Tensorflow学习：MINIST手写体

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 22 10:09:50 2017
"""
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
#
#import input_data
mnist = read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
#sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
#sess.run(tf.initialize_all_variables())
#y = tf.nn.softmax(tf.matmul(x,W) + b)
#cross_entropy = -tf.reduce_sum(y_*tf.log(y))
#train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
#for i in range(1000):
#    batch = mnist.train.next_batch(50)
#    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
#correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
#accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
#print( accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

'''
tf.truncated_normal
random_normal: 正太分布随机数，均值mean,标准差stddev
truncated_normal:截断正态分布随机数，均值mean,标准差stddev,
不过只保留[mean-2*stddev,mean+2*stddev]范围内的随机数
random_uniform:均匀分布随机数，范围为[minval,maxval]
'''
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
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)
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")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
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)
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

with tf.Session() as sess:

        sess.run(tf.initialize_all_variables())
        for i in range(101):
            batch = mnist.train.next_batch(50)
            if i%100 == 0:
                    train_accuracy = accuracy.eval(session=sess,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})

# 如果测试时此处出错，可能因为内存不够，改为每次输入100张图片
        print ("test accuracy %g"%accuracy.eval(session=sess,feed_dict={
             x: mnist.test.images[1:100], y_: mnist.test.labels[1:100], keep_prob: 1.0}))