matlab朴素贝叶斯手写数字识别_基于MNIST数据集实现手写数字识别

介绍

在TensorFlow的官方入门课程中，多次用到mnist数据集。mnist数据集是一个数字手写体图片库，但它的存储格式并非常见的图片格式，所有的图片都集中保存在四个扩展名为idx*-ubyte.gz的二进制文件。

数据集

可以直接从官网进行下载

http://yann.lecun.com/exdb/mnist/

数据集

如果我们想要知道大名鼎鼎的mnist手写体数字都长什么样子，就需要从mnist数据集中导出手写体数字图片。了解这些手写体的总体形状，也有助于加深我们对TensorFlow入门课程的理解。

训练数据集

当我们下载了数据集后，需要对数据集进行训练。并保存训练的模型

#!/usr/bin/python3.5# -*- coding: utf-8 -*-from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfmnist = input_data.read_data_sets('MNIST_data', one_hot=True)x = tf.placeholder(tf.float32, [None, 784])y_ = tf.placeholder(tf.float32, [None, 10])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"))saver = tf.train.Saver()with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(20000): 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}) saver.save(sess, 'WModel/model.ckpt') print('test accuracy %g' % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))对应的模型文件如图所示

模型

用画图手写数字

通过电脑自带画图工具，手写一个数字，像素为28，如图所示

生成手写体

识别手写数字

把上面生成的图片保存为bmp或png

然后通过程序调用，在使用之前需要先加载前面保存的模型

#!/usr/bin/python3.5# -*- coding: utf-8 -*-from PIL import Image, ImageFilterimport tensorflow as tfimport matplotlib.pyplot as pltimport timedef imageprepare(): """ This function returns the pixel values. The imput is a png file location. """ file_name='result/4.bmp'#导入自己的图片地址 #in terminal 'mogrify -format png *.jpg' convert jpg to png im = Image.open(file_name) # plt.imshow(im) # plt.show() im = im.convert('L') im.save("sample.png")   tv = list(im.getdata()) #get pixel values #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black. tva = [ (255-x)*1.0/255.0 for x in tv]  #print(tva) return tva """ This function returns the predicted integer. The imput is the pixel values from the imageprepare() function. """ # Define the model (same as when creating the model file)result=imageprepare()x = tf.placeholder(tf.float32, [None, 784])y_ = tf.placeholder(tf.float32, [None, 10])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"))saver = tf.train.Saver()with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver.restore(sess, "./WModel/model.ckpt")#这里使用了之前保存的模型参数 print ("Model restored.") prediction=tf.argmax(y_conv,1) predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess) print(h_conv2) print('识别结果:') print(predint[0])识别结果如图所示:

运行结果