TensorFlow 学习（1）——第一个程序：线性回归

#!/usr/bin/env python

import os
os.environ['TF_CPP_MIN_LOG+LEVEL'] = '2'#加上这一句可以少显示一些没有必要的红字
import  tensorflow as tf
import  numpy as np

#设置训练样本
train_X = np.linspace(-1,1,100)
train_Y = 2*train_X + np.random.randn(*train_X.shape)*0.33 +10

#设置训练模型
X = tf.placeholder("float")
Y = tf.placeholder("float")
w = tf.Variable(0.0,name = 'weight')
b = tf.Variable(0.0,name = 'bias')
loss = tf.square(Y - X*w - b)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

#开始训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    epoch = 1
    for i in range(10):
        for (x,y) in zip(train_X,train_Y):
            _, w_value, b_value = sess.run([train_op,w,b],feed_dict={X:x,Y:y})

        print('Epoch: {},w: {},b: {}'.format(epoch,w_value,b_value))
        epoch += 

目前这个程序还有很多地方没有搞懂，先跑一跑例程看看效果如何。从结果来看，最终的训练成果能够接近于预设的数据