现有一个时间序列international-airline-passengers.csv,怎么使用RNN来预测呢?本文就对其进行详细的阐述。

本时间序列一共144行,数据量很小,但是用其来学习RNN的使用已经足够了。

使用RNN预测时间序列的整体思路是:

*
取时间序列的第二列(international-airline-passengers.csv的第一列数据为时间,未在本次程序中使用),由于第二列值差异较大,所以本文采用“(原值-平均值)/标准差”的方法对数据进行标准化处理,前80%的数据作为训练集train_x,后20%的数据作为测试集test_x。
* 设置RNN的参数:input_dim=1、seq_size=5、hidden_dim=100
*
训练集和测试集的标签分别记为train_y、test_y,train_x和train_y的值错开了一个单位,test_x和test_y的值也是错开一个单位,便于计算cost值
* RNN模型的输出out为5个值的一维数组
实现程序

读CSV文件、数据标准化data_loader.py:
import csv import numpy as np import matplotlib.pyplot as plt def
load_series(filename, series_idx=1): try: with open(filename) as csvfile:
csvreader = csv.reader(csvfile) data = [float(row[series_idx]) for row in
csvreader if len(row) > 0] #
数据标准化,可以使数据的浮动差异不那么大,使预测结果更准确。np.mean求平均值,np.std求标准差。 normalized_data = (data -
np.mean(data)) / np.std(data) return normalized_data except IOError: return
None # 把数据切分成80%训练集、20%测试集 def split_data(data, percent_train=0.80): num_rows =
len(data) train_data, test_data = [], [] for idx, row in enumerate(data): if
idx < num_rows * percent_train: train_data.append(row) else:
test_data.append(row) return train_data, test_data if __name__ == '__main__':
timeseries = load_series('international-airline-passengers.csv')
print(np.shape(timeseries)) plt.figure() plt.plot(timeseries) plt.show()
144条数据的显示结果:

