from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)#讀取mnist數(shù)據(jù)

#定義模型

x=tf.placeholder("float",[None,784])#接收mnist集中的圖

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集中的正確標簽

cross_entropy= -tf.reduce_sum(y_*tf.log(y))#y是預(yù)測的值，y_是x輸入對應(yīng)的真實數(shù)字值

train_step=tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init=tf.initialize_all_variables()

sess=tf.Session()

sess.run(init)

'''分批訓(xùn)練：分成1000次，每次抽取100個樣本'''

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}))

結(jié)果：0.9145