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_datamnist = 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_normalrandom_normal: 正太分布随机数，均值mean,标准差stddevtruncated_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}))