机器学习实验（四）：用tensorflow实现卷积神经网络识别人类活动

声明：版权所有，转载请联系作者并注明出处  http://blog.csdn.net/u013719780?viewmode=contents

博主简介：风雪夜归子（英文名：Allen），机器学习算法攻城狮，喜爱钻研Meachine Learning的黑科技，对Deep Learning和Artificial Intelligence充满兴趣，经常关注Kaggle数据挖掘竞赛平台，对数据、Machine Learning和Artificial Intelligence有兴趣的童鞋可以一起探讨哦，个人CSDN博客：http://blog.csdn.net/u013719780?viewmode=contents

在近几年，越来越多的用户在智能手机上安装加速度传感器等一些设备，这就为做一些应用需要收集相关的数据提供了方便。人类活动识别(human activity recognition (HAR))是其中的一个应用。对于HAR，有很多的方法可以去尝试，方法的performance很大程度上依赖于特征工程。传统的机器学习特征工程通常是手工完成（人肉工程），这需要拥有较好的专业领域知识，同时比较耗时间。神经网络特别是深度学习在object recognition, machine translation, audio generation等取得了很大的成功，同样，深跌学习技术也可以应用到HAR上。

在本文中，我们将会看到如何将卷积神经网络技术应用到HAR问题上。

数据预处理

我们将会使用Wireless Sensor Data Mining (WISDM) lab发布的数据集Actitracker（http://www.cis.fordham.edu/wisdm/dataset.php） 这个数据集是在一个可以控制的实验环境中收集到的。数据集中包含6个活动类别，分别是jogging, walking, ascending stairs, descending stairs, sitting and standing。 这个数据集关于activities（labels）分布如下图所示：

首先导入相应的库和函数reading, normalising and plotting数据集。

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom scipy import statsimport tensorflow as tf%matplotlib inlineplt.style.use('ggplot')def read_data(file_path):    column_names = ['user-id','activity','timestamp', 'x-axis', 'y-axis', 'z-axis']    data = pd.read_csv(file_path,header = None, names = column_names)    return datadef feature_normalize(dataset):    mu = np.mean(dataset,axis = 0)    sigma = np.std(dataset,axis = 0)    return (dataset - mu)/sigma    def plot_axis(ax, x, y, title):    ax.plot(x, y)    ax.set_title(title)    ax.xaxis.set_visible(False)    ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])    ax.set_xlim([min(x), max(x)])    ax.grid(True)    def plot_activity(activity,data):    fig, (ax0, ax1, ax2) = plt.subplots(nrows = 3, figsize = (15, 10), sharex = True)    plot_axis(ax0, data['timestamp'], data['x-axis'], 'x-axis')    plot_axis(ax1, data['timestamp'], data['y-axis'], 'y-axis')    plot_axis(ax2, data['timestamp'], data['z-axis'], 'z-axis')    plt.subplots_adjust(hspace=0.2)    fig.suptitle(activity)    plt.subplots_adjust(top=0.90)    plt.show()

首先，读取数据集，然后normalise特征x-axis、y-axis、z-axis。

dataset = read_data('/Users/youwei.tan/Desktop/WISDM_ar_v1.1/WISDM_ar_v1.1_raw.txt')dataset['x-axis'] = feature_normalize(dataset['x-axis'])dataset['y-axis'] = feature_normalize(dataset['y-axis'])dataset['z-axis'] = feature_normalize(dataset['z-axis'])

接下来可视化x-axis、y-axis、z-axis与时间的关系图。

for activity in np.unique(dataset["activity"]):    subset = dataset[dataset["activity"] == activity][:180]    plot_activity(activity,subset)

