#!/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()
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])
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})
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}))