模型训练rnn_ts.py:
import numpy as np import tensorflow as tf from tensorflow.contrib import rnn
import data_loader import matplotlib.pyplot as plt class SeriesPredictor: def
__init__(self, input_dim, seq_size, hidden_dim): # Hyperparameters
self.input_dim = input_dim self.seq_size = seq_size self.hidden_dim =
hidden_dim # Weight variables and input placeholders self.W_out =
tf.Variable(tf.random_normal([hidden_dim, 1]), name='W_out') self.b_out =
tf.Variable(tf.random_normal([1]), name='b_out') self.x =
tf.placeholder(tf.float32, [None, seq_size, input_dim]) # 5个小片段,有5个预测值 self.y =
tf.placeholder(tf.float32, [None, seq_size]) # Cost optimizer self.cost =
tf.reduce_mean(tf.square(self.model() - self.y)) self.train_op =
tf.train.AdamOptimizer(learning_rate=0.01).minimize(self.cost) # Auxiliary ops
self.saver = tf.train.Saver() def model(self): """ :param x: inputs of size [T,
batch_size, input_size] :param W: matrix of fully-connected output layer
weights :param b: vector of fully-connected output layer biases """ cell =
rnn.BasicLSTMCell(self.hidden_dim) outputs, states = tf.nn.dynamic_rnn(cell,
self.x, dtype=tf.float32) num_examples = tf.shape(self.x)[0] W_repeated =
tf.tile(tf.expand_dims(self.W_out, 0), [num_examples, 1, 1]) out =
tf.matmul(outputs, W_repeated) + self.b_out out = tf.squeeze(out) # 返回长度为5的一维数组
return out def train(self, train_x, train_y, test_x, test_y): with tf.Session()
as sess: tf.get_variable_scope().reuse_variables()
sess.run(tf.global_variables_initializer()) max_patience = 3 patience =
max_patience # 最小err指定为无限 min_test_err = float('inf') step = 0 #
test_err出现3次浮动停下 while patience > 0: _, train_err = sess.run([self.train_op,
self.cost], feed_dict={self.x: train_x, self.y: train_y}) if step % 100 == 0:
test_err = sess.run(self.cost, feed_dict={self.x: test_x, self.y: test_y})
print('step:{}\t\ttrain err:{}\t\ttest err:{}'.format(step, train_err,
test_err)) if test_err < min_test_err: min_test_err = test_err patience =
max_patience else: patience -= 1 step += 1 save_path = self.saver.save(sess,
'./model/') print('Model saved to {}'.format(save_path)) def test(self, sess,
test_x): tf.get_variable_scope().reuse_variables() self.saver.restore(sess,
'./model/') output = sess.run(self.model(), feed_dict={self.x: test_x}) return
output def plot_results(train_x, predictions, actual, filename): plt.figure()
num_train = len(train_x) # 训练集。plt.plot(x,y,format_string,**kwargs) x轴数据,y轴数据
plt.plot(list(range(num_train)), train_x, color='b', label='training data') #
预测集 plt.plot(list(range(num_train, num_train + len(predictions))), predictions,
color='r', label='predicted') # 真实值 plt.plot(list(range(num_train, num_train +
len(actual))), actual, color='g', label='test_data') # 加图例 plt.legend() if
filename is not None: plt.savefig(filename) else: plt.show() if __name__ ==
'__main__': # 序列长度,基于一个小片段去预测下一个值 seq_size = 5 predictor =
SeriesPredictor(input_dim=1, seq_size=seq_size, hidden_dim=100) data =
data_loader.load_series('international-airline-passengers.csv') # 数据切分成训练集,测试集
train_data, actual_vals = data_loader.split_data(data) train_x, train_y = [],
[] for i in range(len(train_data) - seq_size - 1): # 训练数据和标签错开了1个单位
train_x.append(np.expand_dims(train_data[i:i + seq_size], axis=1).tolist())
train_y.append(train_data[i + 1: i + seq_size + 1]) test_x, test_y = [], [] for
i in range(len(actual_vals) - seq_size - 1):
test_x.append(np.expand_dims(actual_vals[i:i + seq_size], axis=1).tolist())
test_y.append(actual_vals[i + 1: i + seq_size + 1]) predictor.train(train_x,
train_y, test_x, test_y) with tf.Session() as sess: # [:, 0]为什么是0呢?不应该是-1么? #
1、用训练好的模型'./model/'预测,所以第一个值也是基于前面序列预测到的。 # 2、画图时使预测值和真实值具有对照性。 predicted_vals
= predictor.test(sess, test_x)[:, 0] print('predicted_vals',
np.shape(predicted_vals)) plot_results(train_data, predicted_vals, actual_vals,
'predictions.png') # 拿出训练集最后5个数据 prev_seq = train_x[-1] predicted_vals = [] for
i in range(20): next_seq = predictor.test(sess, [prev_seq]) #
把预测的结果当做当前的结果值,继续预测。 predicted_vals.append(next_seq[-1]) #
np.vstack:按垂直方向(行顺序)堆叠数组构成一个新的数组;np.hstack:按水平方向(列顺序)堆叠数组构成一个新的数组 #
prev_seq[1:]取第二至第五个数据,一共4个数据 prev_seq = np.vstack((prev_seq[1:], next_seq[-1]))
plot_results(train_data, predicted_vals, actual_vals, 'hallucinations.png')
运行结果:
step:0 train err:1.8898743391036987 test err:2.7799108028411865 step:100 train
err:0.04205527901649475 test err:0.2253977209329605 step:200 train
err:0.039602071046829224 test err:0.28264862298965454 step:300 train
err:0.03779347985982895 test err:0.2434949427843094 step:400 train
err:0.03636837378144264 test err:0.2533899247646332 Model saved to ./model/
predicted_vals (22,)
predictions.png:

hallucinations.png:

停止迭代方法大概有三种:

* 精度大于某个阈值停下来。例:acc>=0.98停下来;
* 测试err出现几次浮动停下来;
* 迭代次数达到XXX次停下来。
本文采用的是第二种方法。

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