mnist入门

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
input=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(input,w)+b))
y_=tf.placeholder(tf.float32,[None,10])
cross_entropy=-tf.reduce_sum(y_*tf.log(y))
train_step=tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
initialize=tf.initialize_all_variables()
accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y,1),tf.argmax(y_,1)),"float"))
with tf.Session() as sess:
    sess.run(initialize)
    for i in range(10000):
        batch_x,batch_y=mnist.train.next_batch(100)
        sess.run(train_step,feed_dict={input:batch_x,y_:batch_y})
        if (i%10==0):
            test_batch_input,test_batch_label=mnist.test.next_batch(15)
            print ("accuracy:%.4f" %sess.run(accuracy,feed_dict={input:mnist.test.images,y_:mnist.test.labels}))