Tensorflow realize parameter adjustment of linear equations

import tensorflow as tfimport numpy as np#create datax_data=np.random.rand(100).astype(np.float32)y_data=x_data*0.1+0.3 #target resultspan>#create tensorflow structure startWeights=tf.Variable(tf.random_uniform([1],-1.0,1.0))biases=tf.Variable(tf.zeros([1])) # The weights and biases are continuously learned from the initial value and approach the target valuey=Weights*x_data+biasesloss=tf.reduce_mean(tf.square(y-y_data))# mean square erroroptimizer=tf.train.GradientDescentOptimizer(0.5)train=optimizer.minimize(loss)init=tf.initialize_all_variables()# create tensorflow structure endsess=tf.Session()sess.run(init) #points to the processing placefor step in range(201):sess.run(train)if step%20==0:print(step,sess.run(Weights),sess.run(biases))

Experiment results:

The final experimental results shown above show that Weight is close to the target value of 0.1, and biases is close to the target value of 0.3