机器学习笔记8：基于TensorFlow的数据预测

　　本文是在一篇博客预测天朝铁路的客运量一文中学习，代码部分引用该文，对其进行部分修改。

　　时间序列数据是指在不同时间点上收集到的数据，这类数据反映了某一事物、现象等随时间的变化状态或程度。

铁路客运量历史数据

　　铁路客运量.csv（2005-2016月度数据）

使用matplotlib画出数据走势

import matplotlib.pyplot as plt  import pandas as pd  import requests  import io  import numpy as np  url = 'http://blog.topspeedsnail.com/wp-content/uploads/2016/12/铁路客运量.csv'  ass_data = requests.get(url).content  df = pd.read_csv(io.StringIO(ass_data.decode('utf-8')))  # python2使用StringIO.StringIO  data = np.array(df['铁路客运量_当期值(万人)'])  # normalize  normalized_data = (data - np.mean(data)) / np.std(data)  plt.figure()  plt.plot(data)  plt.show() 

利用TensorFlow进行预测

# coding=utf-8'''Author:Chen haoDescription: counterDate: August 22 , 2017'''import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltimport pandas as pdimport requestsimport io# 加载数据url = 'http://blog.topspeedsnail.com/wp-content/uploads/2016/12/铁路客运量.csv'ass_data = requests.get(url).contentdf = pd.read_csv(io.StringIO(ass_data.decode('utf-8')))  # python2使用StringIO.StringIOdata = np.array(df['铁路客运量_当期值(万人)'])# normalizenormalized_data = (data - np.mean(data)) / np.std(data)seq_size = 3train_x, train_y = [], []for i in range(len(normalized_data) - seq_size - 1):    train_x.append(np.expand_dims(normalized_data[i: i + seq_size], axis=1).tolist())    train_y.append(normalized_data[i + 1: i + seq_size + 1].tolist())input_dim = 1X = tf.placeholder(tf.float32, [None, seq_size, input_dim])Y = tf.placeholder(tf.float32, [None, seq_size])# regressiondef ass_rnn(hidden_layer_size=6):    W = tf.Variable(tf.random_normal([hidden_layer_size, 1]), name='W')    b = tf.Variable(tf.random_normal([1]), name='b')    cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_layer_size)    outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)    W_repeated = tf.tile(tf.expand_dims(W, 0), [tf.shape(X)[0], 1, 1])    out = tf.matmul(outputs, W_repeated) + b    out = tf.squeeze(out)    return outdef train_rnn():    out = ass_rnn()    loss = tf.reduce_mean(tf.square(out - Y))    train_op = tf.train.AdamOptimizer(learning_rate=0.003).minimize(loss)    saver = tf.train.Saver(tf.global_variables())    with tf.Session() as sess:        # tf.get_variable_scope().reuse_variables()        sess.run(tf.global_variables_initializer())        for step in range(9000):            _, loss_ = sess.run([train_op, loss], feed_dict={X: train_x, Y: train_y})            if step % 10 == 0:                # 用测试数据评估loss                print(step, loss_)        print("保存模型: ", saver.save(sess, 'ass.model'))def prediction():    out = ass_rnn()    saver = tf.train.Saver(tf.global_variables())    with tf.Session() as sess:        # tf.get_variable_scope().reuse_variables()        saver.restore(sess, './ass.model')        prev_seq = train_x[-1]        predict = []        for i in range(12):            next_seq = sess.run(out, feed_dict={X: [prev_seq]})            predict.append(next_seq[-1])            prev_seq = np.vstack((prev_seq[1:], next_seq[-1]))        plt.figure()        plt.plot(list(range(len(normalized_data))), normalized_data, color='b')        plt.plot(list(range(len(normalized_data), len(normalized_data) + len(predict))), predict, color='r')        plt.show()#train_rnn()prediction()

　　需要注意的是，先要运行train_rnn()函数，然后将训练产生的模型保存到当前的文件夹（屏蔽prediction函数），然后在运行prediction()函数（屏蔽train_rnn()函数）即可获得预测的结果。

　　运行的结果如下图所示：

　　运行构建的模型在很多情况下并不理想。