TensorFlow随笔-多分类单层神经网络softmax

版权声明：本博客所有文章版权归博主刘兴所有，转载请注意来源 https://blog.csdn.net/AI_LX/article/details/89684104

#!/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 "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。