用Tensorflow实现弹性网络回归算法

最新推荐文章于 2024-08-08 16:44:55 发布

Leonwenbin

最新推荐文章于 2024-08-08 16:44:55 发布

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文章标签： Tensorflow python

本文链接：https://blog.youkuaiyun.com/weixin_43788499/article/details/84584890

本文通过在Win10环境下使用Python 3.6和TensorFlow 1.12.0，在PyCharm中实现了对鸢尾花数据集的弹性网络回归分析。通过设置学习率为0.001，批量大小为50，迭代1000次，使用梯度下降优化器最小化损失函数，最终绘制了每代的损失曲线。

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测试平台：win10 pycharm tensorflow-1.12.0 python-3.6

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets as ds
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
sess = tf.Session()
iris = ds.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])

batch_size = 50
learning_rate = 0.001
x_data = tf.placeholder(shape=[None, 3], dtype=tf.float32)
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)

elastic_parma_1 = tf.constant(1.)
elastic_parma_2 = tf.constant(1.)

l1_a_loss = tf.reduce_mean(tf.abs(A))
l2_a_loss = tf.reduce_mean(tf.square(A))
e1_term = tf.multiply(elastic_parma_1, l1_a_loss)
e2_term = tf.multiply(elastic_parma_2, 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)
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[0])

    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))

plt.plot(loss_vec, 'k-')
plt.title('Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.savefig('test')
plt.show()