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人工智能实践:全连接网络实践

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标签:argmax   cti   列操作   sha   mes   ini   完成   cap   rgb   

 输入手写数字图片输出识别结果

一、断点续训

关键处理:加入ckpt操作:

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

1、注解:

1)tf.train.get_checkpoint_state(checkpoint_dir,latest_filename=None)

该函数表示如果断点文件夹中包含有效断点状态文件,则返回该文件。

参数说明:checkpoint_dir:表示存储断点文件的目录

                latest_filename=None:断点文件的可选名称,默认为“checkpoint”

2)saver.restore(sess,ckpt.model_checkpoint_path)

该函数表示恢复当前会话,将ckpt中的值赋给w和b。 

参数说明:sess:表示当前会话,之前保存的结果将被加载入这个会话

                 ckpt.model_checkpoint_path:表示模型存储的位置,不需要提供模型的名字,它会去查看checkpoint文件,看看最新的是谁,叫做什么。 

二、输入真实图片,输出预测结果 

技术分享图片

网络输入:一维数组(784个像素点) 

技术分享图片

像素点:0-1之间的浮点数(接近0越黑,接近1越白) 

技术分享图片技术分享图片

     像素为0      像素为1

网络输出:一维数组(十个可能性概率),数组中最大的那个元素所对应的索引号就是预测的结果。 

关键处理:

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

注解: 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 

示例代码如下:

#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):
    #利用tf.Graph()复现之前定义的计算图
    with tf.Graph().as_default() as tg:
        x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
        #调用mnist_forward文件中的前向传播过程forword()函数
        y = mnist_forward.forward(x, None)
        #得到概率最大的预测值
        preValue = tf.argmax(y, 1)
 
        #实例化具有滑动平均的saver对象
        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:
            #通过ckpt获取最新保存的模型
            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
 
#预处理,包括resize,转变灰度图,二值化
def pre_pic(picName):
    img = Image.open(picName)
    reIm = img.resize((28,28), Image.ANTIALIAS)
    im_arr = np.array(reIm.convert(L))
    #对图片做二值化处理(这样以滤掉噪声,另外调试中可适当调节阈值)
    threshold = 50
    #模型的要求是黑底白字,但输入的图是白底黑字,所以需要对每个像素点的值改为255减去原值以得到互补的反色。
    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
    #把图片形状拉成1行784列,并把值变为浮点型(因为要求像素点是0-1 之间的浮点数)
    nm_arr = im_arr.reshape([1, 784])
    nm_arr = nm_arr.astype(np.float32)
    #接着让现有的RGB图从0-255之间的数变为0-1之间的浮点数
    img_ready = np.multiply(nm_arr, 1.0/255.0)
 
    return img_ready
 
def application():
    #输入要识别的几张图片
    testNum = input("input the number of test pictures:")
    for i in range(testNum):
        #给出待识别图片的路径和名称
        testPic = raw_input("the path of test picture:")
        #图片预处理
        testPicArr = pre_pic(testPic)
        #获取预测结果
        preValue = restore_model(testPicArr)
        print "The prediction number is:", preValue
 
def main():
    application()
 
if __name__ == __main__:
    main()        

实践代码验证

1)运行mnist_backward.py

技术分享图片

2)运行 mnist_test.py来监测模型的准确率

技术分享图片

3) 运行mnist_app.py输入10(表示循环验证十张图片)

技术分享图片

制作数据集,实现特定应用

1、数据集生成读取文件(mnist_generateds.py)

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。

如:‘ img_raw ’ :  值 

      ‘label ’      :  值 

         值是 Byteslist/FloatList/Int64List

3)SerializeToString():把数据序列化成字符串存储。

生成tfrecords文件

示例代码:

#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()
    #循环遍历每张图和标签 
    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  
        #把每张图片和标签封装到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))
                })) 
        #把example进行序列化
        writer.write(example.SerializeToString())
        num_pic += 1 
        print ("the number of picture:", num_pic)
    #关闭writer
    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

示例代码:

