使用Tensorflow和MNIST识别自己手写的数字，并连续识别多张手写数字

一、准备待识别的图片（格式 .jpg、.png）

      1、通过画图工具可手写数字，在改变成28*28像素大小。

       2、可以通过解析MNIST数字集，得到数字图片。

       3、可以通过手机拍照获得数字照片，一般需要做二值化处理。

二、利用Tensorflow进行模型训练

    详细代码如下：       

    # -*- coding: utf-8 -*-
    """
    Created on Tue Jun  5 08:34:09 2018

    @author: jaminliu
    """

    from tensorflow.examples.tutorials.mnist import input_data
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

    import tensorflow as tf

    sess = tf.InteractiveSession()

    #权重使用的truncated_normal进行初始化,stddev标准差定义为0.1   偏置初始化为常量0.1

        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)

    '''
    卷积函数：
    strides[0]和strides[3]的两个1是默认值，中间两个1代表padding时在x方向运动1步，y方向运动1步
    padding='SAME'代表经过卷积之后的输出图像和原图像大小一样
    '''
    def conv2d(x, W):                                                               #x是图片的所有参数，W是此卷积    层的权重
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
'''
池化函数
ksize指定池化核函数的大小
根据池化核函数的大小定义strides的大小
'''
def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')                   #池化的核函数大小为2x2，因此ksize=[1,2,2,1]，步长为2，因此strides=[1,2,2,1]

x = tf.placeholder("float", shape=[None, 784])                                     #输入值
y_ = tf.placeholder("float", shape=[None, 10])                                     #输出值
x_image = tf.reshape(x, [-1,28,28,1])                                              #-1代表先不考虑输入的图片例子多少这个维度，后面的1是channel的数量，
                                                                                   #因为我们输入的图片是黑白的，因此channel是1，例如如果是RGB图像，那么channel就是3

#第一层卷积层
W_conv1 = weight_variable([5, 5, 1, 32])                                          #卷积核定义为5x5,1是输入的通道数目，32是输出的通道数目
b_conv1 = bias_variable([32])                                                     # 每个输出通道对应一个偏置

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)                          # 卷积运算，并使用ReLu激活函数激活
h_pool1 = max_pool_2x2(h_conv1)                                                   # pooling操作

#第二层卷积层
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)

#第一层全连接层
W_fc1 = weight_variable([7 * 7 * 64, 1024])                                            # 将最后操作的数据展开
b_fc1 = bias_variable([1024])                                                      # 下面就是定义一般神经网络的操作了，继续扩大为1024

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])                                       # 对应的偏置
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)                              # 运算、激活（这里不是卷积运算了，就是对应相乘）

#输出层
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)                                          # dropout防止过拟合

W_fc2 = weight_variable([1024, 10])                                                 # 最后一层权重初始化
b_fc2 = bias_variable([10])                                                       # 对应偏置

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)                         # 使用softmax分类器

#训练
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))                                   # 交叉熵损失函数来定义cost function
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)              #调用梯度下降                                                        
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess.run(tf.initialize_all_variables())

saver = tf.train.Saver()

for i in range(3000):
  batch = mnist.train.next_batch(50)                                               # 使用SGD，每次选50个数据训练
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})                                # dropout值定义为0.5
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})           # 每50次输出一下准确度

#红色部分要保存为自己的路径，和测试加载路径相对应

save_path = saver.save(sess,'my_net/model.ckpt')

print("test accuracy %g"%accuracy.eval(feed_dict={
       x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

三、使用训练好的模型进行识别

代码如下：

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# -*- coding: utf-8 -*-
"""
Created on Wed Jun  6 15:56:16 2018

@author: jaminliu
"""
from PIL import Image, ImageFilter
import tensorflow as tf
import matplotlib.pyplot as plt
#import cv2

def imageprepare(i):
    """
    This function returns the pixel values.
    The imput is a png file location.
    """
   
    file_name='test_image/' + str(i+1) + ".png" #导入自己的图片地址#导入自己的图片地址
    #in terminal 'mogrify -format png *.jpg' convert jpg to png
    im = Image.open(file_name).convert('L')

    
   # im.save("C:/software-projects/tensorflow_projects/shou_xie_shibie3/test_image/sample.png")
    plt.imshow(im)
    plt.show()
    #tf.reset_default_graph()
    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(1)

#此行代码很重要，为其他图片的测试设置重置，清除此前的初值

tf.reset_default_graph()
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([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(tf.float32)
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)

init_op = tf.initialize_all_variables()

saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init_op)

    saver.restore(sess,'C:/software-projects/tensorflow_projects/shou_xie_shibie3/models/model.ckpt')

    prediction=tf.argmax(y_conv,1)
    for i in range(9):
        result = imageprepare(i)
        predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess)

        print('recognize result:')
        print(predint[0])

四、测试结果如下：

等

训练模型中，我是训练了3000次，其中图片9的识别不准确，大家可以尝试训练更多次，增加准确性。