from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)#读取mnist数据
#定义模型
x=tf.placeholder("float",[None,784])#接收mnist集中的图
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("float",[None,10])#接收mnist集中的正确标签
cross_entropy= -tf.reduce_sum(y_*tf.log(y))#y是预测的值，y_是x输入对应的真实数字值
train_step=tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init=tf.initialize_all_variables()
sess=tf.Session()
sess.run(init)
'''分批训练：分成1000次，每次抽取100个样本'''
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})
#print(sess.run(y, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
print(sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels}))
结果：0.9145