#解析tfrecords文件  
def read_tfRecord(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
    filename_queue = tf.train.string_input_producer([tfRecord_path], shuffle=True)
    #新建一个reader
    reader = tf.TFRecordReader()
    #把读出的每个样本保存在serialized_example中进行解序列化,标签和图片的键名应该和制作tfrecords的键名相同,其中标签给出几分类。
    _, serialized_example = reader.read(filename_queue) 
    #将tf.train.Example协议内存块(protocol buffer)解析为张量
    features = tf.parse_single_example(serialized_example,
                                       features={
                                        label: tf.FixedLenFeature([10], tf.int64),
                                        img_raw: tf.FixedLenFeature([], tf.string)
                                        })
    #将img_raw字符串转换为8位无符号整型
    img = tf.decode_raw(features[img_raw], tf.uint8)
    #将形状变为一行784列
    img.set_shape([784])
    img = tf.cast(img, tf.float32) * (1. / 255)
    #变成0到1之间的浮点数      
    label = tf.cast(features[label], tf.float32)
    #返回图片和标签
    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)
    #随机读取一个batch的数据
    img_batch, label_batch = tf.train.shuffle_batch([img, label],
                                                    batch_size = num,
                                                    num_threads = 2,
                                                    capacity = 1000,
                                                    min_after_dequeue = 700)
    #返回的图片和标签为随机抽取的batch_size组
    return img_batch, label_batch

2、反向传播文件修改图片标签获取的接口(mnist_backward.py)

关键操作:利用多线程提高图片和标签的批获取效率 

方法:将批获取的操作放到线程协调器开启和关闭之间开启线程协调器:

coord = tf.train.Coordinator( ) 

threads =tf.train.start_queue_runners(sess=sess, coord=coord)

关闭线程协调器:

coord.request_stop( ) coord.join(threads)

注解:

tf.train.start_queue_runners(sess=None,                          
   coord=None,                             
   daemon=True,                            
   start=True,                              
   collection=tf.GraphKeys.QUEUE_RUNNERS)

这个函数将会启动输入队列的线程,填充训练样本到队列中,以便出队操作可以从队列中拿到样本。这种情况下最好配合使用一个 tf.train.Coordinator,这样可以在发生错误的情况下正确地关闭这些线程。

 参数说明:sess:用于运行队列操作的会话。 默认为默认会话。        

coord:可选协调器,用于协调启动的线程。        

daemon: 守护进程,线程是否应该标记为守护进程,这意味着它们不会阻止程序退出。

start:设置为False只创建线程,不启动它们。        

collection:指定图集合以获取启动队列的 GraphKey。默认为

GraphKeys.QUEUE_RUNNERS。 

反向传播中示例代码mnist_backward.py

#coding:utf-8
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"
#手动给出训练的总样本数6万
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()
    #一次批获取 batch_size张图片和标签
    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)
 
        #利用多线程提高图片和标签的批获取效率    
        coord = tf.train.Coordinator()#4
        #启动输入队列的线程
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)#5
        
        for i in range(STEPS):
            #执行图片和标签的批获取
            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()

3、测试文件修改图片标签获取的接口(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
import mnist_generateds
TEST_INTERVAL_SECS = 5
#手动给出测试的总样本数1万
TEST_NUM = 10000#1
 
def test():
    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))
        #用函数get_tfrecord替换读取所有测试集1万张图片
        img_batch, label_batch = mnist_generateds.get_tfrecord(TEST_NUM, isTrain=False)#2
 
        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]
                    #利用多线程提高图片和标签的批获取效率    
                    coord = tf.train.Coordinator()#3
                    #启动输入队列的线程
                    threads = tf.train.start_queue_runners(sess=sess, coord=coord)#4
 
                    #执行图片和标签的批获取
                    xs, ys = sess.run([img_batch, label_batch])#5
 
                    accuracy_score = sess.run(accuracy, feed_dict={x: xs, y_: ys})
 
                    print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score))
                    #关闭线程协调器
                    coord.request_stop()#6
                    coord.join(threads)#7
 
                else:
                    print(No checkpoint file found)
                    return
            time.sleep(TEST_INTERVAL_SECS)
 
def main():
    test()#8
 
if __name__ == __main__:
    main()

4、实践代码验证

1)运行测试代码mnist_test.py 

2)准确率稳定在95%以上后运行应用程序mnist_app.py 

技术分享图片

人工智能实践:全连接网络实践

标签:argmax   cti   列操作   sha   mes   ini   完成   cap   rgb   

原文地址:https://www.cnblogs.com/xiaojianliu/p/9368967.html

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