TensorFlow-多分類單層神經網絡softmax
阿新 • • 發佈：2018-12-15
dice float none variables 網絡 tutorial .so log created
#!/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 "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，表示屬於該類的概率。
TensorFlow-多分類單層神經網絡softmax