基于深度学习的PM2.5实时预测系统开发

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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：http://www.tour-beijing.com/real_time_weather_photo/

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“https://pan.baidu.com/s/1Spps1Wy0bvrQHH2IMkRfpg”。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/' + kfor file in os.listdir(dir):fpath = os.path.join(dir, file)fpaths.append(fpath)if not file.lower().endswith('.jpg'):continuetry:resized_img = load_img(os.path.join(dir, file))except OSError:continueimgs[k].append(resized_img)  # [1, height, width, depth] * nif len(imgs[k]) == 400:  # only use 400 imgs to reduce my memory loadbreak# fake length data for tiger and catlabel = 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 centershort_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, 224resized_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 parameterstry: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 BGRred, 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-tuneconv1_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# flattenself.flatten = tf.reshape(pool5, [-1, 7 * 7 * 512])# fully connectedself.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 graphself.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 trainedconv = 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 loutdef train(self, x, y):loss, _ = self.sess.run([self.loss, self.train_op], {self.tfx: x, self.tfy: y})return lossdef 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“https://blog.csdn.net/u012328159/article/details/81123018”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。