tensorflow 进阶(二),BP神经网络

这是一个三层的神经网络,只含有一个隐藏层.正确率有98%

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Mar 22 22:15:25 2018@author: luogan """from tensorflow.examples.tutorials.mnist import input_datamnist=input_data.read_data_sets('MNIST_data/',one_hot=True)print(mnist.train.images.shape)print(mnist.train.labels.shape)print(mnist.test.images.shape)print(mnist.test.images.shape)a=mnist.train.images[8] #a.reshape(28,28) import pandas as pd #b=pd.DataFrame(a) #b b=pd.DataFrame(a.reshape(28,28)) #b #b=pd.DataFrame(a.reshape(28,28)) b.to_excel('c.xls') d=mnist.train.labels[8]print(mnist.validation.images.shape) print(mnist.validation.labels.shape)import tensorflow as tf sess=tf.InteractiveSession()in_units=784 h1_units=300w1=tf.Variable( tf.truncated_normal([in_units,h1_units],stddev=0.1 ) ) b1=tf.Variable(tf.zeros([h1_units]))w2=tf.Variable(tf.zeros([h1_units,10])) b2=tf.Variable(tf.zeros([10]))x=tf.placeholder(tf.float32,[None,in_units]) keep_prob=tf.placeholder(tf.float32)hidden1=tf.nn.relu(tf.matmul(x,w1)+b1) hidden1_drop=tf.nn.dropout(hidden1,keep_prob)y=tf.nn.softmax(tf.matmul(hidden1_drop,w2)+b2)y_=tf.placeholder(tf.float32,[None,10]) 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)tf.global_variables_initializer().run()for i in range(1000):batch_xs,batch_ys=mnist.train.next_batch(100)train_step.run({x:batch_xs,y_:batch_ys,keep_prob:0.75})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,keep_prob:1})) (55000, 784) (55000, 10) (10000, 784) (10000, 784) (5000, 784) (5000, 10) 0.9823

总结

以上是生活随笔为你收集整理的tensorflow 进阶(二),BP神经网络的全部内容，希望文章能够帮你解决所遇到的问题。

如果觉得生活随笔网站内容还不错，欢迎将生活随笔推荐给好友。