内容简介:版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/cuiran/article/details/86604935
版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/cuiran/article/details/86604935
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。
可以直接从官网进行下载
http://yann.lecun.com/exdb/mnist/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_data import tensorflow as tf mnist = 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
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
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, ImageFilter import tensorflow as tf import matplotlib.pyplot as plt import time def 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:
以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网
猜你喜欢:- 机器学习 KNN 算法之手写数字识别
- 机器学习 KNN 算法之手写数字识别
- TensorFlow.js 卷积神经网络手写数字识别
- 使用TensorFlow处理MNIST手写体数字识别问题
- Python机器学习随笔之logistic回归识别手写数字
- 手撸卷积神经网络之手写数字识别 (Java)
本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。
数据结构与算法分析
[美]Mark Allen Weiss / 张怀勇 / 人民邮电出版社 / 2007年 / 49.00元
《数据结构与算法分析:C++描述(第3版)》是数据结构和算法分析的经典教材,书中使用主流的程序设计语言C++作为具体的实现语言。书的内容包括表、栈、队列、树、散列表、优先队列、排序、不相交集算法、图论算法、算法分析、算法设计、摊还分析、查找树算法、k-d树和配对堆等。《数据结构与算法分析:C++描述(第3版)》适合作为计算机相关专业本科生的数据结构课程和研究生算法分析课程的教材。本科生的数据结构课......一起来看看 《数据结构与算法分析》 这本书的介绍吧!