TensorFlow入门程序-线性回归

1.算法理论

机器学习之1-单变量线性回归(Linear Regression with One Variable)

2.代码

import numpy as npimport tensorflow as tf# Model parametersW=tf.Variable([.3],dtype=tf.float32)b=tf.Variable([-.3],dtype=tf.float32)# Model input and outputx=tf.placeholder(tf.float32)linear_model=W*x+by=tf.placeholder(tf.float32)#lossloss=tf.reduce_sum(tf.square(linear_model-y)) #sum of the squares#optimizeroptimizer=tf.train.GradientDescentOptimizer(0.01)train=optimizer.minimize(loss)#training datax_train=[1,2,3,4]y_train=[0,-1,-2,-3]#training loopinit=tf.global_variables_initializer()sess=tf.Session()sess.run(init) #reset values to wrongfor i in range(1000):    sess.run(train,{x:x_train,y:y_train})#evaluate training accuracycurr_W,curr_b,curr_loss=sess.run([W,b,loss],{x:x_train,y:y_train})print ("W: %s b:%s loss: %s"%(curr_W,curr_b,curr_loss))