基于深度学习的PM2.5实时预测系统开发_开发实时预测系统

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今天去首师大参加了农行软开笔试，时间2小时，题目分为四大类型，总体感觉难度不大但是很多题目都触碰到了我的知识盲区，所以还是得加强基础知识的学习，果真应验了那句话"基础不牢,地动山摇"啊！这次想把我申请的那个科技基金项目好好总结一下，该科技基金是学校设立用于鼓励大家崇尚科研，学会写本子的，每年好像资助100项左右且资助额度是4000RMB/年，感觉学校在这一点做的很好，给学校点个大大的赞。言归正传，下面就自己是如何将科技基金顺利结题的做一个小小的总结。

一、数据集建立

图片的来源：Beijing Realtime Weather Photos

图片对应的PM2.5来源：http://aqicn.org/city/beijing/us-embassy/cn/

整理收集到的图片与相应的PM2.5，得到一个共包含几万个样本的数据集，且每个样本的以“序号+PM2.5浓度值”命名。具体数据集如下图所示

二、预测模型建立

考虑到所建立数据集的规模不足以训练好一个VGG16的网络，所以经过相关文献发现可以通过“迁移学习”的方式解决这个问题。大体思路分两步，第一步：复现在ImageNet数据集上训练好的VGG16网络，第二步：基于迁移学习思想采用训练数据集对VGG16进行微调。

第一步的目的在于要弄懂VGG16的网络结构长啥样,下图是论文作者给出的一张网络结构描述表。

下图是真实的网络结构：

第二步是要基于原始的VGG16网络，通过微调最后一层全连接层参数来实现迁移学习。在微调之前，需要先下载训练好的vgg16.npy文件，如果您还没有下载请转向“vgg16.npy_免费高速下载|百度网盘-分享无限制”。下面就来分析一下基于迁移学习的VGG16网络源码。

1、导入需要的包

import os
import numpy as np
import tensorflow as tf
import skimage.io
import skimage.transform
import matplotlib.pyplot as plt

2、读取图片并进行resize操作

# 读取图片
def load_data():
    imgs = {'training': []}
    fpaths = []
    labels = []
    for k in imgs.keys():
        dir = 'C:/PycharmFiles/Tensorflow_VGG_PM2.5/for_transfer_learning/data/' + k
        for file in os.listdir(dir):
            fpath = os.path.join(dir, file)
            fpaths.append(fpath)
            if not file.lower().endswith('.jpg'):
                continue
            try:
                resized_img = load_img(os.path.join(dir, file))
            except OSError:
                continue
            imgs[k].append(resized_img)  # [1, height, width, depth] * n
            if len(imgs[k]) == 400:  # only use 400 imgs to reduce my memory load
                break
    # fake length data for tiger and cat
            label = int(file.split("_")[0])
            labels.append(label)
    ys = [[label] for label in labels]
    ys1 = np.array(ys)
    xs = np.concatenate(imgs['training'], axis=0)
   
    return xs, ys1
 
#对图片进行resize操作
def load_img(path):
    img = skimage.io.imread(path)
    img = img / 255.0
    # print "Original Image Shape: ", img.shape
    # we crop image from center
    short_edge = min(img.shape[:2])
    yy = int((img.shape[0] - short_edge) / 2)
    xx = int((img.shape[1] - short_edge) / 2)
    crop_img = img[yy: yy + short_edge, xx: xx + short_edge]
    # resize to 224, 224
    resized_img = skimage.transform.resize(crop_img, (224, 224))[None, :, :, :]  # shape [1, 224, 224, 3]
    return resized_img

3、微调VGG，只对后面的全连接层进行训练

class Vgg16:
    vgg_mean = [103.939, 116.779, 123.68]
    print(3)
 
    def __init__(self, vgg16_npy_path=None, restore_from=None):
        # pre-trained parameters
        try:
            self.data_dict = np.load(vgg16_npy_path, encoding='latin1', allow_pickle = True).item()
            print(Vgg16)
        except FileNotFoundError:
            print(
                'Please download VGG16 parameters from here https://mega.nz/#!YU1FWJrA!O1ywiCS2IiOlUCtCpI6HTJOMrneN-Qdv3ywQP5poecM\nOr from my Baidu Cloud: https://pan.baidu.com/s/1Spps1Wy0bvrQHH2IMkRfpg')
 
        self.tfx = tf.placeholder(tf.float32, [None, 224, 224, 3])
        self.tfy = tf.placeholder(tf.float32, [None, 1])
 
        # Convert RGB to BGR
        red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=self.tfx * 255.0)
        bgr = tf.concat(axis=3, values=[
            blue - self.vgg_mean[0],
            green - self.vgg_mean[1],
            red - self.vgg_mean[2],
        ])
 
