用tensorflow实现MNIST（手写数字识别）

自动下载和导入MNIST数据集

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0 #
# Unless required by applicable law or agreed to in writing, software
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"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import gzip
import os
import tempfile

import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets

#!usr/bin/python
#coding:utf-8

import input_data
import tensorflow as tf

#权值初始化
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)  #随机数
return tf.Variable(initial)

def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

#卷积
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

#池化
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

sess = tf.InteractiveSession()

x = tf.placeholder(tf.float32, [None, 784])   #占位符，为输入图像和输出类别创建节点
y_ = tf.placeholder("float", shape=[None, 10])
x_image = tf.reshape(x, [-1,28,28,1])   #-1为缺省值，由python根据实际情况推算

#第一层卷积池化
W_conv1 = weight_variable([5, 5, 1, 32])#前两个维度是patch的大小，接着是输入的通道数目，最后是输出的通道数目
b_conv1 = bias_variable([32])

# 一句话概括：不用simgoid和tanh作为激活函数，而用ReLU作为激活函数的原因是：加速收敛。
# 因为sigmoid和tanh都是饱和(saturating)的。何为饱和？个人理解是把这两者的函数曲线和导数曲线plot出来就知道了：
#他们的导数都是倒过来的碗状，也就是，越接近目标，对应的导数越小。而ReLu的导数对于大于0的部分恒为1。于是ReLU确实
#可以在BP的时候能够将梯度很好地传到较前面的网络。ReLU（线性纠正函数）取代sigmoid函数去激活神经元
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

#第二层卷积池化
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

#全连接层
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")   #每个神经元被保留下来的概率
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  #防止过拟合

#输出层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)#将任意实值向量映射到0-1范围内，元素总和为1

##########训练和评估模型##########
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))  #交叉熵
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #1e-4:学习速率
#tf.argmax给出某个tensor对象在某一维上的其数据最大值所在的索引值
#由于标签向量是由0,1组成，因此最大值1所在的索引位置就是类别标签
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))#计算均值
sess.run(tf.initialize_all_variables())

for i in range(1000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print "step %d, training accuracy %g"%(i, train_accuracy)
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print "test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})