title: Tensorflow MNIST (Weight and bias variables)
body: <p>I'm learning how to use Tensorflow with the MNIST tutorial, but I'm blocking on a point of the tutorial.</p>
  
  <p>Here is the code provided : </p>
  
  <pre><code>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, [None, 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})
  </code></pre>
  
  <p>But I actually don't understand at all how the variables "W" (The weight) and "b" (The bias) are changed while the computing ?
  On each batch, they are initialized at zero, but after ?
  I don't see at all where in the code they're going to change ?</p>
  
  <p>Thanks you very much in advance!</p>