Regression

import tensorflow as tf 
import numpy as np 

# 去掉警告信息
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# create data
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

# create tensorflow structure start
weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))

y_pre = weights * x_data + biases

# 计算误差
loss = tf.reduce_mean(tf.square(y_data - y_pre))


# 梯度下降
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# 训练
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(weights), sess.run(biases))