        # pre-trained VGG layers are fixed in fine-tune
        conv1_1 = self.conv_layer(bgr, "conv1_1")
        conv1_2 = self.conv_layer(conv1_1, "conv1_2")
        pool1 = self.max_pool(conv1_2, 'pool1')
 
        conv2_1 = self.conv_layer(pool1, "conv2_1")
        conv2_2 = self.conv_layer(conv2_1, "conv2_2")
        pool2 = self.max_pool(conv2_2, 'pool2')
 
        conv3_1 = self.conv_layer(pool2, "conv3_1")
        conv3_2 = self.conv_layer(conv3_1, "conv3_2")
        conv3_3 = self.conv_layer(conv3_2, "conv3_3")
        pool3 = self.max_pool(conv3_3, 'pool3')
 
        conv4_1 = self.conv_layer(pool3, "conv4_1")
        conv4_2 = self.conv_layer(conv4_1, "conv4_2")
        conv4_3 = self.conv_layer(conv4_2, "conv4_3")
        pool4 = self.max_pool(conv4_3, 'pool4')
 
        conv5_1 = self.conv_layer(pool4, "conv5_1")
        conv5_2 = self.conv_layer(conv5_1, "conv5_2")
        conv5_3 = self.conv_layer(conv5_2, "conv5_3")
        pool5 = self.max_pool(conv5_3, 'pool5')
 
        # detach original VGG fc layers and
        # reconstruct your own fc layers serve for your own purpose
        # flatten
        self.flatten = tf.reshape(pool5, [-1, 7 * 7 * 512])
 
        # fully connected
        self.fc6 = tf.layers.dense(self.flatten, 256, tf.nn.relu, name='fc6')
        self.out = tf.layers.dense(self.fc6, 1, name='out')
 
        self.sess = tf.Session()
        if restore_from:
            saver = tf.train.Saver()
            saver.restore(self.sess, restore_from)
        else:  # training graph
            self.loss = tf.losses.mean_squared_error(labels=self.tfy, predictions=self.out)
            self.train_op = tf.train.RMSPropOptimizer(0.001).minimize(self.loss)
            self.sess.run(tf.global_variables_initializer())
 
    def max_pool(self, bottom, name):
        return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
 
    def conv_layer(self, bottom, name):
        with tf.variable_scope(name):  # CNN's filter is constant, NOT Variable that can be trained
            conv = tf.nn.conv2d(bottom, self.data_dict[name][0], [1, 1, 1, 1], padding='SAME')
            lout = tf.nn.relu(tf.nn.bias_add(conv, self.data_dict[name][1]))
            return lout
 
    def train(self, x, y):
        loss, _ = self.sess.run([self.loss, self.train_op], {self.tfx: x, self.tfy: y})
        return loss
 
    def predict(self, paths):
        fig, axs = plt.subplots(1, 2)
        for i, path in enumerate(paths):
            x = load_img(path)
            length = self.sess.run(self.out, {self.tfx: x})
            print("%s's prediction results: %s" % (path,length))
            
 
    def save(self, path='C:/PycharmFiles/Tensorflow_VGG_PM2.5/for_transfer_learning/model/transfer_learn'):
        saver = tf.train.Saver()
        saver.save(self.sess, path, write_meta_graph=False)

4、进行训练，定义 epoch 为1000, batchsize为10，并打印输出每个 epoch的 loss 值

def train():
    # tigers_x, cats_x, tigers_y, cats_y = load_data()
    xs, ys = load_data()
    print(1)
    vgg = Vgg16(vgg16_npy_path='C:/PycharmFiles/Tensorflow_VGG_PM2.5/for_transfer_learning/vgg16.npy')
    print(2)
    print('Net built')
 
 
    for i in range(1000):
        b_idx = np.random.randint(0, len(xs), 10)
        train_loss = vgg.train(xs[b_idx], ys[b_idx])
        print(i, 'train loss: ', train_loss)
    vgg.save('C:/PycharmFiles/Tensorflow_VGG_PM2.5/for_transfer_learning/model/transfer_learn')  # save learned fc layers

5、基于训练好的模型进行预测，输入一张任意大小图片，输出一个预测的PM2.5值

def eval():
    vgg = Vgg16(vgg16_npy_path='C:/PycharmFiles/Tensorflow_VGG_PM2.5/for_transfer_learning/vgg16.npy',
                restore_from='C:/PycharmFiles/Tensorflow_VGG_PM2.5/for_transfer_learning/model/transfer_learn')
    fpaths = []
 
    dir = 'C:/PycharmFiles/Tensorflow_VGG_PM2.5/for_transfer_learning/data/test'
    for file in os.listdir(dir):
        fpath = os.path.join(dir, file)
        fpaths.append(fpath)
    vgg.predict( fpaths )

三、模型迁移移动端

刚开始将tensorflow模型迁移到移动端，参考了很多网上的教程的，也走了很多弯路，但最终还是将训练效果最好的VGG16网络成功迁移到Android Studio的移动客户端上了，实现了通过手机对周围环境进行拍照即可预测周围PM2.5浓度功能。这部分考虑到与自己的硕士毕业论文挂钩，所以在未毕业之前暂时不能对外公布，但可以给大家一个博客“一步步做一个数字手势识别APP_手势识别app代码_天泽28的博客-CSDN博客”参考参考，让大家少走一些弯路，还请各位多多包涵。

日积月累，与君共进，增增小结，未完待续。