tensorflow实现多项式回归

tensorflow实现多项式回归

以下代码是用tensorflow实现多项式回归

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import random

n_samples =100
X = np.linspace(-3,3,n_samples)
Y = np.sin(X) + np.random.uniform(0,1,n_samples)
plt.scatter(X,Y)
plt.show()

xs = tf.placeholder(dtype=tf.float32,name='xs')
ys = tf.placeholder(dtype=tf.float32,name='ys')

w1 = tf.Variable(tf.random_normal([1]),name='w1')
w2 = tf.Variable(tf.random_normal([1]),name='w2')
w3 = tf.Variable(tf.random_normal([1]),name='w3')
w4 = tf.Variable(tf.random_normal([1]),name='w4')
b = tf.Variable(tf.zeros(1),name='b')

y_pred = tf.multiply(w1,xs) + tf.multiply(w2,tf.pow(xs,2)) + tf.multiply(w3,tf.pow(xs,3)) \
    +tf.multiply(w4,tf.pow(xs,4)) + b

loss = tf.square(y_pred-ys,name='loss')/n_samples

lr = 0.01
epochs = 1000
optimizer = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(epochs):
        total_loss = 0
        for x,y in zip(X,Y):
            _,tmp_loss = sess.run([optimizer,loss],feed_dict={xs:x,ys:y})
            total_loss += tmp_loss

        if epoch%10 == 0:
            print('epoch{} the loss {}'.format(epoch,total_loss))
    w1,w2,w3,w4,b = sess.run([w1,w2,w3,w4,b])

plt.plot(X, Y, 'bo', label='real data')
plt.plot(X, X * w1+X**2*w2+X**3*w3+X**4*w4 + b, 'r', label='predicted data')
plt.legend()
plt.show()

运行结果:
tensorflow实现多项式回归_第1张图片

可以看出结果还是可以的

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