TensorFlow练习6: 基于WiFi指纹的室内定位

TensorFlow练习6: 基于WiFi指纹的室内定位（autoencoder）

本帖基于论文：Low-effort place recognition with WiFi fingerprints using Deep Learning

室内定位有很多种方式，利用WiFi指纹就是是其中的一种。在室内，可以通过WiFi信号强度来确定移动设备的大致位置，参看：https://www.zhihu.com/question/20593603。

使用WiFi指纹定位的简要流程

首先采集WiFi信号，这并不需要什么专业的设备，几台手机即可。Android手机上有很多检测WiFi的App，如Sensor Log。

把室内划分成网格块(对应位置)，站在每个块内分别使用Sensor Log检测WiFi信号，数据越多越好。如下：

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location1 : WiFi{"BSSID":"11:11:11:11:11:11",...."level":-33,"....} # 所在位置对应的AP,RSSI信号强度等信息

location2 : WiFi{"BSSID":"11:11:11:11:11:11",...."level":-27,"....}

location2 : WiFi{"BSSID":"22:22:22:22:22:22",...."level":-80,"....}

location3 : WiFi{"BSSID":"22:22:22:22:22:22",...."level":-54,"....}

...

无线信号强度是负值，范围一般在0<->-90dbm。值越大信号越强，-50dbm强于-70dbm，

数据采集完成之后，对数据进行预处理，制作成WiFi指纹数据库，参考下面的UJIIndoorLoc数据集。

开发分类模型(本帖关注点)。

最后，用户上传所在位置的wifi信息，分类模型返回预测的位置。

TensorFlow练习: 基于WiFi指纹的室内定位

使用的数据集：https://archive.ics.uci.edu/ml/datasets/UJIIndoorLoc

TensorFlow练习6: 基于WiFi指纹的室内定位（autoencoder）

下载数据集：

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$ wget https://archive.ics.uci.edu/ml/machine-learning-databases/00310/UJIndoorLoc.zip

$ unzip UJIndoorLoc.zip

代码：

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import tensorflow as tf   # http://blog.topspeedsnail.com/archives/10399

from sklearn.preprocessing import scale  # 使用scikit-learn进行数据预处理

import pandas as pd

import numpy as np

training_data = pd.read_csv("trainingData.csv",header = 0)

# print(training_data.head())

train_x = scale(np.asarray(training_data.ix[:,0:520]))

train_y = np.asarray(training_data["BUILDINGID"].map(str) + training_data["FLOOR"].map(str))

train_y = np.asarray(pd.get_dummies(train_y))

test_dataset = pd.read_csv("validationData.csv",header = 0)

test_x = scale(np.asarray(test_dataset.ix[:,0:520]))

test_y = np.asarray(test_dataset["BUILDINGID"].map(str) + test_dataset["FLOOR"].map(str))

test_y = np.asarray(pd.get_dummies(test_y))

output = train_y.shape[1]

X = tf.placeholder(tf.float32, shape=[None, 520])  # 网络输入

Y = tf.placeholder(tf.float32,[None, output]) # 网络输出

# 定义神经网络

def neural_networks():

 # --------------------- Encoder -------------------- #

 e_w_1 = tf.Variable(tf.truncated_normal([520, 256], stddev = 0.1))

 e_b_1 = tf.Variable(tf.constant(0.0, shape=[256]))

 e_w_2 = tf.Variable(tf.truncated_normal([256, 128], stddev = 0.1))

 e_b_2 = tf.Variable(tf.constant(0.0, shape=[128]))

 e_w_3 = tf.Variable(tf.truncated_normal([128, 64], stddev = 0.1))

 e_b_3 = tf.Variable(tf.constant(0.0, shape=[64]))

 # --------------------- Decoder  ------------------- #

 d_w_1 = tf.Variable(tf.truncated_normal([64, 128], stddev = 0.1))

 d_b_1 = tf.Variable(tf.constant(0.0, shape=[128]))

 d_w_2 = tf.Variable(tf.truncated_normal([128, 256], stddev = 0.1))

 d_b_2 = tf.Variable(tf.constant(0.0, shape=[256]))

 d_w_3 = tf.Variable(tf.truncated_normal([256, 520], stddev = 0.1))

 d_b_3 = tf.Variable(tf.constant(0.0, shape=[520]))

