#!/usr/bin/env python2 # -*- 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) print "樣本數據維度大小：",mnist.train.images.shape print "樣本標簽維度大小：",mnist.train.labels.shape x=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(x,w)+b) y_=tf.placeholder(tf.float32,[None,10])#真實概率分布 cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1])) train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) with tf.Session() as sess:init_op=tf.global_variables_initializer()sess.run(init_op)#訓練for i in range(1000):batch_xs,batch_ys=mnist.train.next_batch(100)train_step.run({x:batch_xs,y_:batch_ys}) #驗證correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))print (accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))

多分類目標通過tf.nn.softmax函數，確保輸出為一個向量，所有向量元素均>0 且<1，其和為1每個元素，表示屬于該類的概率。

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