tensorflow —— 基本的LSTM循环网络的实现

利用基本的LSTM循环网络实现对 “international-airline-passengers.csv“ 的预测 ，该数据是 1949到1960共12年,每年12个月的数据,一共 144 个数据。在 SeriesPredictor 类中定义了模型构建、模型训练、模型测试的方法来实现预测，plot_result 方法实现了预测结果的折线图绘制。

import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
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])
        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)
        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 # 停止训练的参数：连续3次误差增大则停止训练
            patience = max_patience
            min_test_err = float('inf')
            step = 0
            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, './rnn_ts/model')
            print('Model saved to {}'.format(save_path))

    def test(self, sess, test_x):
        tf.get_variable_scope().reuse_variables()
        self.saver.restore(sess, tf.train.latest_checkpoint('./rnn_ts/'))
        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)
    plt.plot(list(range(num_train)), train_x, color='b', label='training data')
    plt.plot(list(range(num_train, num_train + len(predictions))), predictions, color='r', label='predicted')
    plt.plot(list(range(num_train, num_train + len(predictions))), actual[:len(predictions)], color='g', label='test data')
    plt.legend()
    if filename is not None:
        plt.savefig(filename)
    else:
        plt.show()

在数据导入之前，需要对数据进行归一化，本次将数据划分为80% 的训练集和20%的测试集。由 data_loader 模块实现。

import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
import data_loader
import matplotlib.pyplot as plt

seq_size = 5 # 设置滑动窗口的大小
    predictor = SeriesPredictor(input_dim=1, seq_size=seq_size, hidden_dim=100)
    data, data_mean, data_std = data_loader.load_series('international-airline-passengers.csv')
    train_data, actual_vals = data_loader.split_data(data)

    train_x, train_y = [], []
    for i in range(len(train_data) - seq_size - 1):
        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:
        # method one
        predicted_vals = predictor.test(sess, test_x)[:,0]
        print('predicted_vals', np.shape(predicted_vals))
        plot_results(train_data, predicted_vals, actual_vals, 'predictions1.png')
        inversed_predata = np.array(predicted_vals) * data_std + data_mean
        inversed_trudata = np.array(actual_vals)[:len(predicted_vals)] * data_std + data_mean
        print(inversed_predata)
        print(inversed_trudata)

        # method two
        prev_seq = train_x[-1]
        predicted_vals = []
        predicted_num = 12
        for i in range(predicted_num):
            next_seq = predictor.test(sess, [prev_seq])
            predicted_vals.append(next_seq[-1])
            prev_seq = np.vstack((prev_seq[1:], next_seq[-1]))
        plot_results(train_data, predicted_vals, actual_vals, 'predictions2.png')
        # 反归一化
        inversed_predata = np.array(predicted_vals) * data_std + data_mean
        inversed_trudata = np.array(actual_vals)[-predicted_num:] * data_std + data_mean
        print(inversed_predata)
        print(inversed_trudata)

方法一是用原始数据当作测试数据进行测试，共测试了22个样本

预测数据：[ 395.60186768  354.80560303  309.34631348  334.51806641  355.72271729   339.14706421  397.39108276  388.42373657  409.85327148  455.11221313   517.96313477  526.71069336  447.40203857  398.28482056  357.55560303
  396.49676514  407.19207764  383.92028809  408.96676636  445.68139648   455.11221313  507.50881958]
真实数据：[ 404.  359.  310.  337.  360.  342.  406.  396.  420.  472.  548.  559.  463.  407.  362.  405.  417.  391.  419.  461.  472. 535.]

方法二是用预测出的数据作为测试数据进行预测，该方法的精度不如方法一，但扔可看出数据走势，适用于没有真实数据的情况，本次预测了未来12天的数据。

预测数据 [ 469.43139648  467.07409668  410.91229248  362.13061523  315.65072632   338.21929932  329.56622314  324.51544189  356.00354004  380.85159302   410.90618896  441.54260254]
真实数据 [ 417.  391.  419.  461.  472.  535.  622.  606.  508.  461.  390.  432.]