前景回顾：
【手把手TensorFlow】一、从开始使用TensorFlow到弄清楚“搭建神经网络套路”
【手把手TensorFlow】二、神经网络优化
【手把手TensorFlow】三、神经网络搭建完整框架+MNIST数据集实践

一、实现模型识别手动输入的图片

根据我们前面提到的搭建神经网络框架，我们先搭建神经网络框架并训练。

1.前向传播mnist_forward.py

import tensorflow as tf

INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

def get_weight(shape, regularizer):
    w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))
    if regularizer != None: tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w


def get_bias(shape):  
    b = tf.Variable(tf.zeros(shape))  
    return b
	
def forward(x, regularizer):
    w1 = get_weight([INPUT_NODE, LAYER1_NODE], regularizer)
    b1 = get_bias([LAYER1_NODE])
    y1 = tf.nn.relu(tf.matmul(x, w1) + b1)

    w2 = get_weight([LAYER1_NODE, OUTPUT_NODE], regularizer)
    b2 = get_bias([OUTPUT_NODE])
    y = tf.matmul(y1, w2) + b2
    return y

2.反向传播mnist_backward.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import os

BATCH_SIZE = 200
LEARNING_RATE_BASE = 0.1
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
STEPS = 50000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH="./model/"
MODEL_NAME="mnist_model"


def backward(mnist):

    x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
    y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
    y = mnist_forward.forward(x, REGULARIZER)
    global_step = tf.Variable(0, trainable=False)	
	
    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
    cem = tf.reduce_mean(ce)
    loss = cem + tf.add_n(tf.get_collection('losses'))

    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE, 
        LEARNING_RATE_DECAY,
        staircase=True)

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    ema_op = ema.apply(tf.trainable_variables())
    with tf.control_dependencies([train_step, ema_op]):
        train_op = tf.no_op(name='train')

    saver = tf.train.Saver()

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

        ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)

        for i in range(STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})
            if i % 1000 == 0:
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)


def main():
    mnist = input_data.read_data_sets("./data/", one_hot=True)
    backward(mnist)

if __name__ == '__main__':
    main()

3.测试程序mnist_test.py

#coding:utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import mnist_backward
TEST_INTERVAL_SECS = 5

def test(mnist):
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
        y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
        y = mnist_forward.forward(x, None)

        ema = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY)
        ema_restore = ema.variables_to_restore()
        saver = tf.train.Saver(ema_restore)
		
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))		

        while True:
            with tf.Session() as sess:
                ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH)
                if ckpt and ckpt.model_checkpoint_path:
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    accuracy_score = sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
                    print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score))
                else:
                    print('No checkpoint file found')
                    return
            time.sleep(TEST_INTERVAL_SECS)

def main():
    mnist = input_data.read_data_sets("./data/", one_hot=True)
    test(mnist)

if __name__ == '__main__':
    main()

经过上述3步我们就建立了一个完整的训练框架，但要是我们现在手里有10个图片，如下图，我们应该怎么应用训练好的模型判断呢？
在这里插入图片描述
所以我们引入应用程序代码：

4.应用程序mnist_app.py

#coding:utf-8

import tensorflow as tf
import numpy as np
from PIL import Image
import mnist_backward
import mnist_forward

def restore_model(testPicArr):
    with tf.Graph().as_default() as tg:
        x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
        y = mnist_forward.forward(x, None)
        preValue = tf.argmax(y, 1)

        variable_averages = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)

        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
		
                preValue = sess.run(preValue, feed_dict={x:testPicArr})
                return preValue
            else:
                print("No checkpoint file found")
                return -1

def pre_pic(picName):
    img = Image.open(picName)
    reIm = img.resize((28,28), Image.ANTIALIAS)
    im_arr = np.array(reIm.convert('L'))
    threshold = 50
    for i in range(28):
        for j in range(28):
            im_arr[i][j] = 255 - im_arr[i][j]
            if (im_arr[i][j] < threshold):
                im_arr[i][j] = 0
            else: im_arr[i][j] = 255

    nm_arr = im_arr.reshape([1, 784])
    nm_arr = nm_arr.astype(np.float32)
    img_ready = np.multiply(nm_arr, 1.0/255.0)

    return img_ready

def application(): 
    testNum = input("input the number of test pictures:")
    for i in range(int(testNum)):
        testPic = input("the path of test picture:")
        testPicArr = pre_pic(testPic)
        print(type(list(testPicArr)))
        preValue = restore_model(list(testPicArr))
        print("The prediction number is:", preValue)

def main():
    application()

if __name__ == '__main__':
	main()

任务分成两个函数完成：
1）testPicArr = pre_pic(testPic)对手写数字图片做预处理
2）preValue = restore_model(testPicArr) 将符合神经网络输入要求的图片喂给复现的神经网络模型，输出预测值

处理具体过程：

模型要求为黑底白字，所以需要将输入像素点改为255减去原值得到互补的反色。
进行二值化处理
将形状拉为1行784列，并把值变为浮点型
让现有RGB图从0-25的变为0-1之间的浮点数
计算输出的y，y的最大值所对应的索引为预测结果

