机器学习之线性回归

Demo：

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

num_points = 1000
vectors_set = []
for i in range(num_points):
    x1 = np.random.normal(0.0, 0.55)
    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    vectors_set.append([x1, y1])

x_data = [v[0] for v in vectors_set]
y_data = [v[1] for v in vectors_set]

# Graphic display
plt.plot(x_data, y_data, 'ro')
plt.legend()
plt.show()

W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b

loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

for step in range(20):
    sess.run(train)
    print(step, sess.run(W), sess.run(b))
    print(step, sess.run(loss))

    # Graphic display
    plt.plot(x_data, y_data, 'ro')
    plt.plot(x_data, sess.run(W) * x_data + sess.run(b))
    plt.xlabel('x')
    plt.xlim(-2, 2)
    plt.ylim(0.1, 0.6)
    plt.ylabel('y')
    plt.legend()
    plt.show()

效果图：