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))
