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()

本文参考书《Tensorflow机器学习实战指南》

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。