import tensorflow as tf
import numpy as np
x_data=np.float32(np.random.rand(2,100))
y_data=np.dot([0.100,0.200],x_data)+0.300
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
y=tf.matmul(W,x_data)+b
loss=tf.reduce_mean(tf.square(y-y_data))
optimizer=tf.train.GradientDescentOptimizer(0.5)
train=optimizer.minimize(loss)
init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)
for step in range(0,201):
sess.run(train)
if step%20==0:
print (step,sess.run(W),sess.run(b)
how do you understand the tf.session here?