 # --------------------- DNN  ------------------- #

 w_1 = tf.Variable(tf.truncated_normal([64, 128], stddev = 0.1))

 b_1 = tf.Variable(tf.constant(0.0, shape=[128]))

 w_2 = tf.Variable(tf.truncated_normal([128, 128], stddev = 0.1))

 b_2 = tf.Variable(tf.constant(0.0, shape=[128]))

 w_3 = tf.Variable(tf.truncated_normal([128, output], stddev = 0.1))

 b_3 = tf.Variable(tf.constant(0.0, shape=[output]))

 #########################################################

 layer_1 = tf.nn.tanh(tf.add(tf.matmul(X,       e_w_1), e_b_1))

 layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, e_w_2), e_b_2))

 encoded = tf.nn.tanh(tf.add(tf.matmul(layer_2, e_w_3), e_b_3))

 layer_4 = tf.nn.tanh(tf.add(tf.matmul(encoded, d_w_1), d_b_1))

 layer_5 = tf.nn.tanh(tf.add(tf.matmul(layer_4, d_w_2), d_b_2))

 decoded = tf.nn.tanh(tf.add(tf.matmul(layer_5, d_w_3), d_b_3))

 layer_7 = tf.nn.tanh(tf.add(tf.matmul(encoded, w_1),   b_1))

 layer_8 = tf.nn.tanh(tf.add(tf.matmul(layer_7, w_2),   b_2))

 out = tf.nn.softmax(tf.add(tf.matmul( layer_8, w_3),   b_3))

 return (decoded, out)

# 训练神经网络

def train_neural_networks():

 decoded, predict_output = neural_networks()

 us_cost_function = tf.reduce_mean(tf.pow(X - decoded, 2))

 s_cost_function = -tf.reduce_sum(Y * tf.log(predict_output))

 us_optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(us_cost_function)

 s_optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(s_cost_function)

 correct_prediction = tf.equal(tf.argmax(predict_output, 1), tf.argmax(Y,1))

 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

 training_epochs = 20

 batch_size = 10

 total_batches = training_data.shape[0]

 with tf.Session() as sess:

  sess.run(tf.initialize_all_variables())

  # ------------ Training Autoencoders - Unsupervised Learning ----------- #

  # autoencoder是一种非监督学习算法，他利用反向传播算法，让目标值等于输入值

  for epoch in range(training_epochs):

   epoch_costs = np.empty(0)

   for b in range(total_batches):

    offset = (b * batch_size) % (train_x.shape[0] - batch_size)

    batch_x = train_x[offset:(offset + batch_size), :]

    _, c = sess.run([us_optimizer, us_cost_function],feed_dict={X: batch_x})

    epoch_costs = np.append(epoch_costs, c)

   print("Epoch: ",epoch," Loss: ",np.mean(epoch_costs))

  print("------------------------------------------------------------------")

  # ---------------- Training NN - Supervised Learning ------------------ #

  for epoch in range(training_epochs):

   epoch_costs = np.empty(0)

   for b in range(total_batches):

    offset = (b * batch_size) % (train_x.shape[0] - batch_size)

    batch_x = train_x[offset:(offset + batch_size), :]

    batch_y = train_y[offset:(offset + batch_size), :]

    _, c = sess.run([s_optimizer, s_cost_function],feed_dict={X: batch_x, Y : batch_y})

    epoch_costs = np.append(epoch_costs,c)

   accuracy_in_train_set = sess.run(accuracy, feed_dict={X: train_x, Y: train_y})

   accuracy_in_test_set = sess.run(accuracy, feed_dict={X: test_x, Y: test_y})

   print("Epoch: ",epoch," Loss: ",np.mean(epoch_costs)," Accuracy: ", accuracy_in_train_set, ' ', accuracy_in_test_set)

train_neural_networks()