代码：

#解释原理http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html
# -*- coding:utf-8 -*-
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder("float", [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_real = tf.placeholder("float", [None,10])

cross_entropy = -tf.reduce_sum(y_real*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) ? #用梯度下降算法（gradient descent algorithm）以0.01的学习速率最小化交叉熵。
#初始化模型
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_real: batch_ys})
??

#100个批处理数据点的小批量随机梯度下降训练?

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_real,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print sess.run(accuracy, feed_dict={x: mnist.test.images, y_real: mnist.test.labels})