数据已经处理好啦，现在需要将数据转变成卷积神经网络模型所需要的数据形式。具体实现直接看代码：

def windows(data, size):    start = 0    while start < data.count():        yield start, start + size        start += (size / 2)        def segment_signal(data,window_size = 90):    segments = np.empty((0,window_size,3))    labels = np.empty((0))    for (start, end) in windows(data["timestamp"], window_size):        x = data["x-axis"][start:end]        y = data["y-axis"][start:end]        z = data["z-axis"][start:end]        if(len(dataset["timestamp"][start:end]) == window_size):            segments = np.vstack([segments,np.dstack([x,y,z])])            labels = np.append(labels,stats.mode(data["activity"][start:end])[0][0])    return segments, labels

segments, labels = segment_signal(dataset)labels = np.asarray(pd.get_dummies(labels), dtype = np.int8)reshaped_segments = segments.reshape(len(segments), 1,90, 3)

现在的数据已经是我们所期待的数据形式了，为了后面做交叉验证，需要将数据集分割为训练集和测试集。

train_test_split = np.random.rand(len(reshaped_segments)) < 0.70train_x = reshaped_segments[train_test_split]train_y = labels[train_test_split]test_x = reshaped_segments[~train_test_split]test_y = labels[~train_test_split]

卷积神经网络模型

CNN模型的结构如下图所示：

下面直接上代码：

input_height = 1input_width = 90num_labels = 6num_channels = 3batch_size = 10kernel_size = 60depth = 60num_hidden = 1000learning_rate = 0.0001training_epochs = 5total_batchs = reshaped_segments.shape[0] // batch_sizedef weight_variable(shape):    initial = tf.truncated_normal(shape, stddev = 0.1)    return tf.Variable(initial)def bias_variable(shape):    initial = tf.constant(0.0, shape = shape)    return tf.Variable(initial)def depthwise_conv2d(x, W):    return tf.nn.depthwise_conv2d(x,W, [1, 1, 1, 1], padding='VALID')def apply_depthwise_conv(x,kernel_size,num_channels,depth):    weights = weight_variable([1, kernel_size, num_channels, depth])    biases = bias_variable([depth * num_channels])    return tf.nn.relu(tf.add(depthwise_conv2d(x, weights),biases))    def apply_max_pool(x,kernel_size,stride_size):    return tf.nn.max_pool(x, ksize=[1, 1, kernel_size, 1],                           strides=[1, 1, stride_size, 1], padding='VALID')

X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels])Y = tf.placeholder(tf.float32, shape=[None,num_labels])c = apply_depthwise_conv(X,kernel_size,num_channels,depth)p = apply_max_pool(c,20,2)c = apply_depthwise_conv(p,6,depth*num_channels,depth//10)shape = c.get_shape().as_list()c_flat = tf.reshape(c, [-1, shape[1] * shape[2] * shape[3]])f_weights_l1 = weight_variable([shape[1] * shape[2] * depth * num_channels * (depth//10), num_hidden])f_biases_l1 = bias_variable([num_hidden])f = tf.nn.tanh(tf.add(tf.matmul(c_flat, f_weights_l1),f_biases_l1))out_weights = weight_variable([num_hidden, num_labels])out_biases = bias_variable([num_labels])y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases)

loss = -tf.reduce_sum(Y * tf.log(y_))optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

cost_history = np.empty(shape=[1],dtype=float)with tf.Session() as session:    tf.initialize_all_variables().run()    for epoch in range(training_epochs):        for b in range(total_batchs):                offset = (b * batch_size) % (train_y.shape[0] - batch_size)            batch_x = train_x[offset:(offset + batch_size), :, :, :]            batch_y = train_y[offset:(offset + batch_size), :]            _, c = session.run([optimizer, loss],feed_dict={X: batch_x, Y : batch_y})            cost_history = np.append(cost_history,c)        print "Epoch: ",epoch," Training Loss: ",c," Training Accuracy: ",              session.run(accuracy, feed_dict={X: train_x, Y: train_y})        print "Testing Accuracy:", session.run(accuracy, feed_dict={X: test_x, Y: test_y})