手写数字集介绍

Posted 2023-05-09

参考技术A 手写数字集介绍

import tensorflow as tf

import matplotlib.pyplot as plt

import numpy as np

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

#MNIST数据集

minist = tf.keras.datasets.mnist

(train_x, train_y), (test_x, test_y) = minist.load_data()

#训练集和测试集的长度

print("Training set:", len(train_x))

print("Testing set:", len(test_x))

#图像数据和标记数据的形状

print("train_x", train_x.shape, train_x.dtype)

print("train_y", train_y.shape, train_y.dtype)

#数据集中的第1个样本

train_x[0]

#显示图片5

plt.axis("off")

plt.imshow(train_x[0], cmap="gray")

plt.title("显示图片5")

plt.show()

#输出图片5标签

train_y[0]

#随机显示4幅手写数字图片

for i in range(4):

    num = np.random.randint(1, 60000)

    plt.subplot(1, 4, i+1)

    plt.axis("off")

    plt.imshow(train_x[num], cmap="gray")

    plt.title(train_y[num])

plt.show()

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。

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: