python – Tensorflow MNIST(权重和偏差变量)

这是提供的代码：

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
x = tf.placeholder(tf.float32,[None,784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder(tf.float32,10])

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
saver = tf.train.Saver()
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
  batch_xs,batch_ys = mnist.train.next_batch(100)
  sess.run(train_step,feed_dict={x: batch_xs,y_: batch_ys})

但实际上我根本不明白变量“W”(重量)和“b”(偏差)是如何在计算时改变的？
在每个批次上,它们初始化为零,但之后？
我根本没有看到他们将要改变的代码在哪里？

非常感谢你提前！