使用TensorFlow低级别的API进行编程

import tensorflow as tf
import numpy as np

# 构建一些数据
x_true = np.linspace(-1, 1, 100).astype(np.float32)
y_true = 3*x_true + 1

# 定义W和b变量
Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='weights')
biases = tf.Variable(tf.zeros([1]), name='biases')

# 计算损失
y_pred = Weights * x_true + biases
loss = tf.reduce_mean(tf.square(y_pred - y_true), name='loss')

# 定义优化
optimizer = tf.train.GradientDescentOptimizer(0.1)
train = optimizer.minimize(loss, name='train')

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(100):
        sess.run(train)
        if i % 10 == 0:
            print('Weights', sess.run(Weights), 'biases', sess.run(biases), 'loss', sess.run(loss))