运行结果：
在这里插入图片描述

二、自己生成数据集进行训练

1.生成数据集mnist_generateds.py

#coding:utf-8
import tensorflow as tf
import numpy as np
from PIL import Image
import os

image_train_path='./mnist_data_jpg/mnist_train_jpg_60000/'
label_train_path='./mnist_data_jpg/mnist_train_jpg_60000.txt'
tfRecord_train='./data/mnist_train.tfrecords'
image_test_path='./mnist_data_jpg/mnist_test_jpg_10000/'
label_test_path='./mnist_data_jpg/mnist_test_jpg_10000.txt'
tfRecord_test='./data/mnist_test.tfrecords'
data_path='./data'
resize_height = 28
resize_width = 28
# =============================================================================
# 生成tfrecords文件
# =============================================================================
def write_tfRecord(tfRecordName, image_path, label_path):
    #新建一个writer
    writer = tf.python_io.TFRecordWriter(tfRecordName)  
    num_pic = 0 
    f = open(label_path, 'r')
    contents = f.readlines()
    f.close()
    for content in contents:
        value = content.split()
        img_path = image_path + value[0] 
        img = Image.open(img_path)
        img_raw = img.tobytes() 
        labels = [0] * 10  
        labels[int(value[1])] = 1  
        #以键值对的方式存储训练数据  ，封装到example中。  
        example = tf.train.Example(features=tf.train.Features(feature={
                'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
                'label': tf.train.Feature(int64_list=tf.train.Int64List(value=labels))
                })) 
        #将example进行序列化
        writer.write(example.SerializeToString())
        num_pic += 1 
        print ("the number of picture:", num_pic)
    writer.close()
    print("write tfrecord successful")

def generate_tfRecord():
    isExists = os.path.exists(data_path) 
    if not isExists: 
        os.makedirs(data_path)
        print('The directory was created successfully')
    else:
        print ('directory already exists' )
    write_tfRecord(tfRecord_train, image_train_path, label_train_path)
    write_tfRecord(tfRecord_test, image_test_path, label_test_path)
  
# =============================================================================
# 解析tfrecords文件
# =============================================================================
def read_tfRecord(tfRecord_path):
    #生成先入先出序列，文件阅读器会使用它来读数据
    filename_queue = tf.train.string_input_producer([tfRecord_path], shuffle=True)
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue) 
    features = tf.parse_single_example(serialized_example,
                                       features={
                                        'label': tf.FixedLenFeature([10], tf.int64),
                                        'img_raw': tf.FixedLenFeature([], tf.string)
                                        })
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img.set_shape([784])
    img = tf.cast(img, tf.float32) * (1. / 255)
    label = tf.cast(features['label'], tf.float32)
    return img, label 
      
def get_tfrecord(num, isTrain=True):
    if isTrain:
        tfRecord_path = tfRecord_train
    else:
        tfRecord_path = tfRecord_test
    img, label = read_tfRecord(tfRecord_path)
    img_batch, label_batch = tf.train.shuffle_batch([img, label],
                                                    batch_size = num,
                                                    num_threads = 2,
                                                    capacity = 1000,
                                                    min_after_dequeue = 700)
    return img_batch, label_batch

def main():
    generate_tfRecord()

if __name__ == '__main__':
    main()

tfrecords 文件
1）tfrecords：是一种二进制文件，可先将图片和标签制作成该格式的文件。使用 tfrecords 进行数据读取，会提高内存利用率。
2）tf.train.Example: 用来存储训练数据。训练数据的特征用键值对的形式表示。
3）SerializeToString( )：把数据序列化成字符串存储。

filename_queue = tf.train.string_input_producer([tfRecord_path]) 该函数会生成一个先入先出的队列，文件阅读器会使用它来读取数据。

除此之外，反向传播文件修改图片标签获取的接口（mnist_backward.py），并且利用多线程提高图片和标签的批获取效率。

2.mnist_backward.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import os
import mnist_generateds#1

BATCH_SIZE = 200
LEARNING_RATE_BASE = 0.1
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
STEPS = 50000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH="./model/"
MODEL_NAME="mnist_model"
train_num_examples = 60000#2

def backward():

    x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
    y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
    y = mnist_forward.forward(x, REGULARIZER)
    global_step = tf.Variable(0, trainable=False) 

    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
    cem = tf.reduce_mean(ce)
    loss = cem + tf.add_n(tf.get_collection('losses'))

    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        train_num_examples / BATCH_SIZE, 
        LEARNING_RATE_DECAY,
        staircase=True)

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    ema_op = ema.apply(tf.trainable_variables())
    with tf.control_dependencies([train_step, ema_op]):
        train_op = tf.no_op(name='train')

    saver = tf.train.Saver()
    img_batch, label_batch = mnist_generateds.get_tfrecord(BATCH_SIZE, isTrain=True)#3

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

        ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
        #开启线程协调器
        coord = tf.train.Coordinator()#4
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)#5
        
        for i in range(STEPS):
            xs, ys = sess.run([img_batch, label_batch])#6
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})
            if i % 1000 == 0:
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)
        #关闭线程协调器
        coord.request_stop()#7
        coord.join(threads)#8


def main():
    backward()#9

if __name__ == '__main__':
    main()

其中mnist_test.py和mnist_app.py对获取数据都需要一点改动。最终便有了生成数据集并应用的案例。

参考中国MOOC《TensorFlow笔记》——北京大学曹健老师

【手把手TensorFlow】四、输入手写数字并输出识别结果