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_data

mnist=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=300

w1=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