

									#用tensorflow实现弹性网络算法(多变量) 

									#使用鸢尾花数据集，后三个特征作为特征，用来预测第一个特征。 

									#1 导入必要的编程库，创建计算图，加载数据集 

									import matplotlib.pyplot as plt 

									import tensorflow as tf 

									import numpy as np 

									from sklearn import datasets 

									from tensorflow.python.framework import ops 

									ops.get_default_graph() 

									sess = tf.Session() 

									iris = datasets.load_iris() 

									x_vals = np.array([[x[1], x[2], x[3]] for x in iris.data]) 

									y_vals = np.array([y[0] for y in iris.data]) 

									#2 声明学习率，批量大小，占位符和模型变量,模型输出 

									learning_rate = 0.001

									batch_size = 50

									x_data = tf.placeholder(shape=[None, 3], dtype=tf.float32) #占位符大小为3 

									y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) 

									A = tf.Variable(tf.random_normal(shape=[3,1])) 

									b = tf.Variable(tf.random_normal(shape=[1,1])) 

									model_output = tf.add(tf.matmul(x_data, A), b) 

									#3 对于弹性网络回归算法，损失函数包括L1正则和L2正则 

									elastic_param1 = tf.constant(1.) 

									elastic_param2 = tf.constant(1.) 

									l1_a_loss = tf.reduce_mean(abs(A)) 

									l2_a_loss = tf.reduce_mean(tf.square(A)) 

									e1_term = tf.multiply(elastic_param1, l1_a_loss) 

									e2_term = tf.multiply(elastic_param2, l2_a_loss) 

									loss = tf.expand_dims(tf.add(tf.add(tf.reduce_mean(tf.square(y_target - model_output)), e1_term), e2_term), 0) 

									#4 初始化变量， 声明优化器， 然后遍历迭代运行， 训练拟合得到参数 

									init = tf.global_variables_initializer() 

									sess.run(init) 

									my_opt = tf.train.GradientDescentOptimizer(learning_rate) 

									train_step = my_opt.minimize(loss) 

									loss_vec = [] 

									for i in range(1000): 

									   rand_index = np.random.choice(len(x_vals), size=batch_size) 

									   rand_x = x_vals[rand_index] 

									   rand_y = np.transpose([y_vals[rand_index]]) 

									   sess.run(train_step, feed_dict={x_data:rand_x, y_target:rand_y}) 

									   temp_loss = sess.run(loss, feed_dict={x_data:rand_x, y_target:rand_y}) 

									   loss_vec.append(temp_loss) 

									   if (i+1)%250 == 0: 

									     print('Step#' + str(i+1) +'A = ' + str(sess.run(A)) + 'b=' + str(sess.run(b))) 

									     print('Loss= ' +str(temp_loss)) 

									#现在能观察到， 随着训练迭代后损失函数已收敛。 

									plt.plot(loss_vec, 'k--') 

									plt.title('Loss per Generation') 

									plt.xlabel('Generation') 

									plt.ylabel('Loss') 

									plt.show()