1 import tensorflow.examples.tutorials.mnist.input_data as input_data
 2 import tensorflow as tf
 3 
 4 mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
 5 
 6 x = tf.placeholder(tf.float32,[None, 784])
 7 W = tf.Variable(tf.zeros([784,10]))
 8 b = tf.Variable(tf.zeros([10]))
 9 
10 y= tf.nn.softmax(tf.matmul(x,W) + b)
11 y_ = tf.placeholder("float",[None,10])
12 cross_entropy = -tf.reduce_sum(y_*tf.log(y))
13 train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
14 
15 init = tf.initialize_all_variables()
16 
17 sess = tf.Session()
18 sess.run(init)
19 
20 for i in range(1000):
21         batch_xs, batch_ys = mnist.train.next_batch(100)
22         sess.run(train_step, feed_dict={x: batch_xs,y_: batch_ys})
23 
24 correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
25 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
26 print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})

 按照教程，首先跑的是MNIST的历程。按照极客学院的教程，首先使用的是一个传统softmax的方法来实现的机器学习算法，核心是使用梯度下降的方式来进行的。上边是我整理的代码，train阶段的正确率是91%。