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TensorFlow-多分类单层神经网络softmax

时间:2018-12-15 00:18:42      阅读:255      评论:0      收藏:0      [点我收藏+]

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#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Wed Aug 8 19:13:09 2018 @author: myhaspl """ import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) print "样本数据维度大小:",mnist.train.images.shape print "样本标签维度大小:",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])#真实概率分布 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) #训练 for i in range(1000): batch_xs,batch_ys=mnist.train.next_batch(100) train_step.run({x:batch_xs,y_:batch_ys}) #验证 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}))

多分类目标通过tf.nn.softmax函数,确保输出为一个向量,所有向量元素均>0 且<1,其和为1每个元素,表示属于该类的概率。

TensorFlow-多分类单层神经网络softmax

标签:bat   ges   ice   none   reduce   example   data   sha   eval   

原文地址:http://blog.51cto.com/13959448/2330674

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