TensorFlow——HelloWorld线性回归

import tensorflow as tf
import numpy as np

# 使用 NumPy 生成假数据(phony data), 总共 100 个点.
x_data = np.float32(np.random.rand(2, 100))
# 随机输入
y_data = np.dot([0.100, 0.200], x_data) + 0.300

# 构造一个线性模型
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
y = tf.matmul(W, x_data) + b

# 最小化方差
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# 初始化变量
init = tf.global_variables_initializer()

# 启动图 (graph)
with tf.Session() as sess:
    sess.run(init)
    # 拟合平面
    for step in range(0, 201):
        sess.run(train)
        if step % 20 == 0:
            print(step, sess.run(W), sess.run(b))

# 得到最佳拟合结果 W: [[0.100  0.200]], b: [0.300]